{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":9229,"offset":9219,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"### **Code Assessment**\n\nOverall, your code is well-structured for fine-tuning the `google/flan-t5-base` model on the `danielv835/personal_finance_v0.2` dataset. However, there are a few areas where improvements can be made, and some discrepancies in the output that warrant attention.\n\n#### **1. Library Installation and Imports**\n- **Libraries Installed:**\n  - `transformers`: For model and tokenizer.\n  - `datasets`: For dataset handling.\n  - `evaluate` & `bert_score`: For evaluation metrics.\n\n- **Imports:**\n  - Appropriately includes necessary modules from `transformers` and `datasets`.\n\n#### **2. Dataset Preparation**\n- **Loading Dataset:**\n  ```python\n  dataset1 = load_dataset(\"danielv835/personal_finance_v0.2\")\n  ```\n  - Ensure that this dataset is correctly formatted for your task, with `context` and `chosen` fields.\n\n- **Removing Columns:**\n  ```python\n  dataset1 = dataset1.remove_columns(['rejected'])\n  ```\n  - Correctly removes unnecessary columns to streamline the dataset.\n\n#### **3. Tokenization**\n- **Tokenizer Loading:**\n  ```python\n  tokenizer = T5Tokenizer.from_pretrained(\"google/flan-t5-base\")\n  ```\n  - Correct tokenizer choice aligning with the model.\n\n- **Tokenization Function:**\n  ```python\n  def tokenize_function(examples):\n      model_inputs = tokenizer(\n          examples[\"context\"],\n          max_length=256,\n          truncation=True,\n          padding=False\n      )\n      with tokenizer.as_target_tokenizer():\n          labels = tokenizer(\n              examples[\"chosen\"],\n              max_length=256,\n              truncation=True,\n              padding=False\n      )\n      model_inputs[\"labels\"] = labels[\"input_ids\"]\n      return model_inputs\n  ```\n  - **Suggestions:**\n    - **Padding Strategy:** While `padding=False` is acceptable here, leveraging the `DataCollatorForSeq2Seq` with `padding=\"longest\"` is suitable. Alternatively, you can set `padding=\"max_length\"` with a specified `max_length` for consistency.\n    - **Deprecated Method Warning:** The usage of `tokenizer.as_target_tokenizer()` is deprecated in recent versions of Hugging Face Transformers. Instead, consider using `tokenizer(..., text_target=...)` for clarity and future compatibility:\n      ```python\n      def tokenize_function(examples):\n          inputs = tokenizer(examples[\"context\"], max_length=256, truncation=True)\n          targets = tokenizer(examples[\"chosen\"], max_length=256, truncation=True, text_target=True)\n          inputs[\"labels\"] = targets[\"input_ids\"]\n          return inputs\n      ```\n\n#### **4. Model Loading**\n```python\ntw_model = T5ForConditionalGeneration.from_pretrained(\"google/flan-t5-base\")\n```\n- Correct choice aligning with the tokenizer.\n\n#### **5. Data Collator**\n```python\ndata_collator = DataCollatorForSeq2Seq(\n    tokenizer=tokenizer,\n    model=tw_model,\n    label_pad_token_id=-100,\n    padding=\"longest\",\n    return_tensors=\"pt\"\n)\n```\n- **Points to Note:**\n  - `label_pad_token_id=-100` is appropriate as it ensures the loss function ignores padding tokens.\n  - `padding=\"longest\"` allows dynamic padding based on the longest sequence in a batch, which is efficient.\n\n#### **6. Training Arguments**\n```python\ntraining_args = Seq2SeqTrainingArguments(\n    output_dir=\"./trainer_wheel_model\",\n    eval_strategy=\"epoch\",\n    learning_rate=3e-4,\n    per_device_train_batch_size=8,\n    per_device_eval_batch_size=4,\n    gradient_accumulation_steps=2,\n    save_steps=500,\n    save_total_limit=2,\n    logging_dir='./logs',\n    logging_steps=200,\n    num_train_epochs=6,\n    fp16=False,\n    predict_with_generate=True,\n    generation_max_length=256,\n    generation_num_beams=5,\n)\n```\n- **Observations:**\n  - **Learning Rate:** `3e-4` is relatively high for fine-tuning T5 models. Typically, values between `1e-5` to `5e-5` are used. Consider experimenting with lower rates to improve convergence.\n  - **Epochs vs. Output:** Although `num_train_epochs` is set to `6`, your output indicates only one complete epoch. Ensure that the training process isn't being interrupted prematurely.\n  - **Mixed Precision (`fp16`):** Enabling mixed precision (`fp16=True`) can accelerate training if supported by your hardware (e.g., NVIDIA GPUs with Tensor Cores).\n\n#### **7. Evaluation Metrics**\n- **Custom F1 Score:**\n  ```python\n  def f1_score_custom(preds, labels):\n      ...\n  ```\n  - **Concerns:**\n    - **Simplicity:** This implementation calculates token-level F1 based on unique token overlap, which might not accurately reflect the quality of generated sequences. Consider using sentence-level F1 or leveraging more sophisticated metrics.\n  \n- **Hugging Face Metrics:**\n  ```python\n  metric_bert = evaluate.load(\"bertscore\")\n  metric_meteor = evaluate.load(\"meteor\")\n  ```\n  - **Usage:** Appropriate for assessing the quality of generated text.\n\n- **Compute Metrics Function:**\n  ```python\n  def compute_metrics(eval_pred):\n      ...\n  ```\n  - **Suggestions:**\n    - **Handling -100 Labels:** The replacement of `-100` with `tokenizer.pad_token_id` is correct for decoding.\n    - **Label Formatting:** Ensure labels are correctly formatted as a list of references for metrics like METEOR and BERTScore.\n    - **BERTScore Model Selection:** By default, `bertscore` uses `'bert-base-uncased'`. If your task benefits from a different BERT variant, specify it accordingly.\n\n#### **8. Trainer Initialization and Training**\n```python\ntrainer = Seq2SeqTrainer(\n    ...\n)\n\ntorch.cuda.empty_cache()\n\nprint(\"Starting training...\")\ntrainer.train()\nprint(\"Training completed!\")\n```\n- **Key Points:**\n  - **Cache Management:** Clearing CUDA cache can help prevent memory issues, especially if restarting training after interruptions.\n  - **Training Monitoring:** Ensure that logs are being saved correctly in `./logs` and that the `logging_steps` align with your monitoring frequency.\n\n### **Output Assessment**\n\nLet's break down and analyze the provided output:\n\n```\nStarting training...\n [ 7070/14138 1:43:48 < 1:43:48, 1.13 it/s, Epoch 1.00/2]\nEpoch\tTraining Loss\tValidation Loss\tF1\tMeteor\tBert Score F1\n0\t3.132700\t2.974948\t9.036977\t14.245515\t81.960124\n1\t3.084400\t2.952344\t9.511389\t14.501229\t82.234502\n...\nSome weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\nTraining completed!\n```\n\n#### **1. Epoch and Step Information**\n- **Training Progress:**\n  ```\n  [ 7070/14138 1:43:48 < 1:43:48, 1.13 it/s, Epoch 1.00/2]\n  ```\n  - **Discrepancies:**\n    - **Epochs:** Your `training_args` specify `num_train_epochs=6`, but the progress shows `Epoch 1.00/2`. This suggests that either:\n      - The code being run isn't the exact version you've shared.\n      - There's a discrepancy between the intended and actual epoch settings.\n      - An early termination of training occurred after fewer epochs.\n  \n- **Completion Message:**\n  ```\n  Training completed!\n  ```\n  - Indicates that the training process has concluded, potentially before completing all specified epochs.\n\n#### **2. Loss and Metrics**\n- **Training and Validation Loss:**\n  ```\n  Epoch\tTraining Loss\tValidation Loss\n  0\t3.132700\t2.974948\n  1\t3.084400\t2.952344\n  ```\n  - **Observations:**\n    - **Loss Values:** The training and validation losses are relatively high but may be acceptable depending on the task's complexity and data nature.\n    - **Trend:** A slight decrease in both training and validation loss across epochs indicates some level of learning. However, monitoring these values over more epochs would provide a clearer picture.\n\n- **Evaluation Metrics:**\n  ```\n  F1\tMeteor\tBert Score F1\n  0\t9.036977\t14.245515\t81.960124\n  1\t9.511389\t14.501229\t82.234502\n  ```\n  - **F1 Score:**\n    - **Low Values:** Scores around 9-10 suggest that the model's predictions have minimal token overlap with the references. This could be due to:\n      - The custom F1 implementation being too simplistic.\n      - Poor model performance possibly from insufficient training or high learning rate.\n  \n  - **METEOR Score:**\n    - **Moderate Values:** Scores in the range of 14-15 are modest. METEOR typically ranges between 0-100, but implementations can vary based on scoring normalization.\n  \n  - **BERTScore F1:**\n    - **High Values:** Scores around 81-82 indicate good semantic similarity between predictions and references, suggesting that while token overlap (F1) is low, the overall meaning is preserved.\n  \n  - **Interpretation:** The discrepancy between F1 and BERTScore suggests that the model is generating semantically correct responses that do not share many exact tokens with the references. This highlights the limitations of using token-based F1 for evaluating generative models. Relying more on semantic metrics like BERTScore might provide a better assessment of performance.\n\n#### **3. Warning Messages**\n```\nSome weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n```\n- **Discrepancy Explanation:**\n  - **Issue:** Your code specifies loading `T5ForConditionalGeneration` from `\"google/flan-t5-base\"`, yet the warning pertains to `RobertaModel` and `roberta-large`.\n  \n  - **Possible Causes:**\n    - **Model Mismatch:** Ensure that the code you're running matches the code shared. If `roberta-large` is inadvertently loaded elsewhere (perhaps in a different part of the script or notebook), it could trigger this warning.\n    - **Custom Dataset Dependencies:** If the `danielv835/personal_finance_v0.2` dataset internally references or utilizes `RobertaModel`, this might cause such warnings. However, this is uncommon.\n  \n  - **Action Steps:**\n    - **Verify Model Loading:** Double-check that only `T5ForConditionalGeneration` is being loaded and that no other models (like `RobertaModel`) are inadvertently initialized elsewhere in your codebase.\n    - **Check Dependencies:** Ensure that other parts of your project or environment aren't loading `RobertaModel`. If using a notebook, confirm that previous cells aren't interfering.\n\n### **Recommendations for Improvement**\n\n1. **Review Model Loading:**\n   - Ensure that the correct model (`T5ForConditionalGeneration`) is being loaded throughout the entire script. The warning about `RobertaModel` suggests a potential mix-up.\n\n2. **Adjust Training Parameters:**\n   - **Learning Rate:** Consider reducing the learning rate to between `1e-5` and `5e-5` to allow for more stable and potentially more effective fine-tuning.\n   - **Number of Epochs:** Confirm that the training runs for the intended number of epochs (`6`), monitoring early termination causes if any.\n\n3. **Enhance Evaluation Metrics:**\n   - **Refine F1 Implementation:** The current token-based F1 might not capture the model's performance accurately. Consider using sequence-level F1 or integrating more sophisticated metrics.\n   - **Utilize Additional Metrics:** Incorporate metrics like ROUGE or BLEU, which are more standard for evaluating generative models.\n\n4. **Monitor Training Progress:**\n   - **Logging:** Ensure that logs are sufficiently detailed to monitor not just loss and metrics but also potential issues like overfitting.\n   - **Visualization:** Use tools like TensorBoard to visualize training and validation metrics over time.\n\n5. **Handle Padding Appropriately:**\n   - While the current setup with `padding=\"longest\"` in the data collator is generally effective, ensuring consistent padding during both tokenization and collation can prevent potential mismatches.\n\n6. **Address Hardware Utilization:**\n   - If available, enable mixed precision (`fp16=True`) to leverage faster computations and potentially accommodate larger batch sizes.\n\n7. **Validate Dataset Integrity:**\n   - Ensure that the `context` and `chosen` fields are correctly populated and relevant for the task. Any inconsistencies here can adversely affect model performance.\n\n### **Conclusion**\n\nYour setup for fine-tuning the FLAN-T5 model is fundamentally sound, with appropriate dataset handling, tokenization, model loading, and evaluation strategies. However, discrepancies in the output indicate potential issues with model loading and evaluation metric implementations. By addressing the highlighted concerns and refining certain aspects of the training and evaluation pipeline, you can enhance the model's performance and obtain more meaningful evaluation metrics.\n\nIf you continue to encounter issues or require further assistance, feel free to provide additional details or specific questions!","behavior":"degenerate_repetition","conversation_hash":"119ea795d3c3d84b4c7999941f304158","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":20017,"n_turns_in_conversation":1,"n_words":2280,"source_row_offset":9219,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:23 GMT","turn_id":"1fdc5e3a7ba5ed00918e6556","turn_index":0,"user_text":"Assess the code and output below:\n```\n# Install Required Libraries\n!pip install --upgrade transformers\n!pip install datasets\n!pip install evaluate\n!pip install bert_score\n\n# Import Libraries\nimport evaluate\nimport torch\nimport numpy as np\nfrom transformers import (\n    T5Tokenizer,\n    T5ForConditionalGeneration,\n    Seq2SeqTrainer,\n    Seq2SeqTrainingArguments,\n    DataCollatorForSeq2Seq\n)\nfrom datasets import load_dataset, concatenate_datasets\n\n# Load and Prepare the Dataset\ndataset1 = load_dataset(\"danielv835/personal_finance_v0.2\")\n\n# Remove the 'rejected' column from dataset1\ndataset1 = dataset1.remove_columns(['rejected'])\n\n# Load the tokenizer\ntokenizer = T5Tokenizer.from_pretrained(\"google/flan-t5-base\")\n\n# Tokenization function without padding\ndef tokenize_function(examples):\n    model_inputs = tokenizer(\n        examples[\"context\"],\n        max_length=256,\n        truncation=True,\n        padding=False\n    )\n    with tokenizer.as_target_tokenizer():\n        labels = tokenizer(\n            examples[\"chosen\"],\n            max_length=256,\n            truncation=True,\n            padding=False\n    )\n    model_inputs[\"labels\"] = labels[\"input_ids\"]\n    return model_inputs\n\n# Tokenize both datasets\ntokenized_datasets1 = dataset1.map(tokenize_function, batched=True)\n\n# Load the pre-trained FLAN-T5 model for conditional generation\ntw_model = T5ForConditionalGeneration.from_pretrained(\"google/flan-t5-base\")\n\n# Define the data collator\ndata_collator = DataCollatorForSeq2Seq(\n    tokenizer=tokenizer,\n    model=tw_model,\n    label_pad_token_id=-100,  # Explicitly set label padding\n    padding=\"longest\",\n    return_tensors=\"pt\"\n)\n\n# Ensure the pad_token_id is set\ntw_model.config.pad_token_id = tokenizer.pad_token_id\n\n# Define training arguments for the Trainer API\ntraining_args = Seq2SeqTrainingArguments(\n    output_dir=\"./trainer_wheel_model\",    # Output directory for saving model checkpoints\n    eval_strategy=\"epoch\",                 # Evaluate at the end of every epoch\n    learning_rate=3e-4,                    # Set model learning rate\n    per_device_train_batch_size=8,         # Adjust batch size for your GPU/CPU\n    per_device_eval_batch_size=4,\n    gradient_accumulation_steps=2,         # Use gradient accumulation\n    save_steps=500,                        # Save model every 500 steps\n    save_total_limit=2,                    # Limit number of saved checkpoints\n    logging_dir='./logs',                  # Directory for storing logs\n    logging_steps=200,                     # Log every 200 steps\n    num_train_epochs=6,                    # Number of training epochs\n    fp16=False,                             # Disable mixed precision\n    predict_with_generate=True,            # Generate predictions during evaluation\n    generation_max_length=256,             # Maximum length of generated sequences\n    generation_num_beams=5,                # Number of beams for beam search\n)\n\n# Define F1 Score (Custom Implementation)\ndef f1_score_custom(preds, labels):\n    f1_total = 0\n    for p, l in zip(preds, labels):\n        p_tokens = p.split()\n        l_tokens = l.split()\n        common = set(p_tokens) & set(l_tokens)\n\n        # If no common tokens, F1 score is 0\n        if len(common) == 0:\n            f1_total += 0\n        else:\n            precision = len(common) / len(p_tokens) if len(p_tokens) > 0 else 0\n            recall = len(common) / len(l_tokens) if len(l_tokens) > 0 else 0\n            if precision + recall > 0:\n                f1_total += 2 * precision * recall / (precision + recall)\n\n    return f1_total / len(preds) if len(preds) > 0 else 0\n\n# Load required metrics\nmetric_bert = evaluate.load(\"bertscore\")\nmetric_meteor = evaluate.load(\"meteor\")\n\n# Define the compute_metrics function\ndef compute_metrics(eval_pred):\n    preds, labels = eval_pred\n\n    # If logits are returned as a tuple, extract the first element\n    if isinstance(preds, tuple):\n        preds = preds[0]\n\n    # Replace -100 in preds and the labels as we can't decode them\n    preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n\n    # Decode the predicted tokens into text\n    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n\n    # Post-process predictions and labels\n    decoded_preds = [pred.strip() for pred in decoded_preds]\n    decoded_labels = [[label.strip()] for label in decoded_labels]  # METEOR and BERTScore expect a list of references\n\n    # Flatten labels for F1 and METEOR calculation\n    flattened_labels = [label[0] for label in decoded_labels]  # Assuming each label list contains one element\n\n    # Compute F1 Score\n    f1_score = f1_score_custom(decoded_preds, flattened_labels)\n\n    # Compute METEOR\n    meteor_result = metric_meteor.compute(predictions=decoded_preds, references=flattened_labels)\n    meteor_score = meteor_result[\"meteor\"]\n\n    # Compute BERTScore (using default 'bert-base-uncased' model)\n    bert_result = metric_bert.compute(predictions=decoded_preds, references=flattened_labels, lang=\"en\")\n    bert_score_f1 = np.mean(bert_result[\"f1\"])\n\n    # Combine the metrics\n    result = {\n        \"f1\": f1_score * 100,\n        \"meteor\": meteor_score * 100,      # Express METEOR as percentage\n        \"bert_score_f1\": bert_score_f1 * 100  # Express BERTScore F1 as percentage\n    }\n\n    return result\n\n# Initialize the Trainer instance\ntrainer = Seq2SeqTrainer(\n    model=tw_model,                        # Model to be trained\n    args=training_args,                    # Training arguments\n    train_dataset=tokenized_datasets1[\"train\"],           # Training dataset\n    eval_dataset=tokenized_datasets1[\"test\"],             # Evaluation dataset\n    data_collator=data_collator,           # Data collator for batching\n    tokenizer=tokenizer,                   # Tokenizer for encoding/decoding\n    compute_metrics=compute_metrics,       # Function to compute evaluation metrics\n)\n\n# Empty the cache before starting the training process\ntorch.cuda.empty_cache()\n\n# Start training the model\nprint(\"Starting training...\")\ntrainer.train()\nprint(\"Training completed!\")\n```\n```\nStarting training...\n [ 7070/14138 1:43:48 < 1:43:48, 1.13 it/s, Epoch 1.00/2]\nEpoch\tTraining Loss\tValidation Loss\n [ 39/1000 01:31 < 38:35, 0.41 it/s]\n [14138/14138 4:48:56, Epoch 1/2]\nEpoch\tTraining Loss\tValidation Loss\tF1\tMeteor\tBert Score F1\n0\t3.132700\t2.974948\t9.036977\t14.245515\t81.960124\n1\t3.084400\t2.952344\t9.511389\t14.501229\t82.234502\ntokenizer_config.json:\u2007100%\n\u200725.0/25.0\u2007[00:00<00:00,\u20072.16kB/s]\nconfig.json:\u2007100%\n\u2007482/482\u2007[00:00<00:00,\u200739.9kB/s]\nvocab.json:\u2007100%\n\u2007899k/899k\u2007[00:00<00:00,\u20072.05MB/s]\nmerges.txt:\u2007100%\n\u2007456k/456k\u2007[00:00<00:00,\u20071.90MB/s]\ntokenizer.json:\u2007100%\n\u20071.36M/1.36M\u2007[00:00<00:00,\u20076.22MB/s]\nmodel.safetensors:\u2007100%\n\u20071.42G/1.42G\u2007[00:02<00:00,\u2007503MB/s]\nSome weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\nTraining completed!\n```"},{"assistant_initiates":false,"asst_text":"\u0418\u0437 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u044b\u0445 \u043b\u043e\u0433\u043e\u0432 \u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 `bot_token` \u0438 `API_TOKEN` \u043f\u043e\u043b\u0443\u0447\u0430\u044e\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `None`. \u042d\u0442\u043e \u043f\u0440\u0438\u0432\u043e\u0434\u0438\u0442 \u043a \u043e\u0448\u0438\u0431\u043a\u0430\u043c \u043f\u0440\u0438 \u043f\u043e\u043f\u044b\u0442\u043a\u0435 \u043e\u0442\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0447\u0435\u0440\u0435\u0437 Telegram \u0431\u043e\u0442\u0430 \u0438 \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c \u0441 API. \u041e\u0441\u043d\u043e\u0432\u043d\u0430\u044f \u043f\u0440\u0438\u0447\u0438\u043d\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u0432\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0435 `os.getenv()` \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e.\n\n### \u041e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u041f\u0440\u043e\u0431\u043b\u0435\u043c\u044b:\n\n1. **\u041d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 `os.getenv()`:**\n   ```python\n   bot_token = os.getenv(\"7033736801:AAGh-gPCffmyU5MnxOgTj5JLUGISpmkBM0\")\n   API_TOKEN = os.getenv(\"$2y$16$HLh99.gCG85H1tLnpeRFYu3nZxkQ7MOwQpP8IKBCknH1khUfZsAI6\")\n   ```\n   \u0417\u0434\u0435\u0441\u044c \u0432\u044b \u043f\u0435\u0440\u0435\u0434\u0430\u0435\u0442\u0435 \u043d\u0435\u043f\u043e\u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0442\u043e\u043a\u0435\u043d\u0430 \u0432 `os.getenv()`. \u041e\u0434\u043d\u0430\u043a\u043e `os.getenv()` \u043e\u0436\u0438\u0434\u0430\u0435\u0442 \u0438\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f, \u0430 \u043d\u0435 \u0441\u0430\u043c\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435.\n\n2. **\u041e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f:**\n   \u0415\u0441\u043b\u0438 \u0432\u044b \u043f\u044b\u0442\u0430\u0435\u0442\u0435\u0441\u044c \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u044c \u0442\u043e\u043a\u0435\u043d\u044b \u0438\u0437 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u044d\u0442\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u044b \u0432 \u0441\u0438\u0441\u0442\u0435\u043c\u0435.\n\n### \u0420\u0435\u0448\u0435\u043d\u0438\u0435:\n\n#### 1. \u0418\u0441\u043f\u0440\u0430\u0432\u044c\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 `os.getenv()`:\n\n- **\u041e\u043f\u0446\u0438\u044f 1: \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0438\u043c\u0435\u043d\u0430 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f**\n\n  \u0415\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0442\u043e\u043a\u0435\u043d\u044b \u0432 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438, \u0438\u0437\u043c\u0435\u043d\u0438\u0442\u0435 \u043a\u043e\u0434 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n\n  ```python\n  bot_token = os.getenv(\"BOT_TOKEN\")  # \u0418\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f Telegram \u0431\u043e\u0442\u0430\n  chat_id = \"-1002057440931\"          # ID \u0432\u0430\u0448\u0435\u0439 \u0433\u0440\u0443\u043f\u043f\u044b\n  \n  API_TOKEN = os.getenv(\"API_TOKEN\")  # \u0418\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f API \u0442\u043e\u043a\u0435\u043d\u0430\n  ```\n\n  **\u0414\u0435\u0439\u0441\u0442\u0432\u0438\u044f:**\n  \n  - \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f `BOT_TOKEN` \u0438 `API_TOKEN` \u0432 \u0432\u0430\u0448\u0435\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u0435.\n  - \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u044d\u0442\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u0434\u043b\u044f \u0432\u0430\u0448\u0435\u0433\u043e \u0441\u043a\u0440\u0438\u043f\u0442\u0430. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0432 Windows \u043c\u043e\u0436\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u0438\u0445 \u0447\u0435\u0440\u0435\u0437 \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0435 \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438 \u0438\u043b\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c `.env` \u0444\u0430\u0439\u043b \u0441 \u043f\u0430\u043a\u0435\u0442\u0430\u043c\u0438 \u0432\u0440\u043e\u0434\u0435 `python-dotenv`.\n\n- **\u041e\u043f\u0446\u0438\u044f 2: \u0416\u0435\u0441\u0442\u043a\u043e \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u0443\u0439\u0442\u0435 \u0442\u043e\u043a\u0435\u043d\u044b (\u043d\u0435 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d)**\n\n  \u0415\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0431\u044b\u0441\u0442\u0440\u043e \u043f\u0440\u043e\u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0442\u043e\u043a\u0435\u043d\u044b \u043d\u0435\u043f\u043e\u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u0432 \u0441\u043a\u0440\u0438\u043f\u0442\u0435:\n\n  ```python\n  bot_token = \"7033736801:AAGh-gPCffmyU5MnxOgTj5JLUGISpmkBM0\"  # Telegram \u0431\u043e\u0442 \u0442\u043e\u043a\u0435\u043d\n  chat_id = \"-1002057440931\"                                       # ID \u0432\u0430\u0448\u0435\u0439 \u0433\u0440\u0443\u043f\u043f\u044b\n  \n  API_TOKEN = \"$2y$16$HLh99.gCG85H1tLnpeRFYu3nZxkQ7MOwQpP8IKBCknH1khUfZsAI6\"  # API \u0442\u043e\u043a\u0435\u043d\n  ```\n\n  **\u0412\u0430\u0436\u043d\u043e:** **\u041d\u0438\u043a\u043e\u0433\u0434\u0430 \u043d\u0435 \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u0439\u0442\u0435 \u0442\u043e\u043a\u0435\u043d\u044b \u0432 \u043e\u0442\u043a\u0440\u044b\u0442\u043e\u043c \u0434\u043e\u0441\u0442\u0443\u043f\u0435 \u0438\u043b\u0438 \u0432 \u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u044f\u0445.** \u0414\u043b\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f.\n\n#### 2. \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438 \u041f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u041e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f:\n\n\u0415\u0441\u043b\u0438 \u0432\u044b \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u0442\u0435 \u043e\u043f\u0446\u0438\u044e \u0441 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u043c\u0438 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f, \u0443\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u043e\u043d\u0438 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u044b \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e:\n\n- **Windows:**\n  1. \u041e\u0442\u043a\u0440\u043e\u0439\u0442\u0435 \"\u041f\u0430\u043d\u0435\u043b\u044c \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f\" \u2192 \"\u0421\u0438\u0441\u0442\u0435\u043c\u0430 \u0438 \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u044c\" \u2192 \"\u0421\u0438\u0441\u0442\u0435\u043c\u0430\".\n  2. \u041d\u0430\u0436\u043c\u0438\u0442\u0435 \"\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0441\u0438\u0441\u0442\u0435\u043c\u044b\".\n  3. \u0412 \u0440\u0430\u0437\u0434\u0435\u043b\u0435 \"\u041f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0441\u0440\u0435\u0434\u044b\" \u0434\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u043d\u043e\u0432\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 `BOT_TOKEN` \u0438 `API_TOKEN` \u0441 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438.\n\n- **Linux/Mac:**\n  \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0432\u0430\u0448 `.bashrc`, `.zshrc` \u0438\u043b\u0438 \u0434\u0440\u0443\u0433\u043e\u0439 \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0439 \u0444\u0430\u0439\u043b \u043e\u0431\u043e\u043b\u043e\u0447\u043a\u0438:\n  ```bash\n  export BOT_TOKEN=\"7033736801:AAGh-gPCffmyU5MnxOgTj5JLUGISpmkBM0\"\n  export API_TOKEN=\"$2y$16$HLh99.gCG85H1tLnpeRFYu3nZxkQ7MOwQpP8IKBCknH1khUfZsAI6\"\n  ```\n\n  \u0417\u0430\u0442\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0435 `source ~/.bashrc` (\u0438\u043b\u0438 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0439 \u0444\u0430\u0439\u043b), \u0447\u0442\u043e\u0431\u044b \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f.\n\n#### 3. \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0438:\n\n- **\u041b\u043e\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0422\u043e\u043a\u0435\u043d\u043e\u0432:**\n  \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0432\u044b \u043d\u0435 \u043b\u043e\u0433\u0438\u0440\u0443\u0435\u0442\u0435 \u0442\u043e\u043a\u0435\u043d\u044b \u043f\u043e \u043e\u0448\u0438\u0431\u043a\u0435. \u0412 \u0432\u0430\u0448\u0435\u043c \u043a\u043e\u0434\u0435 \u0442\u043e\u043a\u0435\u043d\u044b \u043d\u0435 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u044e\u0442\u0441\u044f \u0432 \u043b\u043e\u0433\u0430\u0445, \u0447\u0442\u043e \u0445\u043e\u0440\u043e\u0448\u043e \u0441 \u0442\u043e\u0447\u043a\u0438 \u0437\u0440\u0435\u043d\u0438\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438.\n\n- **\u041e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u041e\u0448\u0438\u0431\u043e\u043a:**\n  \u0420\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u0442\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438, \u0447\u0442\u043e `bot_token` \u0438 `API_TOKEN` \u043d\u0435 \u0440\u0430\u0432\u043d\u044b `None` \u043f\u0435\u0440\u0435\u0434 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0438\u0445 \u0432 \u0437\u0430\u043f\u0440\u043e\u0441\u0430\u0445.\n\n  ```python\n  if not bot_token:\n      logging.error(\"bot_token \u043d\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f.\")\n      exit(1)\n  \n  if not API_TOKEN:\n      logging.error(\"API_TOKEN \u043d\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f.\")\n      exit(1)\n  ```\n\n- **\u0417\u0430\u0449\u0438\u0442\u0430 \u0422\u043e\u043a\u0435\u043d\u043e\u0432:**\n  \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0432\u0430\u0448\u0438 \u0442\u043e\u043a\u0435\u043d\u044b \u043d\u0435 \u0441\u043a\u043e\u043c\u043f\u0440\u043e\u043c\u0435\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u044b. \u0415\u0441\u043b\u0438 \u043e\u043d\u0438 \u0431\u044b\u043b\u0438 \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d\u044b, \u043d\u0435\u043c\u0435\u0434\u043b\u0435\u043d\u043d\u043e \u0438\u0445 \u0438\u0437\u043c\u0435\u043d\u0438\u0442\u0435.\n\n#### \u041f\u0440\u0438\u043c\u0435\u0440 \u0418\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u041a\u043e\u0434\u0430:\n\n\u0412\u043e\u0442 \u043f\u0440\u0438\u043c\u0435\u0440 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f:\n\n```python\nimport subprocess\nimport time\nimport re\nimport xml.etree.ElementTree as ET\nimport pyperclip\nimport requests\nimport socket\nimport logging\nimport os\n\nfrom requests.adapters import HTTPAdapter\nfrom urllib3.util import Retry\n\n# \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438 ADB \u0438 Telegram \u0431\u043e\u0442\u0430\nADB_PATH = r\"C:\\\\Users\\\\B-ZONE\\\\Desktop\\\\platform-tools\\\\adb.exe\"  # \u041f\u043e\u043b\u043d\u044b\u0439 \u043f\u0443\u0442\u044c \u043a adb.exe\n\n# \u041f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0438\u0437 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438\nbot_token = os.getenv(\"BOT_TOKEN\")    # \u0418\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f\nchat_id = \"-1002057440931\"            # ID \u0432\u0430\u0448\u0435\u0439 \u0433\u0440\u0443\u043f\u043f\u044b\n\nAPI_URL = \"https://manage.stormapi.su/api/other_approve_register\"\nAPI_TOKEN = os.getenv(\"API_TOKEN\")    # \u0418\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f\n\npackage_name = 'com.autel.maxlink'\n\n# \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u043d\u0430\u043b\u0438\u0447\u0438\u044f \u0442\u043e\u043a\u0435\u043d\u043e\u0432\nif not bot_token:\n    logging.error(\"bot_token \u043d\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f.\")\n    exit(1)\n\nif not API_TOKEN:\n    logging.error(\"API_TOKEN \u043d\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f.\")\n    exit(1)\n\n# \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u043b\u043e\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')\n\n# \u041e\u0441\u0442\u0430\u043b\u044c\u043d\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u0432\u0430\u0448\u0435\u0433\u043e \u043a\u043e\u0434\u0430...\n# \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0444\u0443\u043d\u043a\u0446\u0438\u0438 run_adb_command, get_ui_dump, parse_device_id, create_session_with_retries, send_message_via_bot, send_device_id_to_api \u0438 \u0442.\u0434.\n```\n\n### \u0417\u0430\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\n\n\u041e\u0441\u043d\u043e\u0432\u043d\u0430\u044f \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0432 \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 `os.getenv()`. \u0418\u0441\u043f\u0440\u0430\u0432\u0438\u0432 \u044d\u0442\u043e\u0442 \u043c\u043e\u043c\u0435\u043d\u0442, \u0443\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0442\u043e\u043a\u0435\u043d\u044b \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u0438 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u0432\u0430\u0448\u0435\u043c\u0443 \u0441\u043a\u0440\u0438\u043f\u0442\u0443. \u042d\u0442\u043e \u0434\u043e\u043b\u0436\u043d\u043e \u0440\u0435\u0448\u0438\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0441 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u043e\u0439 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u0439 \u0447\u0435\u0440\u0435\u0437 Telegram \u0431\u043e\u0442\u0430 \u0438 \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435\u043c \u0441 API.\n\n\u0415\u0441\u043b\u0438 \u043f\u043e\u0441\u043b\u0435 \u0432\u043d\u0435\u0441\u0435\u043d\u0438\u044f \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0439 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u044e\u0442\u0441\u044f, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u044c\u0442\u0435 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u0435 \u043b\u043e\u0433\u0438, \u0447\u0442\u043e\u0431\u044b \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u043e\u043b\u0435\u0435 \u0434\u0435\u0442\u0430\u043b\u044c\u043d\u043e \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044e.","behavior":"degenerate_repetition","conversation_hash":"b9de639b3473f56fcc22e9313424884f","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":14735,"n_turns_in_conversation":1,"n_words":1469,"source_row_offset":9220,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:26 GMT","turn_id":"06d4c04d52117faada66d516","turn_index":0,"user_text":"import subprocess\nimport time\nimport re\nimport xml.etree.ElementTree as ET\nimport pyperclip\nimport requests\nimport socket\nimport logging\nimport os\n\nfrom requests.adapters import HTTPAdapter\nfrom urllib3.util import Retry\n\n# \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438 ADB \u0438 Telegram \u0431\u043e\u0442\u0430\nADB_PATH = r\"C:\\\\Users\\\\B-ZONE\\\\Desktop\\\\platform-tools\\\\adb.exe\"  # \u041f\u043e\u043b\u043d\u044b\u0439 \u043f\u0443\u0442\u044c \u043a adb.exe\n\n# \u041f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0438\u0437 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438\nbot_token = os.getenv(\"7033736801:AAGh-gPCffmyfU5MnxOgTj5JLUGISpmkBM0\")    # \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0435 \u044d\u0442\u0443 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f\nchat_id = \"-1002057440931\"                      # ID \u0432\u0430\u0448\u0435\u0439 \u0433\u0440\u0443\u043f\u043f\u044b\n\n# API \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438\nAPI_URL = \"https://manage.stormapi.su/api/other_approve_register\"\nAPI_TOKEN = os.getenv(\"$2y$16$HLh99.gCG85H1tLnpeRFYu3nZxkQ7MOwQpP8IKBCknH1khUfZsAI6\")              # \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0435 \u044d\u0442\u0443 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u044f\n\npackage_name = 'com.autel.maxlink'\n\n# \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u043b\u043e\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')\n\ndef run_adb_command(command):\n    \"\"\"\u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 ADB \u043a\u043e\u043c\u0430\u043d\u0434\u0443 \u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0432\u044b\u0432\u043e\u0434.\"\"\"\n    try:\n        logging.debug(f\"\u0412\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 ADB \u043a\u043e\u043c\u0430\u043d\u0434\u044b: {' '.join(command)}\")\n        result = subprocess.run([ADB_PATH] + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True)\n        logging.debug(f\"\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043a\u043e\u043c\u0430\u043d\u0434\u044b: {result.stdout}\")\n        return result.stdout\n    except subprocess.CalledProcessError as e:\n        logging.error(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0438 \u043a\u043e\u043c\u0430\u043d\u0434\u044b adb {' '.join(command)}: {e.stderr}\")\n        return None\n\ndef get_ui_dump():\n    \"\"\"\u041f\u043e\u043b\u0443\u0447\u0430\u0435\u0442 \u0434\u0430\u043c\u043f UI-\u0438\u0435\u0440\u0430\u0440\u0445\u0438\u0438 \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442 \u0435\u0433\u043e \u0432 \u0444\u0430\u0439\u043b \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430.\"\"\"\n    # \u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u0434\u0430\u043c\u043f UI\n    run_adb_command(['shell', 'uiautomator', 'dump', '/sdcard/ui_dump.xml'])\n    # \u0418\u0437\u043c\u0435\u043d\u044f\u0435\u043c \u043f\u0440\u0430\u0432\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430 \u043a \u0444\u0430\u0439\u043b\u0443, \u0447\u0442\u043e\u0431\u044b \u0435\u0433\u043e \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0442\u044c\n    run_adb_command(['shell', 'chmod', '666', '/sdcard/ui_dump.xml'])\n    # \u0427\u0438\u0442\u0430\u0435\u043c \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u0434\u0430\u043c\u043f\u0430\n    ui_dump = run_adb_command(['shell', 'cat', '/sdcard/ui_dump.xml'])\n    if ui_dump:\n        with open('ui_dump.xml', 'w', encoding='utf-8') as f:\n            f.write(ui_dump)\n        logging.info(\"UI-\u0434\u0430\u043c\u043f \u0441\u043e\u0445\u0440\u0430\u043d\u0451\u043d \u0432 \u0444\u0430\u0439\u043b 'ui_dump.xml'. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u0435\u0433\u043e \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435.\")\n    return ui_dump\n\ndef parse_device_id(ui_dump):\n    \"\"\"\u0418\u0437\u0432\u043b\u0435\u043a\u0430\u0435\u0442 Device ID \u0438\u0437 \u0434\u0430\u043c\u043f\u0430 UI-\u0438\u0435\u0440\u0430\u0440\u0445\u0438\u0438.\"\"\"\n    try:\n        root = ET.fromstring(ui_dump)\n    except ET.ParseError as e:\n        logging.error(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043f\u0430\u0440\u0441\u0438\u043d\u0433\u0435 UI-\u0434\u0430\u043c\u043f\u0430: {e}\")\n        return None\n\n    device_id = None\n    # \u0420\u0435\u043a\u0443\u0440\u0441\u0438\u0432\u043d\u044b\u0439 \u043f\u043e\u0438\u0441\u043a \u0432\u0441\u0435\u0445 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432\n    for node in root.iter('node'):\n        text = node.attrib.get('text', '')\n        # \u0428\u0430\u0431\u043b\u043e\u043d\u044b \u0434\u043b\u044f \u0440\u0430\u0437\u043d\u044b\u0445 \u043b\u043e\u043a\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0439\n        patterns = [\n            r'Device id:\\s*([A-Za-z0-9]+)',               # \u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439\n            r'ID \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432\u0430:\\s*([A-Za-z0-9]+)',           # \u0420\u0443\u0441\u0441\u043a\u0438\u0439\n            r'\u0418\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432\u0430:\\s*([A-Za-z0-9]+)'  # \u0414\u0440\u0443\u0433\u0438\u0435 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u044b\n        ]\n        for pattern in patterns:\n            match = re.match(pattern, text)\n            if match:\n                device_id = match.group(1)\n                logging.info(f\"\u041d\u0430\u0439\u0434\u0435\u043d Device id: {device_id}\")\n                return device_id\n    return device_id\n\ndef create_session_with_retries():\n    \"\"\"\u0421\u043e\u0437\u0434\u0430\u0451\u0442 \u0441\u0435\u0441\u0441\u0438\u044e \u0441 \u043f\u043e\u0432\u0442\u043e\u0440\u043d\u044b\u043c\u0438 \u043f\u043e\u043f\u044b\u0442\u043a\u0430\u043c\u0438 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432.\"\"\"\n    session = requests.Session()\n    retry = Retry(\n        total=5,\n        backoff_factor=1,\n        status_forcelist=[429, 500, 502, 503, 504],\n        allowed_methods=[\"HEAD\", \"GET\", \"OPTIONS\", \"POST\"]\n    )\n    adapter = HTTPAdapter(max_retries=retry)\n    session.mount('https://', adapter)\n    session.mount('http://', adapter)\n    return session\n\n# \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u0441\u0435\u0441\u0441\u0438\u044e \u0434\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0435\u0451 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442\nsession = create_session_with_retries()\n\ndef send_message_via_bot(message):\n    \"\"\"\u041e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u0435 \u0432 Telegram \u0433\u0440\u0443\u043f\u043f\u0443 \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442\u0430.\"\"\"\n    url = f\"https://api.telegram.org/bot{bot_token}/sendMessage\"\n    payload = {\n        'chat_id': chat_id,\n        'text': message\n    }\n    try:\n        response = session.post(url, data=payload, verify=True, allow_redirects=True, timeout=10)\n        response.raise_for_status()  # \u0412\u043e\u0437\u0431\u0443\u0434\u0438\u0442 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043f\u043b\u043e\u0445\u0438\u0445 \u043e\u0442\u0432\u0435\u0442\u043e\u0432\n        result = response.json()\n        if result.get(\"ok\"):\n            logging.info(\"\u0421\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u0435 \u0443\u0441\u043f\u0435\u0448\u043d\u043e \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043e \u0431\u043e\u0442\u043e\u043c.\")\n        else:\n            logging.error(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f: {result}\")\n    except requests.exceptions.RequestException as err:\n        # \u041f\u0435\u0447\u0430\u0442\u0430\u0435\u0442 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430 \u0434\u043b\u044f \u0431\u043e\u043b\u0435\u0435 \u0434\u0435\u0442\u0430\u043b\u044c\u043d\u043e\u0439 \u043e\u0442\u043b\u0430\u0434\u043a\u0438\n        try:\n            error_content = response.json()\n        except:\n            error_content = response.text\n        logging.error(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442: {err}\\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {error_content}\")\n        print(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442: {err}\\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {error_content}\")\n\ndef send_device_id_to_api(device_id):\n    \"\"\"\u041e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 Device ID \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u043d\u0430 API-\u044d\u043d\u0434\u043f\u043e\u0438\u043d\u0442.\"\"\"\n    headers = {\n        \"token\": API_TOKEN,  # \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043e\u043a 'token' \u0432\u043c\u0435\u0441\u0442\u043e 'Authorization'\n        \"Content-Type\": \"application/x-www-form-urlencoded\"\n    }\n    data = {\n        \"deviceId\": device_id\n    }\n    try:\n        logging.debug(f\"\u041e\u0442\u043f\u0440\u0430\u0432\u043a\u0430 \u0437\u0430\u043f\u0440\u043e\u0441\u0430 \u043d\u0430 {API_URL} \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438: {data} \u0438 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430\u043c\u0438: {headers}\")\n        response = session.post(API_URL, headers=headers, data=data, verify=True, allow_redirects=True, timeout=10)\n        logging.debug(f\"\u041f\u043e\u043b\u0443\u0447\u0435\u043d \u043e\u0442\u0432\u0435\u0442: {response.status_code} - {response.reason}\")\n        response.raise_for_status()\n        content_type = response.headers.get('Content-Type', '')\n        if 'application/json' in content_type:\n            result = response.json()\n            logging.debug(f\"\u041e\u0442\u0432\u0435\u0442 \u043e\u0442 API (JSON): {result}\")\n            logging.info(f\"\u041e\u0442\u0432\u0435\u0442 \u043e\u0442 API: {result}\")\n        else:\n            logging.warning(\"API \u0432\u0435\u0440\u043d\u0443\u043b \u043e\u0442\u0432\u0435\u0442, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043d\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f JSON.\")\n            logging.debug(f\"\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {response.text}\")\n            print(f\"API \u0432\u0435\u0440\u043d\u0443\u043b \u043e\u0442\u0432\u0435\u0442: {response.text}\")\n    except requests.exceptions.RequestException as err:\n        try:\n            error_content = response.json()\n        except:\n            error_content = response.text\n        logging.error(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 Device ID \u043d\u0430 API: {err}\\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {error_content}\")\n        print(f\"\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 Device ID \u043d\u0430 API: {err}\\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {error_content}\")\n\ndef main():\n    logging.info(\"\u0417\u0430\u043f\u0443\u0441\u043a \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f...\")\n    # \u0417\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u043d\u0430 \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432\u0435\n    run_adb_command(['shell', 'monkey', '-p', package_name, '-c', 'android.intent.category.LAUNCHER', '1'])\n    time.sleep(1)  # \u041e\u0436\u0438\u0434\u0430\u043d\u0438\u0435 \u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0438 \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f\n\n    logging.info(\"\u041f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0434\u0430\u043c\u043f\u0430 UI-\u0438\u0435\u0440\u0430\u0440\u0445\u0438\u0438...\")\n    ui_dump = get_ui_dump()\n    if not ui_dump:\n        logging.error(\"\u041d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0434\u0430\u043c\u043f UI.\")\n        return\n\n    logging.info(\"\u041f\u043e\u0438\u0441\u043a Device id...\")\n    device_id = parse_device_id(ui_dump)\n    if not device_id:\n        logging.error(\"\u041d\u0435 \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u043d\u0430\u0439\u0442\u0438 Device id.\")\n        return\n\n    # \u041a\u043e\u043f\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 \u0431\u0443\u0444\u0435\u0440 \u043e\u0431\u043c\u0435\u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0430 (\u043e\u043f\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e)\n    pyperclip.copy(device_id)\n    logging.info(\"Device id \u0441\u043a\u043e\u043f\u0438\u0440\u043e\u0432\u0430\u043d \u0432 \u0431\u0443\u0444\u0435\u0440 \u043e\u0431\u043c\u0435\u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0430.\")\n\n    # \u041e\u0442\u043f\u0440\u0430\u0432\u043a\u0430 Device id \u0432 Telegram \u0433\u0440\u0443\u043f\u043f\u0443 \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442\u0430\n    message = f\"{device_id}\"\n    send_message_via_bot(message)\n\n    # \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e: \u041e\u0442\u043f\u0440\u0430\u0432\u043a\u0430 Device id \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u043d\u0430 API\n    send_device_id_to_api(device_id)\n\nif __name__ == \"__main__\":\n    main()\n\n\u0421\u0443\u0442\u044c \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u0432 \u0442\u0435\u043b\u0435\u0433\u0440\u0430\u043c \u0433\u0440\u0443\u043f\u043f\u0443 \u0442\u043e \u0447\u0442\u043e \u0441\u043a\u043e\u043f\u0438\u0440\u043e\u0432\u0430\u043b \u0438 \u0432 \u043e\u0442\u0432\u0435\u0442 \u0438\u0437 \u0442\u0435\u043b\u0435\u0433\u0440\u0430\u043c \u0433\u0440\u0443\u043f\u043f\u0443 \u0442\u0430\u043c \u0435\u0441\u0442\u044c \u0431\u043e\u0442 \u043c\u044b \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043e\u0442 \u043d\u0435\u0433\u043e \"\u0420\u0435\u0433\u0438\u0441\u0442\u0440\u0430\u0446\u0438\u044f \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432\u0430 \u0441 ID 17cfca34003ab529 \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0430!\" \u043a\u043e\u0433\u0434\u0430 \u043d\u0430\u0448 \u0431\u043e\u0442 \u0441\u043a\u0438\u0434\u044b\u0432\u0430\u0435\u0442 \u0435\u043c\u0443 \u0432 \u0442\u0435\u043b\u0435\u0433\u0440\u0430\u043c \u0447\u0442\u043e \u0442\u043e \u0438\u0445 \u0431\u043e\u0442 \u043d\u0435 \u0434\u0430\u0435\u0442 \u0442\u0430\u043a\u043e\u0433\u043e \u043e\u0442\u0432\u0435\u0442\u0430\n\u041b\u043e\u0433\u0438\n2024-10-08 21:00:45,363 - INFO - \u041f\u043e\u0438\u0441\u043a Device id...\n2024-10-08 21:00:45,366 - INFO - \u041d\u0430\u0439\u0434\u0435\u043d Device id: ab169b43cca1d6ac\n2024-10-08 21:00:45,370 - INFO - Device id \u0441\u043a\u043e\u043f\u0438\u0440\u043e\u0432\u0430\u043d \u0432 \u0431\u0443\u0444\u0435\u0440 \u043e\u0431\u043c\u0435\u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0430.\n2024-10-08 21:00:45,376 - DEBUG - Starting new HTTPS connection (1): api.telegram.org:443\n2024-10-08 21:00:45,515 - DEBUG - https://api.telegram.org:443 \"POST /botNone/sendMessage HTTP/11\" 404 55\n2024-10-08 21:00:45,517 - ERROR - \u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442: 404 Client Error: Not Found for url: https://api.telegram.org/botNone/sendMessage\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {'ok': False, 'error_code': 404, 'description': 'Not Found'}\n\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u0442: 404 Client Error: Not Found for url: https://api.telegram.org/botNone/sendMessage\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {'ok': False, 'error_code': 404, 'description': 'Not Found'}\n2024-10-08 21:00:45,519 - DEBUG - \u041e\u0442\u043f\u0440\u0430\u0432\u043a\u0430 \u0437\u0430\u043f\u0440\u043e\u0441\u0430 \u043d\u0430 https://manage.stormapi.su/api/other_approve_register \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438: {'deviceId': 'ab169b43cca1d6ac'} \u0438 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430\u043c\u0438: {'token': None, 'Content-Type': 'application/x-www-form-urlencoded'}\n2024-10-08 21:00:45,520 - DEBUG - Starting new HTTPS connection (1): manage.stormapi.su:443\n2024-10-08 21:00:45,640 - DEBUG - https://manage.stormapi.su:443 \"POST /api/other_approve_register HTTP/11\" 401 54\n2024-10-08 21:00:45,640 - DEBUG - \u041f\u043e\u043b\u0443\u0447\u0435\u043d \u043e\u0442\u0432\u0435\u0442: 401 - Unauthorized\n2024-10-08 21:00:45,641 - ERROR - \u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 Device ID \u043d\u0430 API: 401 Client Error: Unauthorized for url: https://manage.stormapi.su/api/other_approve_register\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {'error': True, 'message': '\u041e\u0448\u0438\u0431\u043a\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430'}\n\u041e\u0448\u0438\u0431\u043a\u0430 \u043f\u0440\u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u043a\u0435 Device ID \u043d\u0430 API: 401 Client Error: Unauthorized for url: https://manage.stormapi.su/api/other_approve_register\n\u0421\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u043e\u0442\u0432\u0435\u0442\u0430: {'error': True, 'message': '\u041e\u0448\u0438\u0431\u043a\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430'}\n\n"},{"assistant_initiates":false,"asst_text":"Hello! It looks like there isn't a previous message in our conversation. Could you please provide more details or clarify your request? I'm here to help!","behavior":"degenerate_repetition","conversation_hash":"64cfd5255a908d2a04c4afbcd070cd34","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Nolang","model":"o1-mini-2024-09-12","n_chars":153,"n_turns_in_conversation":1,"n_words":26,"source_row_offset":9221,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:31 GMT","turn_id":"0cabbe06843a6899c3dc65d1","turn_index":0,"user_text":""},{"assistant_initiates":false,"asst_text":"Yes, there are several areas in your code where improvements can be made to enhance readability, maintainability, and performance. Below, I'll outline the key areas along with suggestions:\n\n1. **Order of `dotenv.config()` and Environment Variable Access**:\n   - **Issue**: You're accessing `process.env` variables before calling `dotenv.config()`. This means the environment variables from your `.env` file won't be loaded yet.\n   - **Solution**: Move `dotenv.config()` to the very top of your script, before any environment variables are accessed.\n\n     ```javascript\n     import dotenv from \"dotenv\";\n     dotenv.config();\n     ```\n\n2. **Error Handling with JSON Parsing**:\n   - **Issue**: Directly parsing environment variables with `JSON.parse()` can throw errors if the variables are undefined or not valid JSON.\n   - **Solution**: Add error handling when parsing these variables to gracefully handle errors and provide informative messages.\n\n     ```javascript\n     let BLACK_LIST_FAMILIES = [];\n     let WHITE_LIST_FAMILIES = [];\n\n     try {\n       BLACK_LIST_FAMILIES = JSON.parse(process.env.BLACK_LIST_FAMILIES || '[]');\n     } catch (error) {\n       console.error(\"Invalid JSON for BLACK_LIST_FAMILIES:\", error);\n     }\n\n     try {\n       WHITE_LIST_FAMILIES = JSON.parse(process.env.WHITE_LIST_FAMILIES || '[]');\n     } catch (error) {\n       console.error(\"Invalid JSON for WHITE_LIST_FAMILIES:\", error);\n     }\n     ```\n\n3. **Use of `includes` Instead of `find` for Arrays of Strings**:\n   - **Issue**: You're using `find` to check if a string exists in an array of strings, which is less efficient.\n   - **Solution**: Use `includes` for better readability and performance.\n\n     ```javascript\n     const blacklistMatch = BLACK_LIST_FAMILIES.includes(recInstanceFamily);\n     const whitelistMatch = WHITE_LIST_FAMILIES.includes(recInstanceFamily);\n     ```\n\n4. **Parallelizing API Calls with Concurrency Control**:\n   - **Issue**: Your code processes accounts and pagination sequentially, which can be slow for many accounts.\n   - **Solution**: Use `Promise.all` with concurrency limits to parallelize API calls while respecting rate limits.\n\n     ```javascript\n     import pLimit from 'p-limit';\n\n     async function getEC2InstanceRecommendations(accountIds) {\n       const limit = pLimit(5); // Adjust the concurrency level as needed\n       const recommendations = [];\n\n       const accountPromises = accountIds.map(accountId => limit(async () => {\n         let nextToken;\n         do {\n           const commandParams = { /* ... */ };\n           const command = new GetEC2InstanceRecommendationsCommand(commandParams);\n           const response = await client.send(command);\n           recommendations.push(...response.instanceRecommendations);\n           nextToken = response.nextToken;\n         } while (nextToken);\n       }));\n\n       await Promise.all(accountPromises);\n\n       // ... rest of your code ...\n     }\n     ```\n\n5. **Handling Undefined Properties Safely**:\n   - **Issue**: Directly accessing properties like `item.tags` or `item.recommendationOptions` could lead to errors if they are `undefined`.\n   - **Solution**: Use optional chaining (`?.`) to safely access nested properties.\n\n     ```javascript\n     const appNameTag = item.tags?.find(tag => tag.key === \"APP:NAME\");\n     ```\n\n6. **Improving Variable Names for Clarity**:\n   - **Issue**: Using generic column names like `col1`, `col2`, etc., reduces readability.\n   - **Solution**: Use meaningful property names that reflect the data they hold.\n\n     ```javascript\n     return {\n       accountId: item.accountId,\n       instanceId: item.instanceArn.split(\"/\")[1],\n       instanceName: item.instanceName,\n       // ... other properties ...\n     };\n     ```\n\n   - **Note**: Update `createPriceListExcel` and any other functions consuming this data to align with the new property names.\n\n7. **Consistent Coding Style and Semicolons**:\n   - **Issue**: The inconsistent use of semicolons and formatting can make the code harder to read and maintain.\n   - **Solution**: Use a linter like ESLint with a standard style guide (e.g., Airbnb, StandardJS) to enforce consistency.\n\n8. **Simplifying Error Logging and Propagation**:\n   - **Issue**: Catching errors only to rethrow them adds unnecessary code.\n   - **Solution**: Remove try-catch blocks that don't add value. Let errors propagate naturally unless you need to handle them specifically.\n\n     ```javascript\n     async function getRecommendations() {\n       const accountIds = await getAllAccountIds();\n       const recommendations = await getEC2InstanceRecommendations(accountIds);\n       return recommendations;\n     }\n     ```\n\n9. **Ensuring All Required Data Columns Are Included**:\n   - **Issue**: There's a missing `col14`, which could be an accidental omission.\n   - **Solution**: Review the data mapping and ensure all required columns are properly included.\n\n10. **Proper Proxy Configuration for AWS SDK**:\n    - **Issue**: The way the proxy agent is set up might not be fully compatible with AWS SDK v3.\n    - **Solution**: Use the recommended method to set up a proxy with AWS SDK v3.\n\n      ```javascript\n      import { NodeHttpHandler } from \"@aws-sdk/node-http-handler\";\n      import HttpsProxyAgent from \"https-proxy-agent\";\n\n      if (HTTP_PROXY) {\n        const proxyAgent = new HttpsProxyAgent(\"http://\" + proxy_auth + \"@\" + HTTP_PROXY);\n        useProxy = {\n          requestHandler: new NodeHttpHandler({\n            httpAgent: proxyAgent,\n            httpsAgent: proxyAgent,\n          }),\n        };\n      }\n      ```\n\n11. **Avoid Hardcoding AWS Account IDs and Role ARNs**:\n    - **Issue**: The role ARN is hardcoded, which reduces flexibility and might not be secure.\n    - **Solution**: Move these values to environment variables or configuration files.\n\n      ```javascript\n      const FINOPS_COST_REPORT_ROLE_ARN = process.env.FINOPS_COST_REPORT_ROLE_ARN;\n      ```\n\n12. **Validation and Defaults for Environment Variables**:\n    - **Issue**: If environment variables are missing, your code may fail unexpectedly.\n    - **Solution**: Provide default values or throw informative errors when required environment variables are missing.\n\n      ```javascript\n      if (!process.env.BUCKET_NAME_SHARED) {\n        throw new Error(\"Missing required environment variable BUCKET_NAME_SHARED\");\n      }\n      ```\n\n13. **Optimizing AWS SDK Client Initialization**:\n    - **Issue**: Credentials are being set individually for each client.\n    - **Solution**: Since you're assuming a role, set the credentials once and reuse them.\n\n14. **Lambda Handler Best Practices**:\n    - **Issue**: The handler function is returning a custom response format.\n    - **Solution**: Ensure the response format conforms to API Gateway or Lambda Proxy Integration requirements if applicable.\n\n15. **Comments and Documentation**:\n    - **Issue**: The code lacks comments explaining complex logic, which can make maintenance harder.\n    - **Solution**: Add comments where necessary to explain non-obvious portions of the code.\n\nBy implementing these improvements, your code will be more robust, maintainable, and efficient. Here's how your refactored code might look with some of these changes applied:\n\n```javascript\nimport dotenv from \"dotenv\";\ndotenv.config();\n\nimport {\n  ComputeOptimizerClient,\n  GetEC2InstanceRecommendationsCommand,\n} from \"@aws-sdk/client-compute-optimizer\";\nimport {\n  OrganizationsClient,\n  ListAccountsCommand,\n} from \"@aws-sdk/client-organizations\";\nimport { writeExcelFile, createPriceListExcel } from \"./helperExcel.mjs\";\nimport { getSecretValue } from \"./helper.mjs\";\nimport { getAssumedRoleCredentials } from \"./assume_master.mjs\";\nimport { HttpsProxyAgent } from \"hpagent\";\n\nconst {\n  AWS_REGION,\n  BLACK_LIST_FAMILIES: BLACK_LIST_FAMILIES_STR,\n  WHITE_LIST_FAMILIES: WHITE_LIST_FAMILIES_STR,\n  BUCKET_NAME_SHARED,\n  STS_VPCE,\n  HTTP_PROXY,\n  SECRET_NAME_FINOPS_AUTH,\n  FINOPS_COST_REPORT_ROLE_ARN,\n} = process.env;\n\nif (!AWS_REGION || !BUCKET_NAME_SHARED || !FINOPS_COST_REPORT_ROLE_ARN) {\n  throw new Error(\"Missing required environment variables.\");\n}\n\nlet BLACK_LIST_FAMILIES = [];\nlet WHITE_LIST_FAMILIES = [];\n\ntry {\n  BLACK_LIST_FAMILIES = JSON.parse(BLACK_LIST_FAMILIES_STR || \"[]\");\n} catch (error) {\n  console.error(\"Invalid JSON for BLACK_LIST_FAMILIES:\", error);\n}\n\ntry {\n  WHITE_LIST_FAMILIES = JSON.parse(WHITE_LIST_FAMILIES_STR || \"[]\");\n} catch (error) {\n  console.error(\"Invalid JSON for WHITE_LIST_FAMILIES:\", error);\n}\n\nconst finopsAuth = await getSecretValue(AWS_REGION, SECRET_NAME_FINOPS_AUTH);\nconst masterCredentials = await getAssumedRoleCredentials(\n  AWS_REGION,\n  STS_VPCE,\n  FINOPS_COST_REPORT_ROLE_ARN,\n  \"LambdaCrossAccountSession\"\n);\n\nlet proxySettings = {};\nif (HTTP_PROXY) {\n  const proxyAuth = finopsAuth[\"proxy_auth\"];\n  const agent = new HttpsProxyAgent({\n    proxy: `http://${proxyAuth}@${HTTP_PROXY}`,\n  });\n  proxySettings = {\n    requestHandler: { httpAgent: agent, httpsAgent: agent },\n  };\n}\n\nconst organizationsClient = new OrganizationsClient({\n  region: AWS_REGION,\n  credentials: masterCredentials,\n  ...proxySettings,\n});\n\nconst computeOptimizerClient = new ComputeOptimizerClient({\n  region: AWS_REGION,\n  credentials: masterCredentials,\n  ...proxySettings,\n});\n\nasync function getEC2InstanceRecommendations(accountIds) {\n  const limit = pLimit(5); // Adjust concurrency as needed\n  const recommendations = [];\n\n  const accountPromises = accountIds.map((accountId) =>\n    limit(async () => {\n      let nextToken;\n      do {\n        const commandParams = {\n          accountIds: [accountId],\n          nextToken,\n          recommendationPreferences: {\n            cpuVendorArchitectures: [\"AWS_ARM64\", \"CURRENT\"],\n          },\n        };\n        const command = new GetEC2InstanceRecommendationsCommand(commandParams);\n        const response = await computeOptimizerClient.send(command);\n        recommendations.push(...(response.instanceRecommendations || []));\n        nextToken = response.nextToken;\n      } while (nextToken);\n    })\n  );\n\n  await Promise.all(accountPromises);\n\n  const transformedData = recommendations.flatMap((item) => {\n    const recommendationOptions = item.recommendationOptions || [];\n    return recommendationOptions\n      .filter((rec) => item.currentInstanceType !== rec.instanceType)\n      .map((rec) => {\n        const appNameTag = item.tags?.find((tag) => tag.key === \"APP:NAME\");\n        const appNameValue = appNameTag?.value || null;\n\n        const ownerITTag = item.tags?.find((tag) => tag.key === \"OWNER:IT\");\n        const ownerITValue = ownerITTag?.value || null;\n\n        const envTag = item.tags?.find((tag) => tag.key === \"ENV:DETAIL\");\n        const envValue = envTag?.value || null;\n\n        const recInstanceFamily = rec.instanceType.split(\".\")[0];\n        const monetaRecommendation = BLACK_LIST_FAMILIES.includes(recInstanceFamily)\n          ? \"Blacklist\"\n          : WHITE_LIST_FAMILIES.includes(recInstanceFamily)\n          ? \"Whitelist\"\n          : null;\n\n        return {\n          accountId: item.accountId,\n          instanceId: item.instanceArn.split(\"/\")[1],\n          instanceName: item.instanceName,\n          currentPerformanceRisk: item.currentPerformanceRisk,\n          recommendationPerformanceRisk: rec.performanceRisk,\n          migrationEffort: rec.migrationEffort,\n          currentInstanceType: item.currentInstanceType,\n          recommendedInstanceType: rec.instanceType,\n          projectedUtilizationMetrics: rec.projectedUtilizationMetrics,\n          rank: rec.rank,\n          estimatedMonthlySavings:\n            rec.savingsOpportunityAfterDiscounts?.estimatedMonthlySavings.value || \"\",\n          monetaRecommendation,\n          platformDifferences: rec.platformDifferences,\n          appName: appNameValue,\n          ownerIT: ownerITValue,\n          env: envValue,\n        };\n      });\n  });\n\n  const workbook = await createPriceListExcel(transformedData);\n  await writeExcelFile(workbook, AWS_REGION, BUCKET_NAME_SHARED);\n\n  return transformedData;\n}\n\nasync function getAllAccountIds() {\n  const accounts = [];\n  let nextToken;\n\n  do {\n    const command = new ListAccountsCommand({ NextToken: nextToken });\n    const response = await organizationsClient.send(command);\n    accounts.push(...(response.Accounts?.map((account) => account.Id) || []));\n    nextToken = response.NextToken;\n  } while (nextToken);\n\n  return accounts;\n}\n\nexport const handler = async () => {\n  try {\n    const accountIds = await getAllAccountIds();\n    const result = await getEC2InstanceRecommendations(accountIds);\n    return {\n      statusCode: 200,\n      body: JSON.stringify(result),\n    };\n  } catch (error) {\n    console.error(\"Error in handler:\", error);\n    return {\n      statusCode: 500,\n      body: JSON.stringify({ error: error.message }),\n    };\n  }\n};\n```\n\n**Additional Notes**:\n\n- **Dependency on External Modules**: Ensure that your helper modules (`helperExcel.mjs`, `helper.mjs`, `assume_master.mjs`) are updated to handle any changes, such as the new property names in the transformed data.\n- **AWS Rate Limits**: Be cautious with the concurrency level to avoid throttling. AWS services have rate limits, and exceeding them can cause your requests to fail.\n- **Security Best Practices**: Avoid logging sensitive information and ensure that all credentials and secrets are securely stored and accessed.\n- **Testing**: Thoroughly test the updated script in a non-production environment to validate all changes.\n\nBy addressing these points, your code will be more efficient, easier to understand, and better equipped to handle errors and edge cases.","behavior":"degenerate_repetition","conversation_hash":"981a4eba45f0c42bdd4c565de4fbda8c","conversation_redacted":false,"conversation_toxic":false,"country":"Czechia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-preview-2024-09-12","n_chars":19325,"n_turns_in_conversation":1,"n_words":1939,"source_row_offset":9222,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:35 GMT","turn_id":"ac0751d18af9c8ab09243e46","turn_index":0,"user_text":"do you see something to improve on this code?\n\nimport {\n  ComputeOptimizerClient,\n  GetEC2InstanceRecommendationsCommand,\n} from \"@aws-sdk/client-compute-optimizer\";\nimport {\n  OrganizationsClient,\n  ListAccountsCommand,\n} from \"@aws-sdk/client-organizations\";\n\nimport dotenv from \"dotenv\";\nimport { writeExcelFile, createPriceListExcel } from \"./helperExcel.mjs\";\nimport { getSecretValue } from \"./helper.mjs\";\nimport { getAssumedRoleCredentials } from \"./assume_master.mjs\";\nimport { HttpsProxyAgent } from \"hpagent\";\n\nconst BLACK_LIST_FAMILIES = JSON.parse(process.env.BLACK_LIST_FAMILIES);\nconst WHITE_LIST_FAMILIES = JSON.parse(process.env.WHITE_LIST_FAMILIES);\nconst BUCKET_NAME_SHARED = process.env.BUCKET_NAME_SHARED;\nconst STS_VPCE = process.env.STS_VPCE;\nconst HTTP_PROXY = process.env.HTTP_PROXY;\nconst SECRET_NAME_FINOPS_AUTH = process.env.SECRET_NAME_FINOPS_AUTH;\n\ndotenv.config();\n\nlet finops_auth = await getSecretValue(\n  process.env.AWS_REGION,\n  SECRET_NAME_FINOPS_AUTH\n); // Format of secret value username:pass\n\nlet master_credentials = await getAssumedRoleCredentials(\n  process.env.AWS_REGION,\n  STS_VPCE,\n  \"arn:aws:iam::198979247696:role/finops-cost-report\",\n  \"LambdaCrossAccountSession\"\n);\n\nlet useProxy = null;\nif (HTTP_PROXY) {\n  let proxy_auth = finops_auth[\"proxy_auth\"]; // Format of secret value username:pass\n\n  const agent = new HttpsProxyAgent({\n    proxy: \"http://\" + proxy_auth + \"@\" + HTTP_PROXY,\n  });\n\n  useProxy = {\n    requestHandler: { httpAgent: agent, httpsAgent: agent },\n  };\n}\n\nconst organizationsClient = new OrganizationsClient({\n  region: process.env.AWS_REGION,\n  ...useProxy,\n  credentials: {\n    accessKeyId: master_credentials.AccessKeyId,\n    secretAccessKey: master_credentials.SecretAccessKey,\n    sessionToken: master_credentials.SessionToken,\n  },\n});\n\nconst client = new ComputeOptimizerClient({\n  region: process.env.AWS_REGION,\n  ...useProxy,\n  credentials: {\n    accessKeyId: master_credentials.AccessKeyId,\n    secretAccessKey: master_credentials.SecretAccessKey,\n    sessionToken: master_credentials.SessionToken,\n  },\n});\n\nasync function getEC2InstanceRecommendations(accountIds) {\n  const recommendations = [];\n  for (const accountId of accountIds) {\n    let nextToken;\n\n    do {\n      const commandParams = {\n        accountIds: [accountId],\n        nextToken,\n        recommendationPreferences: {\n          cpuVendorArchitectures: [\"AWS_ARM64\", \"CURRENT\"],\n        },\n      };\n      const command = new GetEC2InstanceRecommendationsCommand(commandParams);\n      const response = await client.send(command);\n      recommendations.push(...response.instanceRecommendations);\n      nextToken = response.nextToken;\n    } while (nextToken);\n  }\n\n  const transformedData = recommendations.flatMap((item) => {\n    return item.recommendationOptions\n      .filter((rec) => item.currentInstanceType !== rec.instanceType)\n      .map((rec) => {\n        // console.log (\"recommendations\",rec.platformDifferences)\n        const appNameTag = item.tags.find((tag) => tag.key === \"APP:NAME\");\n        const appNameValue = appNameTag ? appNameTag.value : null;\n\n        const ownerITTag = item.tags.find((tag) => tag.key === \"OWNER:IT\");\n        const ownerITValue = ownerITTag ? ownerITTag.value : null;\n\n        const envTag = item.tags.find((tag) => tag.key === \"ENV:DETAIL\");\n        const envValue = envTag ? envTag.value : null;\n\n        const blacklistMatch = BLACK_LIST_FAMILIES.find(\n          (family) => family === rec.instanceType.split(\".\")[0]\n        );\n        const whitelistMatch = WHITE_LIST_FAMILIES.find(\n          (family) => family === rec.instanceType.split(\".\")[0]\n        );\n        const monetaRecommendation = blacklistMatch\n          ? \"Blacklist\"\n          : whitelistMatch\n          ? \"Whitelist\"\n          : null;\n\n        return {\n          col1: item.accountId,\n          col2: item.instanceArn.split(\"/\")[1],\n          col3: item.instanceName,\n          col4: item.currentPerformanceRisk,\n          col5: rec.performanceRisk,\n          col6: rec.migrationEffort,\n          col7: item.currentInstanceType,\n          col8: rec.instanceType,\n          col9: rec.projectedUtilizationMetrics,\n          col10: rec.rank,\n          col11: rec.savingsOpportunityAfterDiscounts\n            ? rec.savingsOpportunityAfterDiscounts.estimatedMonthlySavings.value\n            : \"\",\n          col12: monetaRecommendation,\n          col13: rec.platformDifferences,\n          col15: appNameValue,\n          col16: ownerITValue,\n          col17: envValue,\n        };\n      });\n  });\n\n  let workbook = await createPriceListExcel(transformedData);\n  await writeExcelFile(workbook, AWS_REGION, BUCKET_NAME_SHARED);\n\n  return transformedData;\n}\n\nasync function getRecommendations() {\n  try {\n    const accountIds = await getAllAccountIds();\n    const recommendations = await getEC2InstanceRecommendations(accountIds);\n    return recommendations;\n  } catch (error) {\n    console.error(\"Error:\", error);\n    throw error;\n  }\n}\n\nasync function getAllAccountIds() {\n  let accounts = [];\n  let nextToken;\n\n  do {\n    const command = new ListAccountsCommand({ NextToken: nextToken });\n    const response = await organizationsClient.send(command);\n\n    accounts.push(...response.Accounts.map((account) => account.Id)); // Collect account IDs\n    nextToken = response.NextToken; // Handle pagination\n  } while (nextToken);\n\n  return accounts;\n}\n\nexport const handler = async () => {\n  let response = {};\n  try {\n    let result = await getRecommendations();\n    response = {\n      statusCode: 200,\n      body: result,\n    };\n  } catch (e) {\n    response = {\n      statusCode: 500,\n      body: e.toString(),\n    };\n  }\n  return response;\n};\n"},{"assistant_initiates":false,"asst_text":"To make your dating profile more attractive and engaging, it's great to focus on the positive aspects of your personality and interests. Instead of highlighting stress, you can emphasize your love for staying active and keeping busy with activities you enjoy. Here's how you might rephrase your statement:\n\n- **\"I thrive on staying active and having fun, whether it's playing board games, working out, or joining meetups.\"**\n\n- **\"I enjoy keeping both my mind and body busy\u2014I'm into board games, exercise, and exploring new social events.\"**\n\n- **\"Always up for an adventure, I love engaging in activities like board games, hitting the gym, or meeting new people at local events.\"**\n\n- **\"Staying active keeps me energized, so you'll often find me immersed in a board game, breaking a sweat during a workout, or enjoying a meetup.\"**\n\nBy focusing on your enthusiasm for activities that keep you engaged, you present yourself as dynamic and proactive. This approach highlights your interests and positive traits, making your profile more appealing to others who share similar passions.","behavior":"degenerate_repetition","conversation_hash":"d4b1d5aed86eeb6177c21d4c8d7fe0e0","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-preview-2024-09-12","n_chars":1289,"n_turns_in_conversation":1,"n_words":206,"source_row_offset":9223,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:36 GMT","turn_id":"33300c0b16db74d87bdd9614","turn_index":0,"user_text":"how to communicate this better, or more attractively, on  dating profile: im a very stressed out person so i enjoy keeping myself physically busy, wether thats board games, excerise, or some other meetup."},{"assistant_initiates":false,"asst_text":"The author's intention in the GitHub issue titled \"How to use ActionMode with mini drawers?\" is best categorized as \"other.\" The issue does not explicitly describe an unexpected problem (bug) or request a new feature (enhancement). It appears to be a question or request for guidance on using a specific feature.","behavior":"degenerate_repetition","conversation_hash":"40a455e6c47ed1edbcaf3f9a559589d6","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1088,"n_turns_in_conversation":1,"n_words":167,"source_row_offset":9224,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:36 GMT","turn_id":"eaa6a27f6422e3d092a92cc2","turn_index":0,"user_text":"Assistant: \nUser: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nUser: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nDoes the author's intention in this GitHub issue relate to a bug or enhancement or other?\nissue: How to use ActionMode with mini drawers?'\nintention: '\nAssistant:\nAssistant:"},{"assistant_initiates":false,"asst_text":"The author's intention in the GitHub issue \"Catalog App: Shape Theming Demo is Crashing\" relates to a **bug**, as it indicates an unexpected problem or unintended behavior (crashing).","behavior":"degenerate_repetition","conversation_hash":"d9a940f42a71ab6268c0badcd12c3ce2","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":962,"n_turns_in_conversation":1,"n_words":144,"source_row_offset":9225,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:40 GMT","turn_id":"de363331de64840f8e34cce0","turn_index":0,"user_text":"Assistant: \nUser: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nUser: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nDoes the author's intention in this GitHub issue relate to a bug or enhancement or other?\nissue: Catalog App: Shape Theming Demo is Crashing'\nintention: '\nAssistant:\nAssistant:"},{"assistant_initiates":false,"asst_text":"The error you're encountering:\n\n```\nTypeError: Cannot read property 'on' of undefined\n```\n\nindicates that `eventEmitter` is `undefined` when you're trying to use the `.on` method on it within your `AuthProvider`. This typically happens when `eventEmitter` is either not defined, not properly exported, or not correctly imported from your `../services/api` module.\n\n### **Probable Cause**\n\nIn your `AuthContext.js`, you're importing `eventEmitter` from `../services/api`:\n\n```javascript\nimport { api, eventEmitter } from '../services/api';\n```\n\nHowever, the error suggests that `eventEmitter` is not correctly exported or initialized in the `services/api.js` file. \n\n### **Solution**\n\nTo resolve this issue, you need to ensure that `eventEmitter` is properly created and exported in your `services/api.js` file. Here's a step-by-step guide to help you fix the problem:\n\n1. **Choose an Event Emitter Library Compatible with React Native**\n\n   React Native doesn't have built-in support for Node.js's `EventEmitter`, but you can use libraries like [`react-native-eventemitter`](https://www.npmjs.com/package/react-native-eventemitter) or [`mitt`](https://www.npmjs.com/package/mitt). For simplicity and lightweight, we'll use `mitt` in this example.\n\n2. **Install `mitt`**\n\n   Run the following command in your project directory to install `mitt`:\n\n   ```bash\n   npm install mitt\n   # or\n   yarn add mitt\n   ```\n\n3. **Set Up `eventEmitter` in `services/api.js`**\n\n   Update your `services/api.js` to include and export `eventEmitter`. Here's how you can do it using `mitt`:\n\n   ```javascript\n   // services/api.js\n\n   import axios from 'axios';\n   import mitt from 'mitt';\n\n   // Initialize event emitter\n   const eventEmitter = mitt();\n\n   // Create Axios instance\n   const api = axios.create({\n     baseURL: 'https://your-api-base-url.com',\n     // Add other Axios configurations here\n   });\n\n   // Optionally, set up Axios interceptors to emit events\n   api.interceptors.response.use(\n     response => response,\n     error => {\n       if (error.response && error.response.status === 401) {\n         // Emit a 'logout' event when a 401 Unauthorized response is received\n         eventEmitter.emit('logout');\n       }\n       return Promise.reject(error);\n     }\n   );\n\n   export { api, eventEmitter };\n   ```\n\n   **Explanation:**\n   - **Initialize `mitt`:** `mitt` is a minimalist event emitter library. By initializing it, you create an `eventEmitter` instance.\n   - **Axios Instance:** You can set up Axios interceptors to automatically emit events based on certain responses, such as a `401 Unauthorized` error prompting a logout.\n   - **Exporting:** Both `api` and `eventEmitter` are exported for use in other parts of the application.\n\n4. **Ensure Correct Import in `AuthContext.js`**\n\n   Make sure that your `AuthContext.js` correctly imports `eventEmitter`:\n\n   ```javascript\n   // frontend/context/AuthContext.js\n\n   import React, { createContext, useState, useEffect } from 'react';\n   import * as SecureStore from 'expo-secure-store';\n   import { Alert } from 'react-native';\n   import jwtDecode from 'jwt-decode';\n   import { api, eventEmitter } from '../services/api'; // Ensure this path is correct\n   // ... rest of your code\n   ```\n\n   **Tips:**\n   - **Check Import Path:** Ensure that `'../services/api'` is the correct relative path from `AuthContext.js` to `api.js`.\n   - **Verify Exports:** Confirm that both `api` and `eventEmitter` are being exported from `services/api.js`.\n\n5. **Handle the Event in `AuthProvider`**\n\n   Now that `eventEmitter` is correctly set up and exported, your `AuthProvider` should be able to listen to the `'logout'` event without issues.\n\n   ```javascript\n   // frontend/context/AuthContext.js\n\n   useEffect(() => {\n     loadToken();\n\n     // Listen for logout events emitted by api.js\n     const handleLogout = () => {\n       signOut();\n       // Optionally, navigate to the login screen if navigation is accessible here\n       // e.g., navigation.reset({ index: 0, routes: [{ name: 'Login' }] });\n     };\n\n     eventEmitter.on('logout', handleLogout);\n\n     // Clean up the listener on unmount\n     return () => {\n       eventEmitter.off('logout', handleLogout);\n     };\n   }, []);\n   ```\n\n6. **Test the Implementation**\n\n   After making these changes:\n\n   - **Restart Metro Bundler:** Sometimes, changes in native modules or new installations require restarting the bundler.\n     ```bash\n     npx react-native start --reset-cache\n     ```\n   - **Run the App:** Ensure that the app no longer throws the `TypeError` and that the logout functionality works as expected.\n\n### **Additional Recommendations**\n\n- **Error Handling:** Ensure that all potential points of failure, especially around asynchronous operations, are adequately handled to prevent unhandled promise rejections or silent failures.\n  \n- **Security:** Always validate and sanitize tokens and sensitive data. Ensure that JWT tokens are stored securely and are not vulnerable to attacks.\n\n- **Navigation After Logout:** If you're using React Navigation or any other navigation library, ensure that after a logout, the user is redirected appropriately (e.g., to the login screen).\n\n### **Final Thoughts**\n\nThe primary issue was that `eventEmitter` was not defined or properly exported in your `services/api.js`. By setting up an event emitter using a library like `mitt` and ensuring it's correctly exported and imported, you should resolve the `TypeError` and have a functional event-based logout mechanism in your React Native application.\n\nIf you continue to face issues, consider adding more detailed logging or using debugging tools to trace the values and ensure that all modules are correctly set up.","behavior":"degenerate_repetition","conversation_hash":"14ece5d438e4b512c026c47318c3e621","conversation_redacted":false,"conversation_toxic":false,"country":"Canada","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":10617,"n_turns_in_conversation":1,"n_words":1331,"source_row_offset":9226,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:44 GMT","turn_id":"575081cd055c8788fb147d66","turn_index":0,"user_text":"// frontend/context/AuthContext.js\n\nimport React, { createContext, useState, useEffect } from 'react';\nimport * as SecureStore from 'expo-secure-store';\nimport { Alert } from 'react-native';\nimport jwtDecode from 'jwt-decode';\nimport { api, eventEmitter } from '../services/api'; // Import the Axios instance and EventEmitter\n\nexport const AuthContext = createContext();\n\n/**\n * AuthProvider Component\n * Provides authentication context to the application.\n */\nexport const AuthProvider = ({ children }) => {\n  const [authToken, setAuthToken] = useState(null);\n  const [user, setUser] = useState(null); // Manage user object\n  const [loading, setLoading] = useState(true); // Loading state to manage asynchronous operations\n\n  const SIGNIN_KEY = 'authToken'; // Consistent key\n\n  /**\n   * Function to handle user sign-in\n   * @param {String} token - JWT token received from the backend\n   */\n  const signIn = async (token) => {\n    try {\n      if (token && token.split('.').length === 3) { // Basic JWT structure validation\n        setAuthToken(token);\n        await SecureStore.setItemAsync(SIGNIN_KEY, token);\n        console.log('Token stored successfully:', token); // Debugging\n\n        // Decode the token to extract user information\n        const decoded = jwtDecode(token);\n        console.log('Decoded Token:', decoded); // Verify decoded data\n\n        // Adjust according to your token payload\n        setUser({\n          id: decoded.userId || decoded.id, // Adjust based on your token's structure\n          name: decoded.name || null,\n          email: decoded.email || null,\n        });\n      } else {\n        throw new Error('Invalid JWT token');\n      }\n    } catch (error) {\n      console.error('Error during sign-in:', error);\n      Alert.alert('Authentication Error', error.message || 'Failed to authenticate.');\n    }\n  };\n\n  /**\n   * Function to handle user sign-out\n   */\n  const signOut = async () => {\n    try {\n      setAuthToken(null);\n      setUser(null);\n      await SecureStore.deleteItemAsync(SIGNIN_KEY);\n      console.log('Token removed successfully.');\n\n      // Optionally, navigate to the login screen or reset navigation stack\n      // e.g., navigation.reset({ index: 0, routes: [{ name: 'Login' }] });\n    } catch (error) {\n      console.error('Error during sign-out:', error);\n      Alert.alert('Error', 'Failed to log out.');\n    }\n  };\n\n  /**\n   * Function to load the token from SecureStore on app start\n   */\n  const loadToken = async () => {\n    try {\n      const token = await SecureStore.getItemAsync(SIGNIN_KEY);\n      if (token && token.split('.').length === 3) { // Basic JWT structure validation\n        setAuthToken(token);\n        console.log('Token loaded from SecureStore:', token); // Debugging\n\n        // Decode the token to extract user information\n        const decoded = jwtDecode(token);\n        console.log('Decoded Token:', decoded); // Verify decoded data\n\n        setUser({\n          id: decoded.userId || decoded.id,\n          name: decoded.name || null,\n          email: decoded.email || null,\n        });\n      } else if (token) {\n        console.warn('Stored token is invalid. Clearing it.');\n        await SecureStore.deleteItemAsync(SIGNIN_KEY);\n      } else {\n        console.log('No token found.');\n      }\n    } catch (e) {\n      console.log('Failed to load token:', e);\n      Alert.alert('Loading Error', 'Failed to load authentication token.');\n    } finally {\n      setLoading(false);\n    }\n  };\n\n  useEffect(() => {\n    loadToken();\n\n    // Listen for logout events emitted by api.js\n    const handleLogout = () => {\n      signOut();\n      // Optionally, navigate to the login screen if navigation is accessible here\n      // e.g., navigation.reset({ index: 0, routes: [{ name: 'Login' }] });\n    };\n\n    eventEmitter.on('logout', handleLogout);\n\n    // Clean up the listener on unmount\n    return () => {\n      eventEmitter.off('logout', handleLogout);\n    };\n  }, []);\n\n  return (\n    <AuthContext.Provider value={{ authToken, user, loading, signIn, signOut }}>\n      {!loading && children}\n    </AuthContext.Provider>\n  );\n};\n\n\n ERROR  TypeError: Cannot read property 'on' of undefined\n\nThis error is located at:\n    in AuthProvider (created by App)\n    in App (created by withDevTools(App))\n    in withDevTools(App)\n    in RCTView (created by View)\n    in View (created by AppContainer)\n    in RCTView (created by View)\n    in View (created by AppContainer)\n    in AppContainer\n    in main(RootComponent), js engine: hermes\n ERROR  TypeError: Cannot read property 'on' of undefined\n\nThis error is located at:\n    in AuthProvider (created by App)\n    in App (created by withDevTools(App))\n    in withDevTools(App)\n    in RCTView (created by View)\n    in View (created by AppContainer)\n    in RCTView (created by View)\n    in View (created by AppContainer)\n    in AppContainer\n    in main(RootComponent), js engine: hermes\n"},{"assistant_initiates":false,"asst_text":"The intention behind the GitHub issue \"[FR] support res fonts on android\" relates to an \"enhancement.\" This is because \"FR\" typically stands for \"Feature Request,\" indicating a desire for a new feature, which aligns with the enhancement category.","behavior":"degenerate_repetition","conversation_hash":"c76ec82342883be364b6604fc45f8e29","conversation_redacted":false,"conversation_toxic":false,"country":"Australia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1016,"n_turns_in_conversation":1,"n_words":153,"source_row_offset":9227,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:46 GMT","turn_id":"63650e1d8d67c59d02837022","turn_index":0,"user_text":"Assistant: \nUser: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nUser: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nDoes the author's intention in this GitHub issue relate to a bug or enhancement or other?\nissue: [FR] support res fonts on android.'\nintention: '\nAssistant:\nAssistant:"},{"assistant_initiates":false,"asst_text":"The intention of this GitHub issue titled \"[Bug]: where is termux-notifcation, api package is installed\" relates to a bug. The user indicates an unexpected problem or unintended behavior by mentioning a missing component or functionality (termux-notification) despite the package being installed. Therefore, the issue falls under the \"bug\" category.","behavior":"degenerate_repetition","conversation_hash":"c344e0a68dfe12818107ae58bcdbb841","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1146,"n_turns_in_conversation":1,"n_words":167,"source_row_offset":9228,"source_shard":"turns-00032.parquet","timestamp":"Tue, 08 Oct 2024 18:04:52 GMT","turn_id":"592e6d56b280ab6f53b163c7","turn_index":0,"user_text":"Assistant: \nUser: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nUser: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:\nbug: indicates an unexpected problem or unintended behavior\nenhancement: Indicates new feature requests\nother: Other categories\nDoes the author's intention in this GitHub issue relate to a bug or enhancement or other?\nissue: [Bug]: where is termux-notifcation , api package is installed'\nintention: '\nAssistant:\nAssistant:"}],"shard":{"index":32,"name":"turns-00032.parquet","rows":52958}}