# embeddings_queue

created_at: 2025-01-30 07:37:37
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/sort_auto_mappings.py:49-98
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/sort_auto_mappings.py", "start_line": 49, "end_line": 98, "type": "function", "name": "sort_auto_mapping", "docstring": "", "chroma:document": "**Function Summary: `sort_auto_mapping`**\n\nThe `sort_auto_mapping` function sorts the auto-mappings in a given file.\n\n### Purpose and Functionality:\n\nAutomatically rearranges indented lines containing mappings in the specified file to adhere to a specific format. Compares and returns whether the original formatting is preserved upon sorting (when `overwrite=False`) or if changes were made for overwriting (`overwrite=True`).\n\n### Key Implementation Details:\n\n1.  **Regex-based Pattern Matching**:\n    *   `_re_intro_mapping.search()`: Identifies the start of a new mapping in lines.\n2.  **Blocks and Lines Processing**:\n    *   `indentation calculation`: Calculates indentation length before processing each mapping block.\n    *   `block decomposition` (`if lines[line_idx].strip() == \"(\":`): Tackles blocks divided across multiple lines with indentation matching the first line's formatting.\n3.  **Sorting Algorithm**:\n    *   `blocks sorted by ids` (`sorted(blocks, key=lambda x: _re_identifier.search(x).groups()[0])`): Orders and groups mappings according to their unique identifiers.\n\n### Notable Dependencies or Requirements:\n\n1.  `re` module (regular expressions) for pattern matching.\n2.  External file operations via `open` function with UTF-8 encoding.\n\n**API Reference:**\n\n*   `_re_intro_mapping`, `_re_identifier`: Externally defined regular expression patterns used within the sorting logic, not part of this summary but crucial for implementation context.\n\n### Example Usage:\n```python\nresult = sort_auto_mapping('path/to/file.txt')\nprint(result)  # Returns 'None' if overwrite is False and file has been properly sorted, False otherwise.\n```"}


created_at: 2025-01-30 07:37:38
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/sort_auto_mappings.py:101-116
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/sort_auto_mappings.py", "start_line": 101, "end_line": 116, "type": "function", "name": "sort_all_auto_mappings", "docstring": "", "chroma:document": "**Summary**\n\nFunction `sort_all_auto_mappings`: Sorts all auto mappings in the library, fixing inconsistencies if required. \n\n### Purpose\nSynchronizes Python file auto mappings within a module.\n\n### Implementation Details\n\n- Fetches relevant files using list comprehension with path manipulation (`os.path.join`).\n- Iterates through each file, calls `sort_auto_mapping` to sort its mappings.\n- Checks for non-overwritten changes after sorting; raises an error if updates are detected."}


created_at: 2025-01-30 07:37:39
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_tokenization_fast.py:5-7
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_tokenization_fast.py", "start_line": 5, "end_line": 7, "type": "class", "name": "CustomTokenizerFast", "docstring": "", "chroma:document": "**Summary:**\n\nThis class `CustomTokenizerFast` inherits from `BertTokenizerFast`. It overrides the default tokenizer by specifying its own tokenizer class as `SlowTokenizer`. The primary purpose is to enable the use of a custom, slow tokenization mechanism in place of the FastTokenizer.\n\n**Key Implementation Details:**\n\n* Inherited Class: `BertTokenizerFast`\n* Implemented Class: `CustomTokenizer` (specifically assigned as `slow_tokenizer_class`)\n* No additional functionality or method implementations provided; primarily serves as an override for the default tokenizer configuration."}


created_at: 2025-01-30 07:37:40
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_tokenization.py:3-4
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_tokenization.py", "start_line": 3, "end_line": 4, "type": "class", "name": "CustomTokenizer", "docstring": "", "chroma:document": "**Summary**\n\nClass: `CustomTokenizer`\nInherits from: `BertTokenizer`\n\n**Purpose and Functionality**: Creates a custom version of the BERT tokenizer.\n\n**Key Implementation Details**:\n\n* No specific tokenization algorithms or patterns are defined, inheriting functionality from `BertTokenizer`.\n* No method signatures or interfaces are overridden by default.\n \n\n**Notable Dependencies/Requirements**:\n* Inherits from: `BertTokenizer`\n* External dependencies not specified.\n\nNo key implementation details or interface implementations to note."}


created_at: 2025-01-30 07:37:42
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_modeling.py:7-18
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_modeling.py", "start_line": 7, "end_line": 18, "type": "class", "name": "CustomModel", "docstring": "", "chroma:document": "**Summary:**\n\nThe `CustomModel` class is a custom PyTorch model derived from `PreTrainedModel`. It uses the `CustomConfig` configuration.\n\n**Main Purpose and Functionality:**\nIt defines a basic neural network architecture with one linear layer, where the input `x` passes through this layer to produce an output.\n\n**Key Implementation Details:**\n\n*   A linear layer (`self.linear`) with input/ouput size of `config.hidden_size`.\n*   The `forward` method applies the linear transformation to the input `x`.\n\n**Notable Dependencies and Requirements:**\n\n*   Import from PyTorch (`torch.nn.Linear`).\n*   Inheritance from `PreTrainedModel`."}


created_at: 2025-01-30 07:37:43
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_modeling.py:21-32
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_modeling.py", "start_line": 21, "end_line": 32, "type": "class", "name": "NoSuperInitModel", "docstring": "", "chroma:document": "**Summary:**\n\nThe `NoSuperInitModel` class is a PyTorch-based pre-trained model that extends the `PreTrainedModel` class. It defines:\n\n* A linear layer with a configuration attribute `config.attribute`.\n* A forward pass through the linear layer, mapping input `x` to its output.\n* An `_init_weights` method that sets no weights (implies super-init behavior).\n\n**Key Details:**\n\n* **Linear Layer**: `self.linear = torch.nn.Linear(config.attribute, config.attribute)`\n* **Forward Pass**: `return self.linear(x)`\n* **_init_weights Method**: Implicitly inherits from superclass's behavior\n\n**Dependencies/Requirements:**\n\n* PyTorch (`torch`)\n* `PreTrainedModel` class (inherited)\n* `NoSuperInitConfig` configuration class ( referenced)"}


created_at: 2025-01-30 07:37:44
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_image_processing.py:3-4
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_image_processing.py", "start_line": 3, "end_line": 4, "type": "class", "name": "CustomImageProcessor", "docstring": "", "chroma:document": "**Code Summary:**\n\n*   Class Name: `CustomImageProcessor`\n*   Inheritance: Extends `CLIPImageProcessor` to create a custom image processor.\n\n**Additional Details:**\n\n*   No specific implementation details are provided, as the class solely inherits from `CLIPImageProcessor`."}


created_at: 2025-01-30 07:37:46
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_pipeline.py:5-8
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_pipeline.py", "start_line": 5, "end_line": 8, "type": "function", "name": "softmax", "docstring": "", "chroma:document": "**Concise Summary**\n\nThis function implements the softmax activation function. It takes an input array `outputs` of shape `(num_classes, batch_size)` and returns a normalized value for each class.\n\nThe primary purpose is to prevent output values from growing exponentially large by normalizing them over their maximum value across classes.\n\n**Key Implementation Details:**\n\n- Uses NumPy's vectorized operations to calculate the softmax output.\n- Employs shift-invariant exponentiation (`outputs - maxes`) to mitigate numerical instability issues.\n\n**Notable Dependencies and Requirements:**\n\n* `numpy` (imported as `np` for array operations)\n* `outputs` input should be an array of shape `(num_classes, batch_size)` containing output values."}


created_at: 2025-01-30 07:37:48
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_pipeline.py:11-32
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_pipeline.py", "start_line": 11, "end_line": 32, "type": "class", "name": "PairClassificationPipeline", "docstring": "", "chroma:document": "**PairClassificationPipeline Class Summary**\n\n* **Purpose:** This class implements a pipeline for pair classification tasks using a pre-trained model. It sanitizes input parameters, preprocesses text inputs with optional second text, and postprocesses model outputs to extract confidence scores and predicted labels.\n\n* **Key Implementation Details:**\n\t+ `preprocess` method takes in text inputs and an optional second text, and uses the `tokenizer` to generate input tensors in a specific framework.\n\t+ `_forward` method passes the preprocessed input to the trained model for inference.\n\t+ `postprocess` method extracts attention scores from the model output, converts them to probabilities using softmax, and determines the predicted label by finding the class with the highest probability.\n\n* **Notable Dependencies:**\n\t+ `tokenizer`: a tokenizer object used in the `preprocess` method\n\t+ `model`: a pre-trained model instance that takes in input tensors in the same framework as the pipeline\n\t+ `self.framework`: the specific framework used for tensor generation (e.g. PyTorch, TensorFlow)\n\t+ `softmax`, `logits`, and related functions: commonly used mathematical functions for probability calculations"}


created_at: 2025-01-30 07:37:49
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_configuration.py:3-8
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_configuration.py", "start_line": 3, "end_line": 8, "type": "class", "name": "CustomConfig", "docstring": "", "chroma:document": "**Summary**\n\n`CustomConfig` is a custom configuration class for the `PretrainedConfig` base class. It defines a model type as \"custom\" and extends initialization from its parent with an additional `attribute` parameter, defaulting to 1.\n\n**Key Implementation Details:**\n\n* Customizes model type to `model_type = \"custom\"`\n* Extends __init__ from `PretrainedConfig` with `attribute` parameter\n\n**Notable Dependencies or Requirements:**\nNone explicitly listed. Inherited interfaces: `PretrainedConfig`."}


created_at: 2025-01-30 07:37:50
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_configuration.py:11-15
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_configuration.py", "start_line": 11, "end_line": 15, "type": "class", "name": "NoSuperInitConfig", "docstring": "", "chroma:document": "**Summary**\n\nThis `NoSuperInitConfig` class inherits from `PretrainedConfig`, defining a custom configuration for a specific model. The primary purpose is to initialize the configuration with an optional `attribute` parameter.\n\n**Key Details**\n\n* Inherited from: `PretrainedConfig`\n* Model type: \"custom\"\n* Initialization:\n\t+ Required attribute: `attribute=1` (default value)\n\n**Notable Dependencies/Requirements**\n\n* None explicitly listed, but assumes access to `PretrainedConfig` class and related dependencies."}


created_at: 2025-01-30 07:37:51
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_processing.py:3-5
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_processing.py", "start_line": 3, "end_line": 5, "type": "class", "name": "CustomProcessor", "docstring": "", "chroma:document": "**Summary:**\n\nThis `CustomProcessor` class inherits from `ProcessorMixin` and defines two key attributes:\n\n* `feature_extractor_class`: specifies the use of the `AutoFeatureExtractor` component.\n* `tokenizer_class`: specifies the use of the `AutoTokenizer` component.\n\nNo additional implementation details or dependencies are defined for this class."}


created_at: 2025-01-30 07:37:53
topic: persistent://default/default/1145e3af-1ef1-4153-8e3d-6bccee49659b
id: /home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_feature_extraction.py:3-4
encoding: FLOAT32
metadata:
{"file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/test_module/custom_feature_extraction.py", "start_line": 3, "end_line": 4, "type": "class", "name": "CustomFeatureExtractor", "docstring": "", "chroma:document": "**Summary**\n\nA custom `CustomFeatureExtractor` class that inherits from the `Wav2Vec2FeatureExtractor` class, effectively extending the functionality of the parent class with no additional implementation.\n\n**Key Implementation Details**\n\n* Inherited from `Wav2Vec2FeatureExtractor`, implying an extension to the Wav2Vec2 model's feature extraction capabilities.\n* No additional methods or attributes are defined, suggesting a focus on leveraging the existing `Wav2Vec2FeatureExtractor` functionality with no new additions.\n\n**Notable Dependencies**\n\n* `Wav2Vec2FeatureExtractor`: The parent class from which `CustomFeatureExtractor` inherits.\n\n**Additional Requirements**\n\nNone."}


# collections

id: 1145e3af-1ef1-4153-8e3d-6bccee49659b
name: code_chunks
database_id: 00000000-0000-0000-0000-000000000000
config_json_str:
{"hnsw_configuration": {"space": "l2", "ef_construction": 100, "ef_search": 100, "num_threads": 24, "M": 16, "resize_factor": 1.2, "batch_size": 100, "sync_threshold": 1000, "_type": "HNSWConfigurationInternal"}, "_type": "CollectionConfigurationInternal"}


# embeddings

segment_id: 0255a147-1d93-40e1-92e5-32a85d0b5255
created_at: 2025-01-30 05:13:50
embedding_id:
/home/gaby/projects/2025/transformers-4.48.1/src/transformers/image_processing_utils_fast.py:105-115


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embedding_id:
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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/image_processing_utils_fast.py:125-132


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created_at: 2025-01-30 05:14:33
embedding_id:
/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_flash_attention_utils.py:35-59


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_flash_attention_utils.py:62-132


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_flash_attention_utils.py:135-182


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created_at: 2025-01-30 05:17:32
embedding_id:
/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_flax_pytorch_utils.py:151-235


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_flax_pytorch_utils.py:243-324


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/feature_extraction_sequence_utils.py:29-371


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/convert_tf_hub_seq_to_seq_bert_to_pytorch.py:31-60


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py:37-98


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/modeling_gguf_pytorch_utils.py:129-165


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