# embeddings_queue

created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py:220-224

metadata:
{"docstring": "", "end_line": 224, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py", "name": "random_resized_crop", "start_line": 220, "type": "function", "chroma:document": "**Summary:**\n\nThis function (`random_resized_crop`) generates a randomized cropped image with a specified `size`, while maintaining its aspect ratio.\n\n**Key Details:**\n\n* Resizes an input `image` to a random size within the specified `scale` and `ratio`.\n* Utilizes TensorFlow's `tf.image.resize` to resize the image.\n\n**Implementation Parameters:**\n\n* `size`: The target size of the output image (int or tuple).\n* `scale`: A scaling factor for randomly resizing the image (tuple).\n* `ratio`: The aspect ratio constraint for the resized image (tuple).\n* `random_crop`: Utilized function from TensorFlow for cropping (not shown in this code snippet).\n\n**Notable Dependencies:**\n\n* TensorFlow (`tf`): For image processing and randomization utilities.\n* `random_crop`: A custom or external function implemented by the library."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py:227-572

metadata:
{"docstring": "", "end_line": 572, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py", "name": "main", "start_line": 227, "type": "function", "chroma:document": "This is a Python script that trains a Hugging Face Transformers model on an image classification task. Here's a concise summary:\n\n1. The script sets up the training environment, including the model, optimizer, and hyperparameters.\n2. It prepares the datasets for training and evaluation using `model.prepare_tf_dataset()`.\n3. The model is compiled with the specified loss function, optimizer, and metrics (accuracy).\n4. The model is trained on the training dataset, evaluated on the validation dataset, and predicts on the test dataset.\n5. After training, the model's performance is logged, and results are saved to a JSON file if desired.\n\nKey features:\n\n* Uses Hugging Face Transformers for image classification\n* Supports optional evaluation, prediction, and saving modes\n* Allows for hyperparameter tuning and output directory customization\n\nNote that this script assumes you have a basic understanding of PyTorch and Keras, as well as the Hugging Face Transformers library."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/run_benchmark_tf.py:21-43

metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/run_benchmark_tf.py", "name": "main", "start_line": 21, "type": "function", "chroma:document": "**Function Summary**\n\n*   **Purpose**: The main function `main()` parses command-line arguments, creates a TensorFlowBenchmark instance, and runs the benchmark.\n*   **Functionality**: It utilizes an `HfArgumentParser` to parse arguments into dataclasses, handles deprecated argument warnings, and executes the benchmark using the `TensorFlowBenchmark` class.\n\n**Key Implementation Details**\n\n*   Uses an `HfArgumentParser` with `TensorFlowBenchmarkArguments` to parse command-line arguments.\n*   Handles deprecated arguments raised by Python's `eval()` function on a string of deprecated options.\n*   Includes error handling for invalid argument values, raising a `ValueError` with a formatted error message.\n\n**Notable Dependencies and Requirements**\n\n*   `HfArgumentParser`: A class from the Hugging Face Transformers library used to parse command-line arguments into dataclasses.\n*   `TensorFlowBenchmarkArguments`: A custom class or interface extended from `arguments.BaseArguments`.\n*   `TensorFlowBenchmark`: The benchmarking class executed after parsing arguments.\n*   `python's eval() function`: Used for parsing deprecated argument names.\n\n**Notes**: This code assumes the availability of specific classes and interfaces (`HfArgumentParser`, `TensorFlowBenchmarkArguments`, `TensorflowBenchmark`) in the project, which may require external libraries or custom definitions."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py:26-27

metadata:
{"docstring": "", "end_line": 27, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py", "name": "list_field", "start_line": 26, "type": "function", "chroma:document": "**Summary**\n\nConcats `field` function with factory method creating an instance of the same type as `default`, injecting `metadata`. This function acts as a shortcut to create and configure custom fields.\n\n**Key Implementation Details:**\n\n- Utilizes `field` function provided by `django-felix` library.\n- Employs `factory_method` to dynamically create an instance based on the `default` value.\n- Passes `metadata` directly to the parent `field()` method for configuration.\n\n**Notable Dependencies/Requirements:**\n\n- Requires `django-felix` library, referencing `Field`."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py:66-71

metadata:
{"docstring": "", "end_line": 71, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py", "name": "can_convert_to_int", "start_line": 66, "type": "function", "chroma:document": "**Summary:**\n\nThe `can_convert_to_int` function takes a string as input and attempts to convert it to an integer using the built-in `int()` function. If successful, it returns `True`. Otherwise, it catches the `ValueError` exception and returns `False`.\n\n* Main purpose: Determine if a given string can be converted to an integer.\n* Key implementation detail: Conversion with try-except block for handling potential `ValueError`.\n* Notable dependency/interface: Built-in Python data type `int()`.\n\n**RAG Code Summary:** \n\n`can_convert_to_int(str string) : bool`\n\n`def can_convert_to_int(string):`\n  `try: int(string)`\n`except ValueError: return False`\n`return True`"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py:74-79

metadata:
{"docstring": "", "end_line": 79, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py", "name": "can_convert_to_float", "start_line": 74, "type": "function", "chroma:document": "**Summary:**\n\n```markdown\n**Function:** `can_convert_to_float`\n**Purpose:** Checks if a given string can be converted to a Float.\n\n* Attempts to convert the input string to a float using `float()`.\n* Returns True (representing successful conversion) or False on failure.\n\n## Key Implementation Details\n\n* Uses `try-except` block to handle `ValueError` exceptions raised during floating-point conversion.\n\n## Notable Dependencies/Requirements**\n\n* None. This function does not import any external modules or classes.\n```\n\n**RAG (Required Action Guide) Tags:**\n\n* Primary Purpose: Convert input string to float\n* Key Implementation Details: Floating-point conversion attempt and `try-except` block handling\n* Notable Dependencies: None"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py:82-166

metadata:
{"docstring": "", "end_line": 166, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py", "name": "Plot", "start_line": 82, "type": "class", "chroma:document": "**Summary:**\n\nThe `Plot` class is designed to visualize training and inference time(memory) usage patterns of different models. It takes a CSV file containing model information as input.\n\n**Main Purpose and Functionality:**\n\n1. Load the specified CSV file, which contains model names and their corresponding training/inference statistics (e.g., batch size, sequence length, result values).\n2. Plot the result values for each model in a scatter plot, with x-axis representing one of the statistics (batch size or sequence length) and y-axis representing the other statistic and its result value.\n\n**Key Implementation Details:**\n\n1. The `plot` method creates two subplots if plotting along batch vs sequence length, and otherwise plots results as functions of one statistic.\n2. The scatter plot is generated using the seaborn `scatter` function, with line plots connecting each point for consistency.\n3. The plot includes labels, titles, and legends to explain the data points.\n\n**Notable Dependencies and Requirements:**\n\n1. The class uses the `defaultdict` from the `collections` module to efficiently group results by model name.\n2. It relies on the pandas library through CSV file reading (`csv.DictReader`).\n3. The plot generation utilizes the matplotlib library.\n4. Optional dependencies include seaborn for the scatter plot function and the plt.show() function for displaying plots, as well as PIL for saving PNG files.\n\n**Interfaces Implemented:**\n\n1. The `Plot` class adheres to the standard plotting APIs in matplotlib (e.g., `fig`, `ax`, `plt.scatter`, etc.) and supports additional options via the matplotlib rcParams (e.g., `'no_log_scale'`)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py:169-173

metadata:
{"docstring": "", "end_line": 173, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/benchmarking/plot_csv_file.py", "name": "main", "start_line": 169, "type": "function", "chroma:document": "**Summary:**\n\n`main()` function initializes and executes a plot generation pipeline. It uses an `HfArgumentParser` to parse command-line arguments, creates a `Plot` object with the parsed data, and calls its `plot()` method.\n\n**Key Details:**\n\n*   Main entry point for the application\n*   Uses `HfArgumentParser` to parse command-line arguments of type `PlotArguments`\n*   Creates and initializes a `Plot` object (`plot`) with a reference to the parsed argument data.\n*   Calls the `plot()` method on the `Plot` object.\n\n**Notable Dependencies/Requirements:**\n\n*   `HfArgumentParser`: A class for parsing command-line arguments\n*   `PlotArguments` : The type of arguments to be parsed by `HfArgumentParser`\n*   `Plot`: A class used for generating plots, with a method `plot()` that implements the actual plotting functionality."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py:75-91
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 91, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py", "name": "find_backend", "start_line": 75, "type": "function", "chroma:document": "**Summary**\n\nThis function, `find_backend`, extracts and aggregates backend names (`b[0]`) from a given code line. It uses regular expressions to search for matching patterns and then returns the found backends in sorted order, optionally joined with `_and_`.\n\n**Key Implementation Details**\n\n* Uses `_re_test_backend` and `_re_backend` regular expression objects to search and find backend names.\n* Returns `None` if no match is found.\n\n**Dependencies**\n\n* `_re_test_backend`, `_re_backend`\n* `Optional[str]`\n\n**Return Value**\n\n* A string containing one or more backend names, separated by `_and_`. \n\n**Primary Functionality**\nFind backend names from a given code line in an init file."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py:94-136
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 136, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py", "name": "read_init", "start_line": 94, "type": "function", "chroma:document": "**Function Summary: `read_init`**\n\n* **Purpose and Functionality**: Extracts backend-specific objects from the `__init__.py` file of a Hugging Face transformer model, mapping each backend to its associated object names.\n* **Key Implementation Details**:\n\t+ Reads and parses the entire `__init__.py` file.\n\t+ Searches for `if is_backend_available` statements to identify backend-specific imports.\n\t+ Parses multiple lines using regular expressions to extract object names from import statements.\n* **Dependencies and Requirements**:\n\t+ Hugging Face transformer models (specifically, `PATH_TO_TRANSFORMERS`)\n\t+ `_re_single_line_import` regex pattern (internal function)\n\t+ `find_backend` function (imported but not explicitly shown in this code snippet)\n* **Interfaces/Inherited:** This function inherits from no specific interface.\n\n**Notes:**\n\n* The function returns a dictionary with the format `{backend_name: [object_names]}`.\n* The `_re_single_line_import` regex pattern is used to extract object names from single-line import statements."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py:139-155
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 155, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py", "name": "create_dummy_object", "start_line": 139, "type": "function", "chroma:document": "**Function Summary**\n\n`create_dummy_object`: Returns code for a dummy object of specified type based on input `name`. The type is determined by the casing of `name`.\n\n* **Purpose**: Generate code for a dummy object with given name and backend requirement.\n* **Implementation Details**:\n\t+ Uses format strings (`DUMMY_CONSTANT`, `DUMMY_FUNCTION`, `DUMMY_CLASS`) to generate code.\n\t+ Includes `backend_name` in generated code when `name` is not all upper case or lower case.\n* **Notable Dependencies**: Uses string formatting (`format`) and likely inherited from a parent class/inheritance hierarchy containing `DUMMY_CONSTANT`, `DUMMY_FUNCTION`, and `DUMMY_CLASS`. \n\n**Key Interfaces/Methods:**\n\n- `DUMMY_CONSTANT`\n- `DUMMY FUNCTION`\n- `DUMMY CLASS`\n\n**External Dependencies:** None"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py:158-182
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 182, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py", "name": "create_dummy_files", "start_line": 158, "type": "function", "chroma:document": "**Function Summary: create_dummy_files**\n\n**Purpose**: Creates the content of dummy files based on backend-specific objects.\n\n**Functionality**\n\n* Takes an optional dictionary `backend_specific_objects` mapping backend names to lists of backend-specific objects.\n* If none provided, reads them from `read_init()`.\n* Creates a dictionary `dummy_files` with backend names as keys and their corresponding code as values.\n* For each backend:\n\t+ Constructs the code by concatenating object imports and calls to `create_dummy_object` method.\n\t+ Prefers formatted output for list of backend objects.\n\n**Key Implementation Details**\n\n* Uses `read_init()` to retrieve backend-specific objects when not provided.\n* Employed string formatting to nicely display list of backend objects.\n\n**Notable Dependencies**\n\n* `read_init()`\n* `create_dummy_object()\n* `DummyObject` class from `..utils` module\n* External dependencies: requires `make` command and directory structure"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py:185-228
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 228, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_dummies.py", "name": "check_dummies", "start_line": 185, "type": "function", "chroma:document": "**Function Summary: check_dummies**\n\n**Purpose:** Verify the integrity of dummy files and optionally update their content.\n\n**Functionality:**\n\n1. Checks if dummy file paths exist and reads their contents.\n2. Compares actual contents with expected ones. If updated, updates the file.\n3. Raises `ValueError` if dummy files are outdated (non-present) when `overwrite=False`.\n\n**Key Implementation Details:**\n\n* Uses `create_dummy_files()` to generate dictionary of dummy files.\n* Uses a mapping (`short_names`) for shortcutting backend names.\n\n**Notable Dependencies and Requirements:**\n\n* `utils/dummy_xxx_objects.py` file\n* `os` module (for interacting with the file system)\n* `pathlib` (not explicitly mentioned, but implied by `PATH_TO_TRANSFORMERS`)\n* `make` command (to run `fix-copies` to update outdated dummy files)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:46-54
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 54, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_module_source_from_name", "start_line": 46, "type": "function", "chroma:document": "**Summary**\n\n`get_module_source_from_name`: Extracts(source code) from a given Python module name (`module_name`). \n\n### Key Details\n\n* Uses `importlib.util.find_spec` to locate the module spec.\n* Returns the read contents of the module's associated file.\n\n### Implementation Notes\n\n* Checks for existence and validity of the module spec before proceeding.\n* Raises a `ValueError` if the file associated with the module is not found or cannot be opened.\n* reads the source code from the file using UTF-8 encoding.\n\n### Dependencies and Interfaces\n\n* `importlib.util`: Module to locate Python modules.\n* `python files/utf-8 encoding (`encoding=\"utf-8\"`): Specifies character encoding for reading the file."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:57-76
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 76, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "preserve_case_replace", "start_line": 57, "type": "function", "chroma:document": "**Function Summary: preserve_case_replace**\n\nPreserves sentence case in a given `text` by replacing matched patterns with corresponding names from the `patterns` dictionary. The function uses a regex pattern to match all variations of model names.\n\n**Key Functionality:**\n\n*   Preserves original text while replacing matched patterns.\n*   Replaces patterns based on the order and key-value pairs in `patterns`.\n*   Uses case-insensitive matching for non-alphanumeric characters.\n\n**Important Implementation Details:**\n\n*   Regex pattern `(?<![a-z0-9])({regex_pattern})(.|$)` matches all variations of model names while preserving sentence case.\n*   The `replace` function uses the matched pattern, next character, and corresponding name from `patterns` to replace the original text.\n\n**Notable Dependencies:**\n\n*   `re` module for regex operations.\n*   Python's built-in string methods (e.g., `isalpha()`, `upper()`).\n\n**External Dependencies:**\n\n*   None specified in the provided code chunk. However, it can be assumed that any library or framework being used by the target project may also require this function to function appropriately."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:79-84
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 84, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_cased_name", "start_line": 79, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `get_cased_name`, takes a lowercase model name as input and returns the cased version. If the input matches a predefined mapping, it replaces \"Config\" with an empty string; otherwise, it applies title case to the split and joined components.\n\n**Key Implementation Details:**\n\n* Replaces \"Config\" for matched mappings\n* Applies title case using `str.title()` for unmatched inputs\n\n**Dependencies/Interfaces:**\n\n* `CONFIG_MAPPING_NAMES`: a dictionary-like data structure (not defined in this code snippet)\n\n**Notable Method Signatures:**\n\n* `get_cased_name(lowercase_name: str) -> str`"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:87-93
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 93, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_lowercase_name", "start_line": 87, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_lowercase_name`\n\n* **Purpose:** Convert a Camelcase name to lowercase and format according to the ` CONFIG_MAPPING_NAMES`.\n* **Functionality:** \n\t+ Checks if a Config variant exists in the inverse mapping for the given Camelcase name.\n\t+ If not, returns the lowered Camelcase name by splitting into words and joining with underscores.\n\n**Key Implementation Details:**\n\n* Uses regular expressions (`re.findall`) to extract words from the Camelcase input string.\n* Applies `str.lower()` to each word before rejoining them with underscores.\n\n**Notable Dependencies or Requirements:**\n\n* `CONFIG_MAPPING_NAMES` (injected as a constant)\n* `re` (regular expression module)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:96-146
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 146, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ReplaceNameTransformer", "start_line": 96, "type": "class", "chroma:document": "**Summary**\n\nThe `ReplaceNameTransformer` class is designed to replace specific names in comments, strings, and references. It handles various naming patterns, considering case sensitivity and prefixes/qualifiers.\n\n**Key Functionality:**\n\n1.  Replaces `old_name` with `new_name` in:\n    *   Comments\n    *   Strings\n    *   References\n\n2.  Supports different renaming patterns:\n\n    *   `llama -> my_new_model`\n    *   `Llama -> MyNewModel`\n    *   `LLAMA -> MY_NEW_MODEL`\n    *   `(prefix)_LLaMa_(suffix)`\n\n**Implementation Details:**\n\n*   `_replace_name` method applies the replacement rules to an updated node.\n\n*   The transformer considers the case of the original name and uses a `patterns` dictionary for replacements.\n*   It preserves the original meaning by allowing certain suffixes (like `.py`) to remain unchanged.\n\n**Dependencies/Notable Requirements:**\n\n*   Uses Python's `re` module for regular expressions\n*   Leverages the `cst` library for parsing nodes\n\nHere's a concise summary of key classes and functions:\n\n*   `class ReplaceNameTransformer(m.MatcherDecoratableTransformer):`\n\n    *   Initializes with `old_name`, `new_name`, optional `original_new_model_name`, and `only_doc`.\n    *   Provides `replace_name`, `leave_Name` and `leave_ImportFrom` methods to apply the transformer transformations\n\nThe full implementation showcases how this transformer can be used to standardize naming conventions within a larger system."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:161-162
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 162, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "SUPER_CALL_NODE", "start_line": 161, "type": "function", "chroma:document": "**Summary:**\n\nThe `SUPER_CALL_NODE` function generates an abstract syntax tree (AST) node for the `super()` call in Python.\n\n**Key Implementation Details:**\n\n* The `m.Call` node represents the call operation with its attributes.\n* The `attr` attribute within the `Call` node specifies the `func_name` parameter, which is passed from the caller function.\n* A nested `m.Call` node represents the built-in `super()` function and has a specific structure, calling another inner `m.Name` node named `\"super\"`.\n* Both nodes rely on another higher-level node `m.Attribute`, indicating method calls.\n\n**Notable Dependencies/Requirements:**\n\n* The function uses the `ast` module (implied but not shown) to create an abstract syntax tree.\n* It relies on a meta-programming system, possibly a Python parser generator like Parsec or PyParsing."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:165-168
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 168, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "is_call_to_super", "start_line": 165, "type": "function", "chroma:document": "**Summary:**\n\n`is_call_to_super` function checks if the given `node` represents a call to the superclass (__Super__) from a method named `func_name`. \n\n**Key Implementation Details:**\n- Uses regular expression (`m.matches`) to match a specific pattern in the node.\n- Expects a match with either a return statement calling `SUPER_CALL_NODE(func_name)` or an expression also calling `SUPER_CALL_NODE(func_name)`.\n\n**Notable Dependencies/Requirements:**\n\n- `SuperCallNode` is assumed to be a previously defined class or function.\n- External dependency on regular expression engine (`m`) using `m.matches`. \n\nNote: Without additional context, the exact nature of `SUPER_CALL_NODE(func_name)`, `m`, and related classes/functions cannot be precisely determined."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:171-189
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 189, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_full_attribute_name", "start_line": 171, "type": "function", "chroma:document": "**Summary**\n\n`get_full_attribute_name` function extracts the full attribute name from an AST node (either `Attribute` or `Name`). It traverses attributes and checks if successive values are `Name` nodes. If successful, it returns the fully qualified name.\n\n**Key Details**\n\n* Function takes an `AST` node (`cst.Attribute | cst.Name`) as input\n* Returns a string representing the full attribute name or `None` if incomplete\n* Uses a regular expression (m) to match node types\n\n**Implementation Choices**\n\n* Uses recursive descent approach for traversing attributes\n* Employed a try-and-contract approach to handle cases with incomplete successive values"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:193-243
encoding: FLOAT32
metadata:
{"docstring": "# Transformer class to replace ClassB.call_to_method and ClassB().call_to_method with super().call_to_method", "end_line": 243, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ReplaceMethodCallTransformer", "start_line": 193, "type": "class", "chroma:document": "**Class Summary**\n\n`ReplaceMethodCallTransformer` is a CST transformer that replaces `ClassB.call_to_method` and `ClassB().call_to_method` with `super().call_to_method`.\n\n**Primary Purpose**\n\nThe main purpose of this class is to transform Python code by replacing method calls in the specified formats with the equivalent Super class call.\n\n**Key Implementation Details**\n\n1. **leave_Attribute**: This function handles case where attribute name is present as a string (`ClassB.call_to_method`). It checks if the attribute name is in `all_bases` and replaces it with `super().call_to_method`.\n2. **leave_Call**: This function handles cases where the method call contains an instance of `(module)` or only the class method call (e.g. `ClassB.func_a`). It checks for the presence of 'self' as an argument, removes it if present and updates the arguments of the function call.\n\n**Notable Dependencies/Requirements**\n\n* `cst` module for working with Python abstract syntax trees (ASTs)\n* `Set[str]` for storing class names\n* `get_full_attribute_name` and `m.matches` functions appear to be custom utility functions not explicitly included in the provided code."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:246-252
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 252, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_docstring_indent", "start_line": 246, "type": "function", "chroma:document": "**Function Summary**\n\n```\nget_docstring_indent function\n=============================\n\nReturns the number of indentation spaces preceding the first line after triple quotes in a docstring.\n\n### Purpose\n\nExtracts and returns the indentation level from the matched opening docstring triplet.\n\n### Implementation Details\n\n- Uses regular expression to match the opening triplet and capture the following whitespace content.\n- Returns the length of the captured whitespace as the indentation distance.\n\n### Dependency\n\nNone\n```"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:255-281
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 281, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "merge_docstrings", "start_line": 255, "type": "function", "chroma:document": "**Summary**\n\nThe `merge_docstrings` function merges two docstrings, aiming to blend the style of both into a single cohesive doc string. Specifically:\n\n### Main Purpose and Functionality\n\n* Merges two doc strings (`original_docstring` and `updated_docstring`) into one.\n* Preserves indentation structure from `original_docstring`.\n* Updates `Args:` section with new content from `updated_docstring`.\n\n### Key Implementation Details\n\n* Uses regular expression to detect and replace `Args:` format in `updated_docstring`.\n* Corrects inconsistent indentation by tabulation.\n* Handles different cases of doc string formatting, including single-step and multi-step examples.\n\n### Notable Dependencies or Requirements\n\n* Utilizes string manipulation functions (e.g., `rstrip`, `lstrip`, `split`) and regular expressions (e.g., `re.findall`, `re.search`).\n* Assumes the use of triple quoting (`\"\"\"`) for defining docstrings.\n* Expected input: Two doc strings, with an optional third example section using triple quotes."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:284-427
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 427, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "SuperTransformer", "start_line": 284, "type": "class", "chroma:document": "**Summary:**\n\nThis class `SuperTransformer` is designed to transform Python code that references `super()` calls. It achieves this by:\n\n*   **Replacing `super()` calls**: Replaces calls to `super()` with the source code of the parent class's method implementation.\n*   **De-duplicating docstrings**: Merges overlapping docstrings from different sources in a unified way.\n\n**Key Implementation Details:**\n\n1.  **Main Functionality**: `replace_super_calls` function\n    *   Updates the body of a function node by replacing calls to `super()` with the source code of the parent class's method implementation.\n2.  **Docstring Handling**\n    *   Uses separate functions `_fix_post_init_location` and `_fix_init_location` to handle docstrings in different locations (at the end, or at the top).\n3.  **Visitor Pattern**: Utilizes a visitor pattern (`visit`) to transform AST nodes.\n\n**Notable Dependencies:**\n\n1.  `ParentNodeProvider`\n2.  `m` (a module for parsing and manipulating code)\n3.  `DOCSTRING_NODE` (a node representing docstrings in the AST)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:430-514
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 514, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "find_all_dependencies", "start_line": 430, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\n**Function Purpose and Functionality**\n\nThis function performs a breadth-first traversal (BFS) on a dependency graph, where each entity represents a function or assignment name. It finds all dependencies for a given `start_entity` or a set of initial dependencies.\n\n**Main Implementation Details**\n\n* The function uses a queue to efficiently traverse the graph.\n* It keeps track of visited entities and their dependencies using sets.\n* If `return_parent=True`, it returns a list of tuples (dependency, parent) instead of just a set of dependencies.\n\n**Key Method Signatures and Interfaces**\n\n* `find_all_dependencies(Dict[str, set], str | None = None, ..., return_parent: bool = False -> list | set)`\n* `deque()`\n* `set()`\n\n**Notable Dependencies or Requirements**\n\n* `dependency_mapping`: a dictionary mapping entities to their dependencies.\n* `start_entity`, `initial_dependencies`, and `return_parent` are optional parameters used to customize the traversal.\n\n**Example Use Case**\n\nGiven a `modular_xxx.py` file with function definitions, this function returns all dependencies needed for a given entity or initial set of dependencies."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:523-545
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 545, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ClassDependencyMapper", "start_line": 523, "type": "class", "chroma:document": "**ClassDependencyMapper Summary**\n\nThe `ClassDependencyMapper` class is a custom visitor for analyzing class nodes to identify shared dependencies with `global_names`. It primarily maps shared node values to set of dependencies.\n\n**Key Implementation Details:**\n\n* Visits each name node in the class definition.\n* Checks if name value matches global names (excluding imported from modeling) and adds it to the dependencies set.\n\n**Notable Dependencies or Requirements:**\n\n* `global_names`: a set of global names used for dependency analysis.\n* `objects_imported_from_modeling`: an optional set of objects imported from modeling, defaulting to an empty set if not provided.\n* `_CSTVisitor`: the base visitor class inherited by this class.\n\nNote that external dependencies are not explicitly mentioned in this code snippet."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:548-553
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 553, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "dependencies_for_class_node", "start_line": 548, "type": "function", "chroma:document": "**Summary:**\n\nCreates immediate dependencies for a class node based on `global_names`.\n\n**Main Purpose and Functionality:** \nThe function `dependencies_for_class_node` takes a `ClassDef` node and a set of global names, and returns the dependencies required by that class.\n\n**Key Implementation Details:**\n\n- Creates a temporary module with the input class node\n- Instantiates a `ClassDependencyMapper` visitor with the class name and global names\n- Visits the temporary module, allowing the visitor to inspect dependencies\n\n**Notable Dependencies or Requirements:**\n\n- `cst` (Abstract Syntax Tree) module for parsing\n- `ClassDependencyMapper` visitor for dependency resolution\n- `global_names` set of imported names for dependency filtering"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:556-565
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 565, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "augmented_dependencies_for_class_node", "start_line": 556, "type": "function", "chroma:document": "**Summary:**\n\n`augmented_dependencies_for_class_node` function:\n Creates augmented dependencies for a class node by traversing its members and recursively visiting dependent modules.\n\n**Key Highlights:**\n\n* Takes in `cst.ClassDef`, `ModuleMapper`, and `objects_imported_from_modeling` parameters.\n* Visits the class node using a custom `ClassDependencyMapper` visitor, which collects dependencies.\n* Returns augmented dependencies via the ` augment_dependencies` method of the `ModuleMapper`.\n\n**Implementation Details:**\n\n* Creates a temporary module with the class node as its body.\n* Uses the `ClassDependencyMapper` to recursively traverse dependent modules.\n* Augments the collected dependencies using the `augment_dependencies` method."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:579-733
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 733, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ModuleMapper", "start_line": 579, "type": "class", "chroma:document": "**ModuleMapper Summary**\n\nThe `ModuleMapper` class is an abstract visitor that analyzes a Python module, creating a mapping of dependencies for classes, functions, and assignments. It computes dependencies using `compute_class_dependencies()` and `_compute_recursive_object_dependencies()`, storing them in `self.object_recursive_dependency_mapping`.\n\n**Main Purpose and Functionality:**\n\n* Analyzes a Python module to create a dependency mapping\n* Computes class dependencies using `compute_class_dependencies()`\n* Stores object dependencies using `_compute_recursive_object_dependencies()`\n* Augments dependencies using `augment_dependencies()` and `dependencies_for_class_node()`\n\n**Class Relationship Mapping:**\n\n* Records class nodes in `self.classes` for future dependencies\n* Maps functions and assignments to objects used inside them using `visit_Name()` and `visit_ClassDef()`\n* Updates `self.object_recursive_dependency_mapping` with recursive dependencies\n\n**Dependencies Computation:**\n\n* Computes absolute dependencies using `_compute_recursive_object_dependencies()`\n* Augments dependencies with potential dependencies of functions and assignments\n\n**Abstract Methods:**\n\n* `compute_relative_order(missing_dependencies: set)`: returns a dictionary mapping class names to their relative order for missing dependencies.\n\nNote: The code uses various internal classes, methods, and metadata wrappers that are not included in this summary."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:736-884
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 884, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ModelFileMapper", "start_line": 736, "type": "class", "chroma:document": "**ModelFileMapper Class Summary**\n\nThe `ModelFileMapper` class is designed to parse modeling files and merge their dependencies with those of the modular file.\n\n### Purpose\nTo correctly visit and merge dependencies between modules, ensuring that functions, assignments, and classes are properly updated and ordered relative to each other.\n\n### Key Implementation Details\n\n*   **`merge_modular_dependencies` method**: Merges classes, functions, and assignments from the modular definitions into the current module file.\n*   **`compute_relative_order` method**: Computes the relative order of all nodes based on their dependencies and ordering conventions in the source code.\n*   **Methods for merging specific node types (functions, assignments, classes)**\n*   **Updating global nodes and dependency mappings** after merging with modular file dependencies\n\n### Notable Dependencies or Requirements\n\n*   `cst` module for parsing Python code\n*   `MetadataWrapper` class for accessing metadata of the parsed module\n*   External dependencies are restricted to known entities using `_restrict_dependencies_to_known_entities` method.\n\n### Class Inheritance and Interfaces\n\nNone, as it is a custom implementation.\n\n**Example Usage**\n\n```python\nimport cst\n\n# Sample module definition\nmodule_def = \"\"\"class MyClass:\n    def __init__(self):\n        pass\"\"\"\n\n# Create an instance of the ModelFileMapper class\nmapper = ModelFileMapper(module)  # Assuming 'module' is a cst module object\n\n# Call the visit_and_merge_dependencies method to process the module\nmapper.visit_and_merge_dependencies(...\n```\n\nThis code snippet provides a basic overview of how to use the `ModelFileMapper` class."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:887-899
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 899, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "common_partial_suffix", "start_line": 887, "type": "function", "chroma:document": "**Summary:**\n\n### Function Name:\n`common_partial_suffix`\n\n### Primary Purpose:\nReturns the largest common partial suffix between two input strings.\n\n### Key Implementation Details:\n\n* Iterates from the shortest string's end to find matching characters.\n* Builds the `common_suffix` by prepending matching characters from `str1`.\n* If one string is a full suffix of the other, returns an empty string.\n\n### Notable Dependencies/Requirements:\n\n* No external dependencies mentioned.\n* The function uses built-in Python string concatenation and iteration."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:902-1053
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1053, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "replace_class_node", "start_line": 902, "type": "function", "chroma:document": "This function replaces a Python class node in an abstract syntax tree (AST) with a new implementation. The replacement process involves:\n\n1. Updating the base classes and names of the class.\n2. Replacing methods defined in the base classes with those defined in the child class.\n3. Appending new methods not present in the parent class but present in the child class.\n4. Replacing calls to `super()` with the updated code.\n\nThe function takes as input a model file mapper, a node representing the class to be replaced, and the names of the superclasses and the child's superclass. It returns a new node representing the modified class."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1069-1080
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1080, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "find_file_type", "start_line": 1069, "type": "function", "chroma:document": "**Summary:**\n\n### Function Purpose and Functionality\n\n`find_file_type` function takes a `class_name` string as input and returns the corresponding file type. It uses a lookup table `TYPE_TO_FILE_TYPE` to match class names against predefined file types.\n\n### Key Implementation Details\n\n* Uses regular expressions to search for matching file types\n* Falls back to \"modeling\" if no match is found in `TYPE_TO_FILE_TYPE`\n\n### Notable Dependencies and Requirements\n\n* `re` (regular expression) module\n* `TYPE_TO_FILE_TYPE` dictionary (not shown in this snippet)\n\n### Method Signatures and Interfaces\n\n* `find_file_type(class_name: str) -> str`: returns a file type string based on the input class name"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1095-1109
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1109, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "append_new_import_node", "start_line": 1095, "type": "function", "chroma:document": "**Summary of `append_new_import_node` function**\n\nInserts a new import node (`node`) to the `imports_to_keep` list if it's not part of unused or added imports. Modifies `added_names` accordingly.\n\n**Key Implementation Details:**\n\n* Inserts `import_node` into `imports_to_keep` with filtered names.\n\t+ `names_to_keep = [name_value]` where `name_value != unused_imports` and `name_value != added_names`.\n* Applies modifications to `node` using:\n\t+ `with_changes(body=[import_node.with_changes(names=names_to_keep)])`\n\t+ `imports_to_keep.append(new_node)`.\n\n**Notable Dependencies/Requirements:**\n\n* `cst.CSTNode`: Input node type.\n* `added_names`: Set of names to keep track of.\n* `unused_imports`: Set of unused imports.\n\n**External Dependency:**\n\n* `cst.MaybeSentinel.DEFAULT`."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1112-1155
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1155, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_needed_imports", "start_line": 1112, "type": "function", "chroma:document": "**Function Summary: `get_needed_imports`**\n\n*   Purpose:\n    *   Generates needed imports for a given code segment (`body`) from a list of provided imports (`all_imports`)\n*   Functionality:\n\n    1.  **Sorting and Preparation**: Sorts the `body` dictionary based on insert indices, creates a metadata wrapper around the combined `all_imports + new_body`, and resolves scopes to identify assignments.\n    2.  **Import Ref Counting**: Iterates through scope assignments to count reference counts for each import name.\n    3.  **Unused Imports Detection**: Identifies unused imports based on ref count threshold and existing references in the code segment (`body`).\n*   Output:\n    *   A list of needed imports, separated into `usual` (non-protected) and `protected` nodes.\n\n**Key Implementation Details:**\n\n*   Uses a dictionary-based approach to sort and prepare the input data.\n*   Employs a metadata wrapper to analyze import statements.\n*   Utilizes scope resolution to identify assignments and determine ref counts.\n*   Applies thresholds for identifying unused imports.\n\n**Notable Dependencies/Requirements:**\n\n*   `cst.CSTNode` (used for parsing and analyzing code structure)\n*   `MetadataWrapper` (custom class used for metadata-driven analysis)\n*   `ScopeProvider` (interface for scope resolution)\n*   Custom modules (`m.matches`, `append_new_import_node`) not explicitly referenced in the provided function definition."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1158-1176
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1176, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "split_all_assignment", "start_line": 1158, "type": "function", "chroma:document": "**Function Summary: `split_all_assignment`**\n\n**Purpose:** Splits the `__all__` assignment in a modular into separate assignments for corresponding files.\n\n**Main Functionality:**\nExtracts class names from the list of values assigned to `__all__`, determines the file type, and regroups them by file using a dictionary. This function splits the initial assignment into multiple assignments, one per file.\n\n**Key Implementation Details:**\n\n* Iterates through a list of values (`node.body[0].value.elements`) and extracts class names from strings.\n* Determines the file type for each class name using `find_file_type(element.value.evaluated_value)`.\n* Regroups class names by file using a dictionary (`all_all_to_add`).\n\n**Method Signatures and Interfaces:**\n\n* `assign_node.with_changes`: Replaces the value of an assignment node with a new list.\n* `node.with_changes`: Wraps the entire node with new body.\n\n**Notable Dependencies and Requirements:**\n\n* `cst.CSTNode`\n* ` defaultdict(list)`\n* `find_file_type` (not provided)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1179-1448
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1448, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "ModularFileMapper", "start_line": 1179, "type": "class", "chroma:document": "This is a Python code that appears to be a part of the `transformers` library, specifically handling model merging and renaming. Here's a concise summary:\n\nThe code defines several methods for merging models, renaming classes, and handling naming conflicts. It checks if multiple prefixes are used in a modeling file and attempts to use the most frequent or default prefix instead. If a collision is detected, it raises a warning and may use a fallback value.\n\nKey features include:\n\n* Merging models by checking common prefixes\n* Renaming classes to avoid naming conflicts\n* Fallback behavior for missing imported files\n\nThe code requires several external modules, including `tokenizers`, `utils`, and `text_utils`. Some custom functions, such as `get_cased_name` and `common_partial_suffix`, are also used."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1451-1490
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1490, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "check_dependencies_and_create_import_node", "start_line": 1451, "type": "function", "chroma:document": "**Function Summary: `check_dependencies_and_create_import_node`**\n\n* **Purpose**: Ensure that class nodes in `new_dependencies` belong to the correct file type. If not, remove it and create a new import statement.\n* **Key Implementation Details**:\n\t+ Filter class dependencies based on their file types using the `matches` method of a `ModuleMapper`.\n\t+ Create a dictionary `corrected_dependencies` with remaining class nodes that belong to the correct file type.\n\t+ Generate `import_statement` for each misclassified class node and store it in the `new_imports` dictionary.\n\n**Method Signatures and Interfaces**:\n\n* `ModuleMapper.matches()`: Matches a module with a specific node type (in this case, `ClassDef()`).\n* `find_file_type(class_name)`: Returns the file type of a given class name.\n* `cst.parse_statement(import_statement)`: Parses an import statement into a CST (Abstract Syntax Tree) Node.\n\n**Notable Dependencies and Requirements**:\n\n* `ModuleMapper`: A module mapper object that provides the necessary methods.\n* `find_file_type(class_name)` function: Not shown in this code snippet but presumably defined elsewhere in the project.\n* External dependencies:\n\t+ `cst` (Collection of Statements, Functions, Variables, etc.)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1493-1571
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1571, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "get_class_node_and_dependencies", "start_line": 1493, "type": "function", "chroma:document": "**Summary: get_class_node_and_dependencies Function**\n\nThe `get_class_node_and_dependencies` function generates a single class node and its dependencies for a given modular file. It:\n\n1. **Creates the Class Node**: Builds the class node based on inherited classes, if needed, using the provided modular mappings.\n2. **Generates New Imports**: Finds new imports of classes defined in the modular that may be required.\n3. **Analyzes Dependencies**: Checks dependencies and creates import nodes if necessary, recursively finding all dependencies.\n4. **Orders Dependencies**: Computes relative order for the generated dependencies.\n\n**Key Function Details**\n\n* Returns a tuple containing the class node with its updated dependencies, file type, and new imports.\n* Uses modular mappings to rename super classes according to exact rules.\n\n**Notable Dependencies and Requirements**\n\n* `modular_mapper` object\n\t+ `model_specific_imported_objects`\n\t+ `imported_objects_per_file`\n\t+ `visited_modules`\n\t+ `renamers`\n* External dependencies:\n\t+ `find_file_type`\n\t+ `augmented_dependencies_for_class_node`\n\t+ `check_dependencies_and_create_import_node`\n\n**Interfaces and Dependencies**\n\n* Uses interfaces from `modular_mapper` object, including `mapper`, `renamer`, and `visited_modules`.\n* Returns nodes using a dictionary-like structure with relative dependency order."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1574-1625
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1625, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "create_modules", "start_line": 1574, "type": "function", "chroma:document": "**Summary: Create Modules Function**\n\n*   **Purpose:** Creates new modules based on visiting modular files by replacing classes and adding dependencies.\n\n**Main Purpose and Functionality:**\n\nThe `create_modules` function takes a `ModularFileMapper` object as input and returns a dictionary of new modules. It achieves the following key functionality:\n\n1.  Replaces classes in each class definition with updated nodes and added dependencies.\n2.  Adds potential new imports to the `modular_mapper.imported_objects_per_file` map.\n3.  Sorts nodes according to their relative order for file writing.\n4.  Writes all nodes to files, keeping certain variables at the beginning of the file.\n5.  Aggregates and finds correct imports to write full modules.\n\n**Key Implementation Details:**\n\n*   Iterates through each class definition in `modular_mapper.classes`.\n*   Uses `get_class_node_and_dependencies` function to get updated nodes and dependencies for each class.\n*   Updates `imported_objects_per_file` map with new imports.\n*   Sorts nodes for file writing using a custom sorting key.\n*   Creates a new module by combining needed imports and body.\n\n**Notable Dependencies or Requirements:**\n\n*   `ModularFileMapper` object\n*   `get_class_node_and_dependencies` function\n*   `VARIABLES_AT_THE_BEGINNING` list\n*   `cst.Module` class\n\n**Additional Information:**\n\nThis code appears to be part of a larger system designed to work with modular files and their dependencies. The function is closely tied to the `ModularFileMapper` object, which seems to provide necessary data for creating new modules.\n\nThe code uses various external libraries or classes (e.g., `cst.Module`, `VARIABLES_AT_THE_BEGINNING`) that are not defined in this snippet."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1628-1656
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1656, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "convert_modular_file", "start_line": 1628, "type": "function", "chroma:document": "**Function Summary:**\n\n`convert_modular_file(modular_file)`\n\nConverts a modular Python file to conform to formatting standards.\n\n**Key Implementation Details:**\n\n- Parses the input modular file using regular expressions and CST (Common SyntaxTree) parser.\n- Applies transformations using `MetadataWrapper` and `ModularFileMapper`.\n- Formats code using Ruff library on two rounds of review (initial and final).\n\n**Notable Dependencies and Requirements:**\n\n- `re`, `os`, `cst`, and `Ruff` libraries for parsing and formatting purposes.\n- Custom classes (`MetadataWrapper`, `ModularFileMapper`) are used to implement transformation logic."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py:1659-1679
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1679, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/modular_model_converter.py", "name": "save_modeling_file", "start_line": 1659, "type": "function", "chroma:document": "**Summary:**\n\nSave Modeling File Functionality\n==============================\n\n**Purpose and Functionality**\n-----------------------------\n\nThis function `save_modeling_file` takes two inputs, `modular_file` and `converted_file`, and saves the contents of `converted_file` to new files with modified names based on their original file types.\n\n**Key Implementation Details:**\n\n*   Iterates over each file type in `converted_file` keys.\n*   Creates a new file name by replacing \"modular_\" prefix and adding `.py` extension suffix based on file type.\n*   Checks if the converted file lines contain any non-comment content and writes it to the new file.\n\n**Notable Dependencies and Requirements:**\n\n*   Uses `logger` for warnings during file writing.\n*   External dependency on Python's built-in `logging` module (referenced as \"logger\").\n*   Does not inherit or implement any specific interfaces."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_build.py:32-37
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 37, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_build.py", "name": "test_custom_files_are_present", "start_line": 32, "type": "function", "chroma:document": "**Summary:**\n\n`test_custom_files_are_present`: Verifies that custom files exist at a specified `transformers_path`.\n\n**Key Implementation Details:**\n\n* Iterates through `FILES_TO_FIND` and checks existence of each file using `exists()`.\n* Returns `True` if all files are present, `False` otherwise.\n\n**Notable Dependencies/Requirements:**\n\n* `FILES_TO_FIND`: a list of filenames to check (assumed to be defined elsewhere).\n* External dependencies not mentioned.\n* `transformers_path` is assumed to be an existing directory path."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py:37-60
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 60, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py", "name": "get_all_model_names", "start_line": 37, "type": "function", "chroma:document": "**Function Summary: get_all_model_names()**\n\nThe `get_all_model_names()` function extracts and returns a set of unique model names from auto-modeling files in the `transformers.models.auto` namespace.\n\n**Key Implementation Details:**\n\n* Iterates through three auto-modeling files (`modeling_auto`, `modeling_tf_auto`, `modeling_flax_auto`) to gather model mappings.\n* Uses string matching (`str.endswith()`) and object existence checks (`isinstance()` and `is None`) to identify relevant model names.\n\n**Notable Dependencies/Requirements:**\n\n* `transformers.models.auto` namespace\n* No external dependencies listed."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py:63-81
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 81, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py", "name": "get_tiny_model_names_from_repo", "start_line": 63, "type": "function", "chroma:document": "**Concise Summary: `get_tiny_model_names_from_repo`**\n\n**Purpose:** Retrieves the names of tiny models defined in auto-mappings and filtering out those not on the Hub.\n\n**Key Implementation Details:**\n\n* Uses `get_all_model_names()` to get all model names, then filters using a set operation (`difference` and `update`) between model names and their implementations on the Hub (`tiny_models_names`).\n* Checks for the presence of \"TF\" prefix in a specific format (e.g., \"TFx[model_name]\"), removing models that don't have a matching tiny version.\n* Uses JSON data from \"tests/utils/tiny_model_summary.json\" to infer available model classes.\n\n**Dependencies/Requirements:**\n\n* `get_all_model_names()`\n* `json` module\n* `copy` module\n* External dependency on \"tests/utils/tiny_model_summary.json\" file."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py:84-169
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 169, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_tiny_models.py", "name": "get_tiny_model_summary_from_hub", "start_line": 84, "type": "function", "chroma:document": "**Function Summary: `get_tiny_model_summary_from_hub`**\n\n**Purpose:** Fetches summaries for tiny models from the Hugging Face model hub.\n\n**Main Purpose**: The function retrieves a list of tiny models from the Hugging Face model hub, constructs their metadata (e.g., tokenizer, processor, and model classes), and stores it in a JSON file.\n\n**Key Implementation Details:**\n\n*   Filters models by author and namespace.\n*   Iterates through each model, retrieving its metadata using the `hf_api` interface.\n*   Handles cases where model metadata is not available, waiting 1 second between attempts.\n*   Extracts tokenizer classes from slow and fast versions of the AutoTokenizer.\n*   Extracts processor classes from image processors (AutoImageProcessor).\n*   Extracts model classes from transformers module using string-based lookup.\n\n**Notable Dependencies:**\n\n*   `hf_api`: Hugging Face API interface for interacting with the model hub.\n*   `transformers`: Hugging Face transformer library for loading pre-trained models.\n*   ` AutoTokenizer`, `AutoImageProcessor`, and `AutoFeatureExtractor` classes from transformers module.\n\n**External Requirements:**\n\n*   Requires access to Hugging Face API subscription or trial account."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:371-396
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 396, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_missing_backends", "start_line": 371, "type": "function", "chroma:document": "**Summary**\n\nThe `check_missing_backends` function checks if all required backends (PyTorch, TensorFlow, and Flax) are installed. It:\n\n* Compares availability of each backend using `is_`:tch_available`, `is_tf_available`, and `is_flax_available`\n* If any backend is missing, it appends the name to `missing_backends` list\n* Raises an exception in CI if all backends are not installed or a warning for users otherwise\n* Provides instructions on how to install missing backends using pip\n\n**Key Implementation Details**\n\n* Checks availability of three backends using separate functions (`is_torch_available`, `is_tf_available`, and `is_flax_available`)\n* Uses an exception to raise error in CI if all backends are not installed\n* Uses a warning to notify users about the missing backends\n* Provides a pip installation command to install missing backends\n\n**Notable Dependencies/Requirements**\n\n* `pytorch`: PyTorch backend\n* `tensorflow`: TensorFlow backend\n* `flax`: Flax backend\n* `transformers`: Transformers repository\n* `os`: Operating system dependency for environment variable access"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:399-420
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 420, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_model_list", "start_line": 399, "type": "function", "chroma:document": "**Function Summary:** `check_model_list`\n\n**Primary Purpose:** Validates that the models listed as subfolders of `src/transformers/models` match the available models in `transformers.models`.\n\n**Key Implementation Details:**\n\n*   Loads all non-deprecated subfolder models from `src/transformers/models/` into `_models`.\n*   Compares the list of models in `_models` against the models listed in `transformers.models`.\n*   Raising exception when there are missing models that should be included.\n\n**Notable Dependencies:**\n\n*   `os.path`: for path manipulation (e.g., joining paths).\n*   `PATH_TO_TRANSFORMERS`: a defined constant referencing to the transformers directory."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:425-454
encoding: FLOAT32
metadata:
{"docstring": "# If some modeling modules should be ignored for all checks, they should be added in the nested list\n# _ignore_modules of this function.", "end_line": 454, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "get_model_modules", "start_line": 425, "type": "function", "chroma:document": "**Summary**\n\nThis function, `get_model_modules`, retrieves all model modules from the `transformers` library, excluding deprecated and specific \"modeling\" submodules. The primary purpose is to gather a list of models for use in a larger application.\n\n**Key Implementation Details:**\n\n*   ignores ignored module names (`_ignore_modules`) and magic dunder attributes in the directory listing.\n*   uses `getattr()` to dynamically access individual model modules and their \"modeling\" submodules.\n*   excludes deprecated model names detected as \"__deprecated__\".\n\n**Notable Dependencies/Requirements:**\n\n*   The `transformers` library, specifically its `models` module.\n\n**Method Signatures Interfaces:**\n\n*   `get_model_modules() -> List[str]`: Returns a list of model modules in the transformed format, excluding obsolete ones."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:457-478
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 478, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "get_models", "start_line": 457, "type": "function", "chroma:document": "Here's a concise and focused summary of the given function:\n\n### Function Summary: `get_models`\n\n**Purpose:** Extract objects from a module that are models.\n\n**Primary Purpose:** Return a list of model classes as tuples (class name, actual class) based on their inheritance relation to predefined model classes.\n\n**Key Implementation Details:**\n\n* Iterates over attributes in the given module.\n* Filters out attributes whose names contain keywords related to `PreTrainedModel` subclass (excluding them by default).\n* Checks if each attribute is a type and inherits from defined model classes using multiple inheritance.\n* Appends validated attributes to the result list.\n\n**Notable Dependencies/Requirements:**\n\n* `transformers`: Module containing definition of predefined model classes.\n* `types`: Module for accessing built-in types like `ModuleType`.\n\n### Return Value:\n\nA list of tuples (class name, actual class) representing extracted model objects."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:481-492
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 492, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "is_building_block", "start_line": 481, "type": "function", "chroma:document": "Here is a concise summary of the function:\n\n**Function Summary**\n\n`is_building_block(model: str) -> bool`\n\nChecks if a machine learning model is a building block part of a larger model. It returns `True` for models ending with \"Wrapper\", \"Encoder\", \"Decoder\", or \"Prenet\".\n\n**Key Implementation Details**\n\n* Uses string suffix matching (`str.endswith()`)\n* Only checks for specific model prefixes (no regular expression)\n\n**Dependencies and Interfaces Noted**\n\n* No dependencies are mentioned in the function code.\n* Inherited or implemented interfaces not applicable.\n\nThis summary is concise, accurate, and focused on providing essential information about the function's purpose and implementation details."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:495-499
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 499, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "is_a_private_model", "start_line": 495, "type": "function", "chroma:document": "**Summary**\n\nDetermines whether a given model should exclude itself from the main initialization.\n\n**Functionality**\n\n- Checks if the model is in the `PRIVATE Modes` list, returning `True` if it is.\n- Falls back to calling `is_building_block` with the model as an argument to determine its behavior.\n\n**Key Implementation Details**\n\n- Method signature: `def is_a_private_model(model: str) -> bool`\n- Calls `PRIVATE_MODELS` set to determine private models\n- Calls `is_building_block` method on a given model\n\n**Notable Dependencies or Requirements**\n\n- Uses the `PRIVATE.Models` set, which is not defined in this snippet. \n  - References related class 'PrivateModels'\n  \nNote: No external dependencies are listed as this function only references internal sets and methods."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:502-514
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 514, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_models_are_in_init", "start_line": 502, "type": "function", "chroma:document": "**Function Summary**\n\n`check_models_are_in_init()`\n=====================\n\nChecks if defined model libraries are deployed in the main application initialization.\n\n**Primary Purpose**\n\nVerifies that all models imported from external modules are included in the main `init.py` file of the library.\n\n**Key Implementation Details**\n\n* Iterates over model modules using `get_model_modules()` and collects models not present in `transformers` directory\n* Filters out private models using `is_a_private_model()`\n* Raises an exception if any models are missing from main init\n\n**Notable Dependencies and Requirements**\n\n* `transformers`: used to define the expected models directory\n* `get_model_modules()`, `get_models()`, and `is_a_private_model()`: functions not shown in this code snippet, but assumed to be defined elsewhere in the library."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:519-555
encoding: FLOAT32
metadata:
{"docstring": "# If some test_modeling files should be ignored when checking models are all tested, they should be added in the\n# nested list _ignore_files of this function.", "end_line": 555, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "get_model_test_files", "start_line": 519, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_model_test_files()` \n\n Retrieves and returns a list of test files (\"test_modeling\" files) within the `models` directory under the test root. Excludes ignored files from specified `_ignore_files`.\n\n**Key Details:**\n\n* Ignored files are specified in `_ignore_files`.\n* Files with \"test_modeling\" in their filename, excluding those in `_ignore_files`, are considered.\n* Files are joined using `os.path.join()` to construct absolute paths.\n\n**Notable Dependencies:**\n\n* `os`: for file system interactions\n* `PATH_TO_TESTS`: script-defined constant for test root path"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:560-583
encoding: FLOAT32
metadata:
{"docstring": "# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class\n# for the all_model_classes variable.", "end_line": 583, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "find_tested_models", "start_line": 560, "type": "function", "chroma:document": "**Function Summary**\n\n`find_tested_models(test_file: str) -> List[str]`\n\nDetects models tested in a given test file by parsing its content, specifically extracting `all_model_classes`.\n\n**Key Functionality**\n\n* Reads the content of a test file\n* Uses regular expressions to extract `all_model_classes` patterns\n* Parses pattern entries to extract model names\n\n**Implementation Details**\n\n* Method signatures: `re.findall` with two pattern strings for `all_model_classes`\n* Algorithm: Combines multiple pattern findings into a single list using `.append`\n\n**Notable Dependencies/Requirements**\n\n* `os.path.join` and `open` for file operations\n* `re` module for regular expression matching\n\n**No notable inherited or implemented interfaces.**"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:586-592
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 592, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "should_be_tested", "start_line": 586, "type": "function", "chroma:document": "**Function Summary**\n\n### `should_be_tested`\n\nDetermines whether a model with the given name should be tested.\n\n**Purpose**: Checks if a model's existence in an IGNORE_NON_TESTED list or if it's a building block model to exclude testing.\n\n**Key Implementation Details**:\n\n* Returns `False` for models in `IGNORE NON_TESTED`.\n* Excludes models with this method signature from being tested.\n* References `is_building_block(model_name)` using the exact name of the function.\n* Inherently uses an external list (`IGNORE_NON_TESTED`) without defining it within this code snippet.\n\n**Notable Dependencies/Requirements**: \n\n* `IGNORE_NON_TESTED` external list or constant."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:595-625
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 625, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_models_are_tested", "start_line": 595, "type": "function", "chroma:document": "**Summary:**\n\nThis function checks if all models defined in a module (`module`) are tested in a given test file (`test_file`). It returns a list of error messages for models that are not tested.\n\n**Key Details:**\n\n* Defines and tests models from `defined_models` and `tested_models` respectively\n* Returns error messages for models that are not tested but should be (`model_name`)\n* Notifies user to add model to `all_model_classes` in test file or ignore with `IGNORE_NON_TESTED`\n \n**Function Signature:**\n```python\ndef check_models_are_tested(module: types.ModuleType, test_file: str) -> List[str]:\n```\n\n**Notable Dependencies/Requirements:**\n\n* `get_models`: module for getting defined models\n* `find_tested_models`: function for finding tested models in a file\n* `TEST_FILES_WITH_NO_COMMON_TESTS`: dictionary of files with no common tests\n* `utils/check_repo.py`: configuration file for repository checks\n* `types.ModuleType`: type hint for a Python module"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:628-646
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 646, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_models_are_tested", "start_line": 628, "type": "function", "chroma:document": "**Summary**\n\n*   Function: `check_all_models_are_tested()`\n*   **Purpose:** Verifies if all models have corresponding test files and reports any mismatches.\n*   Key implementation:\n    *   Retrieves module metadata (`get_model_modules()`).\n    *   Iterates over each module, matching it to its test file based on the module name prefix.\n    *   Calls a separate function (`check_models_are_tested()`) for modules with test files and combines any returned errors.\n*   **Notable dependencies:**\n    *   `get_model_modules()` (module metadata retrieval)\n    *   `get_model_test_files()` (test file retrieval)\n\n**External interfaces/deependencies:**\n\n    - Check Model Functions\n   "}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:649-664
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 664, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "get_all_auto_configured_models", "start_line": 649, "type": "function", "chroma:document": "**Function Summary: `get_all_auto_configured_models`**\n\n**Purpose:** Extracts and returns a list of model names from auto-configured models in at least one available framework (PyTorch, TensorFlow, or Flax).\n\n**Implementation Details:**\n\n* Iterates over specific attribute names (`MODEL_`, `TF_MODEL_`, and `FLAX_MODEL_`) and their corresponding mapping names within each auto-configured model.\n* Uses set operations to combine unique model names from different frameworks.\n\n**Notable Dependencies:**\n\n* `transformers.models.auto.modeling_auto`\n* `transformers.models.auto.modeling_tf_auto`\n* `transformers.models.auto.modeling_flax_auto`\n* `is_torch_available()`, `is_tf_available()`, and `is_flax_available()` (functions to check availability of respective frameworks)\n\n**Return Value:** A list of strings representing model names from auto-configured models in at least one available framework."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:667-675
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 675, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "ignore_unautoclassed", "start_line": 667, "type": "function", "chroma:document": "**Summary:**\nThis function determines whether a model with the given `model_name` should be in an auto-class. It checks against a special whitelist and ensures that specific models like \"Encoder\" and \"Decoder\" are ignored.\n\n**Key Details:**\n\n* **Functionality:** `ignore_unautoclassed(model_name) -> bool`\n* **Purpose:** Determine if a model is auto-configured\n* **Design Choice:** Whitelisting approach with specific exceptions for \"Encoder\" and \"Decoder\" models.\n* **Dependencies/Requirements:**\n\t+ `IGNORE_NON_AUTO_CONFIGURED`: A predefined list of accepted non-auto-configured model names.\n\nNote: The original code snippet included the `IGNORE_NON_AUTO_CONFIGURED` variable, which is likely defined elsewhere in the project. This summary assumes its existence for the purpose of analysis."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:678-700
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 700, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_models_are_auto_configured", "start_line": 678, "type": "function", "chroma:document": "**Summary:**\n\n### Function Purpose:\n\n`check_models_are_auto_configured` checks if models defined in a module are part of an auto class.\n\n### Key Implementation Details:\n\n*   Calculates the list of defined models from the input `module`.\n*   Iterates over each model, verifying if it exists in the `all_auto_models` list and ignoring unautoclassed models.\n*   Appends error messages to the `failures` list for non-existent models.\n\n### Notable Dependencies/Requirements:\n\n*   `get_all_auto_configured_models()`: Returns a list of all models in an auto class.\n*   `ignore_unautoclassed(model_name)`: Function indicating whether a model is intended to be ignored.\n*   `IGNORE_NON_AUTO_CONFIGURED` constant: File location for modifying ignore behavior."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:703-715
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 715, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_models_are_auto_configured", "start_line": 703, "type": "function", "chroma:document": "**Summary:**\n\n`check_all_models_are_auto_configured()` function checks whether all models are configured as auto-configurable. It verifies that all required backends exist and iterates through modules to validate individual model configurations, accumulating failures in a list.\n\n**Key Details:**\n\n* Verifies existence of all required backends\n* Iterates through modules to validate model configurations\n* Checks for `None` return value from `check_models_are_auto_configured()`\n* Accumulates failure messages and raises an exception\n\n**Notable Dependencies/Requirements:**\n\n* `get_model_modules()` function\n* `get_all_auto_configured_models()` function\n* `check_missing_backends()` function"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:718-759
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 759, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_auto_object_names_being_defined", "start_line": 718, "type": "function", "chroma:document": "**Summary:**\n\nThe `check_all_auto_object_names_being_defined()` function checks if all names defined in auto mapping mappings exist in the library. It iterates through various auto modeling files, maps, and class definitions to identify missing objects.\n\n**Key Functionality:**\n\n* Checks for missing classes derived from `TOKENIZER_MAPPING_NAMES`, `IMAGE_PROCESSOR_MAPPING_NAMES`, etc.\n* Iterates through auto modeling files (`modeling_auto`, `modeling_tf_auto`, `modeling_flax_auto`) and extracts mapping names and their corresponding class definitions\n* Reports missing classes in the following format: \"<class_name> appears in <mapping-name> but it is not defined in the library.\"\n\n**Notable Dependencies/Requirements:**\n\n* `transformers.models.auto` module\n* External dependencies:\n\t+ `transformers`\n\t+ `torch`\n\t+ `jittor`\n\t(+ other transformer-based libraries)\n* Inherited Interfaces:\n\t+ `TOKENIZER_MAPPING_NAMES`, `IMAGE_PROCESSOR_MAPPING_NAMES`, etc.\n\n**Implementation Details:**\n\n* Uses dynamic imports and attribute access to extract mapping names and class definitions.\n* Includes private model checks for certain mapping types.\n\n**Primary Purpose:**\nThe primary purpose of this function is to identify missing classes derived from auto mapping mappings, raising an exception if any are found."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:762-792
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 792, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_auto_mapping_names_in_config_mapping_names", "start_line": 762, "type": "function", "chroma:document": "**CONCISE SUMMARY**\n\n### Function Purpose:\n\nDefectsCheckAutoMappingNames checks if all keys defined in auto mapping names appear in CONFIG_MAPPING_NAMES.\n\n### Key Implementation Details:\n\n- Iterates through special mapping names and their corresponding values from `TOKENIZER_PROCESSOR_MAPPING_NAMES` and `AutoTokenizer`.\n- Compares model types in auto modeling files with those in CONFIG_MAPPING_NAMES.\n- Raises an exception when inconsistencies are found.\n\n### Notable Dependencies/Requirements:\n\n- `CONFIG_MAPPING_NAMES`\n- `IMAGE_PROCESSOR_MAPPING_NAMES`\n- `FEATURE_EXTRACTOR_MAPPING_NAMES`\n- `PROCESSOR_MAPPING_NAMES`\n- `transformers.models.auto` (dynamic import)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:795-816
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 816, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_auto_mappings_importable", "start_line": 795, "type": "function", "chroma:document": "**Summary**\n\n`check_all_auto_mappings_importable` function checks whether all auto-mapping functions can be imported.\n\n**Primary Purpose**: Verify if all auto-mapping files are properly installed and importable.\n\n**Key Implementation Details**:\n\n* Iterates through `modeling_auto`, `modeling_tf_auto`, and `modeling_flax_auto` modules to collect their mapping names.\n* Checks for the existence of each mapped function in the `transformers` namespace using `hasattr`.\n\n**Notable Detections**:\n\n* Failure: `MAPPING_NAME` (e.g. `_MAPPING_NAMES`) not found in `transformers`.\n* Raised exception if any failure is detected, listing all failed mappings.\n\n**Dependencies and Requirements**:\n- `transformers.models.auto`\n- External dependencies: Not explicitly listed, but assumed to be part of the `transformers` package.\n- Inherited/Implemeted interfaces: No notable references."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:819-861
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 861, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_objects_being_equally_in_main_init", "start_line": 819, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `check_objects_being_equally_in_main_init`, checks if the objects present in TensorFlow/Flax counterpart are also present in PyTorch's main module. It iterates through all attributes of the `transformers` object and verifies the existence of corresponding attributes with prefix \"modeling_\". If found, it raises an exception indicating failures.\n\n**Key Details:**\n\n- **Implementation:** This function uses attribute iteration to check for equivalent objects between TensorFlow/Flax and PyTorch.\n- **Important Method Signatures:**\n  - `is_tf_available()`: checks if the TF framework is available.\n  - `is_flax_available()`: checks if the Flax framework is available.\n  \n**Notable Dependencies and Requirements:**\n\n- **Related Classes/Fuctions:** \n  - `transformers` (the object to be checked)\n  - `sys`, `os`\n- **External Dependencies:**\n  - TensorFlow\n  - Flax\n  - PyTorch\n- **Implemented Interfaces:**\n    - No specific interfaces are mentioned, but the function appears to follow a generic structure for comparing attributes between frameworks.\n-"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:867-890
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 890, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_decorator_order", "start_line": 867, "type": "function", "chroma:document": "**Summary: `check_decorator_order` Function**\n\n* **Purpose**: Verifies that in a given test file, the slow decorator \"parameterized\" always follows other decorators.\n* **Implementation Details**:\n\t+ Extracts lines from the specified test file and searches for occurrences of the \"_re_decorator\" regular expression.\n\t+ If the search finds \"parameterized\", adds the current line index to the error list if a preceding \"parameterized\" decorator was found.\n\t+ Resets the tracking of preceding decorators when a new one is encountered.\n* **Notable Dependencies**: `_re_decorator` regular expression (internal), `List[int]` type hint for return value.\n* **Assumptions/Requirements**: None external dependencies mentioned, no inherited or implemented interfaces listed."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:893-906
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 906, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_decorator_order", "start_line": 893, "type": "function", "chroma:document": "**Code Summary**\n\n```markdown\n### Functionality\n\nChecks if the slow decorator is last in every test file.\nRaises ValueError with error messages if not.\n\n### Key Implementation Details\n\n* Iterates over test files (ending with .py) and checks decorator order using `check_decorator_order` method.\n* Compares actual order against expected order (slow decorator first).\n* If mismatch, raises `ValueError` with list of error messages and corresponding file locations.\n\n### Notable Dependencies\n\n- [os] library for accessing file system\n- [PATH_TO_TESTS] environment variable or constant for path to test files\n```\n\nNote: This summary preserves the exact names of classes/functions/variables mentioned in the original code, uses precise technical terms, and focuses on key implementation details."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:909-934
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 934, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "find_all_documented_objects", "start_line": 909, "type": "function", "chroma:document": "**Summary**\n\nThis function, `find_all_documented_objects`, parses Markdown files in the specified directory to detect class and method documentation. It returns two data structures: a list of object names being documented and a dictionary mapping object names to their documented methods.\n\n**Key Implementation Details**\n\n* Uses regular expressions to extract object names and methods from Markdown content\n* Iterates over Markdown files in the specified directory, extracting object objects with autodoc tags\n* Uses dictionary comprehension to create a mapping between object names and their documented methods\n\n**Primary Purpose**: Detects class and method documentation in Markdown files.\n\n**Notable Dependencies**\n\n* `re` for regular expression pattern matching\n* `pathlib` for working with file paths"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1036-1071
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1071, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "ignore_undocumented", "start_line": 1036, "type": "function", "chroma:document": "**Summary**\n\nThis function, `ignore_undocumented`, determines whether a given string `name` should be included in the documentation based on specific rules. It checks for:\n\n* Uppercase constants and pre-trained models\n* Submodules (e.g., transformer.py)\n* Load functions (e.g., load_tf())\n* is_xxx_available functions\n* Deprecated objects\n* MMBT model\n\n**Key Implementation Details**\n\n* The function uses a combination of string matching and path checking using `os.path`.\n* The `PATH_TO_TRANSFORMERS` constant is assumed to be defined elsewhere in the codebase.\n\n**Notable Dependencies and Requirements**\n\n* `os`: for file system operations\n* `PATH_TO_TRANSFORMERS`: implied path to transformer submodules\n\n**Primary Purpose**\nThe primary purpose of this function is to determine whether a given string should be excluded from documentation based on specific rules."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1074-1086
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1086, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_all_objects_are_documented", "start_line": 1074, "type": "function", "chroma:document": "**Summary**\n\n`check_all_objects_are_documented()` function:\n\n*   **Purpose and functionality**: Ensures all models in the `transformers` namespace are properly documented by checking for public initialization objects and documenting methods that exist in a mapping.\n\n**Key implementation details**:\n\n*   Checks objects in the `transformers` namespace using dir() to find non-module, non-internal private objects.\n*   Verifies if objects are not already documented by calling `find_all_documented_objects()` and ignoring certain method names using `ignore_undocumented(c)`.\n*   Raises an exception with a list of undocumented objects if any exist.\n\n**Notable dependencies or requirements**:\n\n*   Calls other functions: `find_all_documented_objects()`, `check_model_type_doc_match()`, and `check_public_method_exists(documented_methods_map)`. \n*   References classes/functions: `transformers._modules`, `dir(transformers)`.\n*   External dependencies: None mentioned.\n*   Implements interfaces: [Unspecified, as it appears to be a custom class or module within the `transformers` namespace."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1089-1135
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1135, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_public_method_exists", "start_line": 1089, "type": "function", "chroma:document": "**Function Summary:**\n\n`check_public_method_exists(documented_methods_map)`\n=============================================\n\n**Purpose:** Ensures that all explicitly documented public methods are defined in the corresponding class.\n\n**Main Functionality:**\n- Verifies consistency of documented public methods across the specified map.\n- Resolves method definitions within nested object hierarchies using dynamic imports.\n\n**Key Implementation Details:**\n\n- Iterates through each object and its associated classes, ensuring unique documented method names.\n- Handles class and method name resolutions via `getattr` with conditional error handling for unknown submodules or dependencies.\n\n**External Dependencies:**\n- `transformers`: A required submodule.\n\n**Interfaces and Methods:** \n\n- The function iterates over a dictionary mapping objects to their listed public methods (`documented_methods_map`)."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1138-1162
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1162, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_model_type_doc_match", "start_line": 1138, "type": "function", "chroma:document": "**Summary**\n\nThis function, `check_model_type_doc_match`, checks if all documentation pages under `PATH_TO_DOC/model_doc` correspond to existing models. It iterates through the doc pages, finds their corresponding model types using the `MODEL_TYPE_TO_DOC_MAPPING` dictionary, and compiles a list of errors. The function raises a `ValueError` with a list of error messages if any errors are found.\n\n**Key Implementation Details**\n\n* Iterates through documentation pages under `PATH_TO_DOC/model_doc`\n* Uses `transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING` to get existing model types\n* Compares each doc page's name with the model types, providing close matches for non-matching names\n* Raises an error if any doc pages do not match an existing model type\n\n**Note**\n\nThe function relies on external dependencies:\n\n* `transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING`\n* `get_close_matches` (not shown in this code snippet)\n* `models/auto/configuration_auto`"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1165-1191
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1191, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_deprecated_constant_is_up_to_date", "start_line": 1165, "type": "function", "chroma:document": "**Summary**\n\nThe `check_deprecated_constant_is_up_to_date` function checks the consistency of the `DEPRECATED_MODELS` constant in `models/auto/configuration_auto.py`. It ensures that:\n\n* All models in the deprecated folder are included in the constant.\n* None of the models listed in the constant are not present in the deprecated folder.\n\n**Key Implementation Details**\n\n* The function uses a list comprehension to extract model names from the deprecated folder.\n* It compares the models in the `DEPRECATED_MODELS` constant with those in the deprecated folder using set differences.\n* If any inconsistencies are found, it raises an exception with a descriptive message.\n\n**Notable Dependencies and Requirements**\n\n* `transformers`: A dependency for accessing the `models.auto.configuration_auto` module.\n* The function assumes that the constants and folders referenced are located within this module."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py:1194-1217
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1217, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_repo.py", "name": "check_repo_quality", "start_line": 1194, "type": "function", "chroma:document": "**Summary:**\n\nThe `check_repo_quality` function runs a sequence of checks on a repository, evaluating various aspects of model quality and configuration. Specifically, it:\n\n*   Verifies models' inclusion\n*   Inspects public visibility of models\n*   Confirms test existence for models\n*   Checks for documentation presence in objects\n*   Validates auto-class configurations\n*   Examines auto-mapping definitions and importability\n*   Ensures object distribution in `__init__`\n*   Verifies the `DEPRECATED_MODELS` constant's accuracy\n\n**Key Functions/Methods:**\n\n- `check_model_list()`: Verifies models' existence\n- `check_models_are_in_init()`: Inspects public visibility of models\n- `check_all_decorator_order()`: Confirms test existence for models\n- `check_all_models_are_tested()`: Confirms documentation presence in objects\n- `check_all_objects_are_documented()`: Validates auto-mapping definitions and importability\n\n**Notable Dependencies/Requirements:**\n\n- The function relies on the `CONFIG_MAPPING_NAMES` constant, indicating an external dependency.\n- It also expects the existence of a `DEPRECATED_MODELS` constant, implying external configuration or setup.\n\nPlease note that further context is necessary to provide a more accurate analysis."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_doctest_list.py:43-75
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 75, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_doctest_list.py", "name": "clean_doctest_list", "start_line": 43, "type": "function", "chroma:document": "**Code Summary**\n\n### Function Purpose\n\n`clean_doctest_list` cleans and organizes a doctest file by removing non-existent paths and sorting the remaining paths in alphabetical order.\n\n### Key Implementation Details\n\n* The function reads a doctest file line-by-line, stripping leading whitespace and splitting on spaces.\n* It checks if each path exists and creates a list of non-existent paths.\n* If any non-existent paths are found, they are reported as an error.\n* The function then sorts the remaining paths alphabetically and writes them back to the original file. The sorting can be skipped with the `overwrite` parameter.\n\n### Notable Dependencies\n\n* `os.path`: used for path manipulation (e.g., `join`, `resolve`)\n* `REPO_PATH`: assumed to be a predefined constant containing the repository root path\n\n### Method Signature and Interfaces\n\n| Method/Interface | Description |\n| --- | --- |\n| `clean_doctest_list(doctest_file: str, overwrite: bool = False)` | Cleans and organizes a doctest file. |\n\n### External Dependencies\n\nNone mentioned in this snippet, but `os` module is used implicitly."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:175-177
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 177, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "_is_definition_header_ending_line", "start_line": 175, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `_is_definition_header_ending_line`, determines whether a given line of code indicates the end of a class or function definition.\n\n```markdown\n### Main Purpose and Functionality\n\nDetermines if a line ends an opening parenthesis of a class or function definition.\n\n### Key Implementation Details\n\n- Uses regular expression (`re.search`) to search for the pattern `^\\s*\\)(\\s*->.*:|:)\\s*$` at the start of the line.\n- Returns `True` when the pattern is found, indicating an ending definition line.\n- The primary purpose is to identify lines that signal the completion of a class or function definition.\n\n### Notable Dependencies and Requirements\n\nNone listed, but uses Python's built-in `re` module for regular expression matching.\n```\n\n**Additional Information:**\n\nThis function does not depend on any external libraries beyond the `re` module."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:180-183
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 183, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "_should_continue", "start_line": 180, "type": "function", "chroma:document": "**Summary:**\n\n- `should_continue(line: str, indent: str) -> bool`: Returns True if the given line is empty, starts with the specified indent, or marks the end of a class or function definition.\n\n**Implementation Details:**\n\n- Checks for three conditions:\n  - Starts with the specified indent (`line.startswith(indent)`)\n  - Is an empty line after stripping whitespace (`len(line.strip()) == 0`)\n  - Marks the end of a definition header using `_is_definition_header_ending_line(line)`"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:186-252
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 252, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "_sanity_check_splits", "start_line": 186, "type": "function", "chroma:document": "**Function Summary: `_sanity_check_splits`**\n\n### Purpose and Functionality\n\nVerifies the structure of paired code blocks in `splits_1` and `splits_2`. For classes, ensures one of three predefined block structures is matched. For functions/methods, verifies exact matching of block arrangements.\n\n### Key Implementation Details\n\n* Class block structure checking:\n\t+ Supports single block without name and consecutive sequence of blocks with name\n\t+ Number of blocks with name in consecutive sequence not considered\n* Function/method block structure checking:\n\t+ Exact matching of block arrangement required\n\n### Notable Dependencies and Requirements\n\n* `is_class` flag indicating class definition or not\n* `filename` parameter for logging purposes\n* Internal constants and interface (`_block_without_name_`, `_empty_block_`)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:255-280
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 280, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "find_block_end", "start_line": 255, "type": "function", "chroma:document": "**Function Summary:**\n`find_block_end`\n---------------\n\nFind the index of the block's ending line in a source code.\n\n```python\ndef find_block_end(lines: List[str], start_index: int, indent: int) -> int:\n    # ...\n```\n\n### Purpose and Functionality:\n\nThis function finds the end of a class/func block in a source code, starting from `start_index`.\nIt iterates through the lines until it encounters an invalid continuation line or reaches the end of the code.\n\n### Key Implementation Details:\n\n- Iterates until a blank or invalid line (`_should_continue`) is encountered.\n- Cleans up empty lines at the end.\n- Returns the index of the block's ending line (inclusive).\n\n### Dependencies and Requirements:\n No external dependencies."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:283-388
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 388, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "split_code_into_blocks", "start_line": 283, "type": "function", "chroma:document": "**Function Summary: `split_code_into_blocks`**\n\n### Purpose and Functionality\nSplits the code block in the specified range into inner blocks, including contiguous regions without empty lines.\n\n### Key Implementation Details\n\n* Takes source code `_lines`, start index `_start_index`, end index `_end_index`, and `indent_level`.\n* Uses a regular expression to find the definition header.\n* Iterates through the code to find inner blocks using a regular expression.\n* Handles backtrace by including lines before the inner block's definition header.\n\n### Method Signatures and Interfaces\n\n* Takes a list of strings (`lines`) as input.\n* Returns a list of tuples containing string, start index, and end index (`List[Tuple[str, int, int]]`).\n\n### Notable Dependencies or Requirements\n\n* Uses the `re` module for regular expressions.\n\n### Output Format\nReturns a list of tuples containing block names, start indices, and end indices.\n\n### Summary\n\nSplits code into inner blocks using a regular expression-based approach. It handles backtrace and returns a list of tuples containing block information."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:391-465
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 465, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "find_code_in_transformers", "start_line": 391, "type": "function", "chroma:document": "**Summary: `find_code_in_transformers` Function**\n\nThe `find_code_in_transformers` function finds and returns the source code of an object in the transformers library.\n\n### Purpose and Functionality\n\n* Takes an object name, base path (optional), and return indices as input arguments.\n* Returns either the object's source code or a tuple containing the source code, file lines, start index, and end index.\n\n### Key Implementation Details\n\n* Finds the module where the object lives by splitting the object name and checking existing files in the base path.\n* Parses the Python code to find the class or function specified by the object name, returning the source code if `return_indices` is False.\n\n### Notable Dependencies and Requirements\n\n* Uses external dependencies:\n\t+ `os` for file operations\n\t+ `re` for regular expression matching\n\t+ `transformers` library (not explicitly imported)\n* Inherited or implemented interfaces:\n\t+ None, but uses Python's built-in functions such as `open`, `readlines`, and `find_block_end`.\n\n### Method Signatures\n\nThe function takes the following input arguments:\n\n* `object_name`: str\n* `base_path`: str (optional)\n* `return_indices`: bool (optional)\n\nThe function returns a Union type containing either str or a tuple with the following elements:\n\t+ str: source code of the object, if `return_indices` is False.\n\t+ Tuple[List[str], int, int]: file lines, start index, and end index, if `return_indices` is True."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:468-490
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 490, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "replace_code", "start_line": 468, "type": "function", "chroma:document": "**Summary**\n\n`replace_code` function:\n\n* Replaces occurrences of `obj1` with `obj2` in `code`, optionally applying casing conversions (`all-casing` option).\n* Takes two inputs: `code` (string) and `replace_pattern` (string, formatted as \"with X1->X2,Y1->Y2,Z1->Z2\").\n* Utilizes regular expressions to perform replacements, leveraging `_re_replace_pattern.search()` and `re.sub()` functions.\n\n**Key Implementation Details**\n\n* Uses regular expression substitutions (`re.sub()`) to replace patterns in the input code.\n* Applies casing conversions for the \"all-casing\" option using additional `re.sub()` calls.\n* Utilizes `_re_replace_pattern.search()` to extract pattern groups (e.g., `obj1`, `obj2`, and `option`)."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:493-533
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 533, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "find_code_and_splits", "start_line": 493, "type": "function", "chroma:document": "**Code Summary: `find_code_and_splits` Function**\n\n**Purpose and Functionality:**\nThe `find_code_and_splits` function searches for the code of an object within a specified directory (`base_path`) and splits it into blocks. It retrieves previous results from a buffer to speed up the process.\n\n**Key Implementation Details:**\n\n*   Uses `find_code_in_transformers` to search for the code in the transformers library.\n*   Utilizes `split_code_into_blocks` to split the retrieved code into blocks based on indentation levels.\n*   Expects the `buffer` parameter to store previous results and retrieve them if available.\n\n**Notable Dependencies and Requirements:**\n\n*   `find_code_in_transformers`: Function from transformers library or custom implementation.\n*   `split_code_into_blocks`: Custom or third-party function for block splitting.\n*   `get_indent`: Custom or external function to calculate indentation levels."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:542-558
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 558, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "get_indent", "start_line": 542, "type": "function", "chroma:document": "**Function Summary**\n\nGetIndent Function: Finds the indent in the first non-empty line of code.\n\n**Primary Purpose:** Extracts the leading whitespace from the first non-empty line of code.\n\n**Key Implementation Details:**\n\n* Splits code into individual lines\n* Iterates through lines until a non-empty one is found\n* Uses regex to match and extract the leading whitespace\n\n**Notable Dependencies:**\n\n* `re` module for regular expression matching ( referencing `search()` method)\n* No external dependencies listed, but assumes Python environment with `str` and `list` data types.\n\n**Method Signature:** `get_indent(code: str) -> str`\n\nThis function is designed to be used in a context where code snippets need to be analyzed or processed before execution. It provides a concise way to extract the leading whitespace from the first non-empty line of code, making it easier to analyze or visualize indentation schemes."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:561-568
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 568, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "run_ruff", "start_line": 561, "type": "function", "chroma:document": "**Summary:**\n\n`run_ruff` is a function that runs the Ruff code formatter with either `check` or `format` functionality. It takes in code and configuration options, utilizes subprocess to execute the Ruff command, and returns the formatted output.\n\n**Key Implementation Details:**\n\n- Utilizes `subprocess.Popen` for executing the Ruff command\n- Supports both `check` (linting) and `format` (code reformattting)\n- Uses `pyproject.toml` as default configuration file\n\n**Dependences/Requirements:**\n\n- Ruff code formatter (`ruff`)\n- `pyproject.toml`\n\nNote: The function returns the formatted output from the standard output of the Ruff command."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:571-586
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 586, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "stylify", "start_line": 571, "type": "function", "chroma:document": "Here is a concise summary of the `stylify` function:\n\n**Summary:**\nConverts input Python code to be compatible with RUFF's formatting style.\n\n**Key Details:**\n\n* Formats input code using the `ruff format` command\n* Appends an empty class wrapper to preserve indentation if present\n* Returns the formatted code without the wrapped class prefix\n\n**Notable Dependencies:**\n\n* `run_ruff`: External function from external library (not shown)\n* `get_indent`: External function from external library (not shown)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:589-637
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 637, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "check_codes_match", "start_line": 589, "type": "function", "chroma:document": "**Function Summary**\n\n`check_codes_match`\n\nChecks if two versions of a code match with the exception of class/function name.\n\n**Purpose and Functionality**\n\nCompares two code strings, ignoring class/function names. Returns the index of the first line where there is a difference (if any) or `None` if they match.\n\n**Key Implementation Details**\n\n* Uses regular expressions to extract class/function names from header lines.\n* Compares each line in the code separately, ignoring the extracted names.\n\n**Notable Dependencies or Requirements**\n\n* Python's `re` module for regex pattern matching\n* Python's `Optional` type hinting\n\n**Return Value**\n\n`Optional[int]`: The index of the first difference or `None` (no difference)\n\n**Method Signatures and Interfaces**\n\n* `_re_class_match` and `_re_func_match` regular expression patterns for class/function name extraction.\n\nNote: This summary focuses on the most important information about the function, omitting minor details."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:640-830
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 830, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "is_copy_consistent", "start_line": 640, "type": "function", "chroma:document": "This is a function that compares two versions of a code file. It checks if the code has been modified and reports changes. The function takes into account various factors such as:\n\n* Overwriting existing files\n* Handling empty lines and whitespace differences\n* Respecting the original block order when ignoring certain blocks\n* Stylifying codes for comparison\n\nHere's a brief overview of what the function does:\n\n1. It reads two versions of the code file, comparing them line by line.\n2. If there are changes between the two versions, it reports these changes and tracks their corresponding indices in both versions.\n3. Depending on whether to overwrite existing files or not, it either makes the necessary modifications or simply returns a list of reported differences.\n4. It also provides an option for modifying the code file in-place if there are any detected changes.\n\nThe function aims to be efficient and accurate when comparing two versions of code and can handle a range of scenarios, from minor changes to major rewrites."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:833-863
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 863, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "check_copies", "start_line": 833, "type": "function", "chroma:document": "**Summary**\n\nThe `check_copies` function ensures that all files in specified directories (`TRANSFORMERS_PATH` and `MODEL_TEST_PATH`) are copy-consistent with their original versions. It checks for discrepancies by calling the `is_copy_consistent` function and reports any inconsistencies via an exception.\n\n**Key Takeaways**\n\n* Function: `check_copies`\n* Purpose: Validate file copies and report inconsistencies\n* Parameters:\n\t+ `overwrite`: Boolean flag to overwrite discrepancies (default: False)\n\t+ `file`: Optional path to a single file for manual inspection (defaults to None)\n\n**Implementation Details**\n\n* Iterates through a list of files in specified directories (`all_files`)\n* Uses `is_copy_consistent` function to check copy consistency for each file\n* Reports inconsistencies via an exception if overwrite is False and discrepancies are found\n\n**Notable Dependencies/Interfaces**\n\n* `os.path.join`\n* `glob`\n* ` TRANSFORMERS_PATH`\n* `MODEL_TEST_PATH`\n* `is_copy_consistent` (function, dependencies not specified)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:866-894
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 894, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "check_full_copies", "start_line": 866, "type": "function", "chroma:document": "**Function Summary: check_full_copies**\n\n Checks files indicated as full copies (`FULL_COPIES`) for consistency and raises an exception if the copies differ.\n\n**Functionality**\n\n* Compares source code with target copy\n* If the target copy differs, considers overwriting the target (if `overwrite` is True) or logs a difference (if `overwrite` is False)\n* Rises an exception if no overwrites is made and differences are found\n\n**Key Implementation Details:**\n\n* Uses dictionary `FULL_COPIES` to map source files to their corresponding target files\n* Reads file contents using open() with encoding=\"utf-8\"\n* Compares source code (`source_code`) with target code (`target_code`)\n* If the copies differ, uses a list to track differences (`diffs`)\n* Uses exception handling to raise an error if no overwrites is made and differences are found\n\n**Notable Dependencies/Requirements:**\n\n* Python 3.7+ (encoding support)\n* Makefile or Python script (utils/check_copies.py)\n\n**Important Method Signatures and Interfaces:**\n\n* `check_full_copies()`: Function to check files for consistency\n\t+ `overwrite**: Boolean flag to control overwrite behavior\n\t+ `FULL_COPIES`: Dictionary mapping source files to target files"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:897-933
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 933, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "get_model_list", "start_line": 897, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_model_list`: Extracts and returns the model list from a README file.\n\n**Purpose**: Finds the start and end markers of the model list in the specified file, reads between them, and returns the extracted list as a string.\n\n**Key Implementation Details:**\n\n* Reads the entire file into memory.\n* Uses two prompts (`start_prompt` and `end_prompt`) to determine the start and end indices of the model list.\n* Iterates through lines until it finds the end prompt, appending each line that starts with \"1.\" (presumably a model name) or has additional content beyond a single character.\n* Handles cases where the current line has more than one character after processing.\n\n**Dependencies and Interfaces:**\n\n* `os`: for file path manipulation\n* `REPO_PATH` (not shown in this code snippet): likely an environment variable or global constant containing the repository root directory."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:936-1023
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1023, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "convert_to_localized_md", "start_line": 936, "type": "function", "chroma:document": "**Function Summary**\n\n`convert_to_localized_md`\n\nCompares model list from main README to localized README and updates the latter with synchronized metadata.\n\n**Main Purpose and Functionality**\n\nThis function compares two model lists, one from the main README and another from a localized README. It synchronizes metadata such as titles, model links, paper affiliations, and authors between the two lists.\n\n**Key Implementation Details**\n\n* Uses regular expressions to capture metadata from English model descriptions.\n* Checks for matching models in both READMEs and updates the localized one if necessary.\n* Synchronizes title link, paper title, and its link (if found) using regex patterns.\n\n**Notable Dependencies or Requirements**\n\n* `re` module for regular expression operations.\n* `Tuple` type hint to indicate return value format.\n\nNote: The original function has been preserved with the most important details. Note that functions like `format_model_list`, `LOCALIZED_READMES`, and `_re_capture_title_link` are not directly analyzed here as they appear to be referenced components instead of internal implementation details."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py:1026-1057
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1057, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_copies.py", "name": "_find_text_in_file", "start_line": 1026, "type": "function", "chroma:document": "**Function Summary:**\n\n`_find_text_in_file` function finds text in a file between two prompts.\n```python\nimport typing as t\nfrom typing import Tuple, List\n\ndef _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> Tuple[str, int, int, List[str]]:\n    \"\"\"\n    Args:\n        filename (str): File to search in.\n        start_prompt (str): String introducing the content searched for.\n        end_prompt (str): String marking the end of the content searched for.\n\n    Returns:\n        A tuple containing:\n            - The extracted text.\n            - The index of the start line in the original file.\n            - The index of the end line in the original file.\n            - The list of all lines in the file.\n    \"\"\"\n```\n\n**Key Implementation Details:**\n\n* Finds start and end indices by searching for prompt strings.\n* Handling cases where prompts may be on empty lines or follow multiple empty lines.\n\n**Notable Dependencies and Requirements:**\n\n* `open` function from Python's standard library for reading files.\n* `re` module is not explicitly imported, but it might be implicitly used through string methods."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_self_hosted_runner.py:5-28
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 28, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_self_hosted_runner.py", "name": "get_runner_status", "start_line": 5, "type": "function", "chroma:document": "**Code Summary**\n\n### Function Purpose\n\n`get_runner_status` retrieves the status of GitHub Actions runners from the GitHub API.\n\n### Key Implementation Details\n\n* Fetches runner status using `curl` and Parses the response with JSON\n* Compares runner names against `target_runners` list to retrieve offline runnersonly\n* Saves result to `offline_runners.txt`\n\n### Notable Dependencies/Requirements\n\n* `github-api`: implicit, accessed via `https://api.github.com/repos/huggingface/transformers/actions/runners`\n* `json` and `subprocess` modules for standard I/O and execution\n* `Authorization` token for authentication (Bearer `token`)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_support_list.py:37-61
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 61, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_support_list.py", "name": "check_flash_support_list", "start_line": 37, "type": "function", "chroma:document": "**Function Summary: `check_flash_support_list`**\n\nPurpose:\nThis function checks if FlashAttention-2 (FA2) support is included for various architectures based on documentation and code patterns.\n\nMain Functionality:\n\n1. Reads documentation text to extract supported architectures.\n2. Searches for model files with FA2 support in `modeling*` pattern and initializes a list of supported architectures (`archs_supporting_fa2`).\n3. Compares the extracted supported architectures against the ones listed in FA2 documentation and raises a ValueError if any mismatch.\n\nKey Implementation Details:\n\n- Uses glob to search for files under `src/transformers/models` with models containing \"_supports_flash_attn_2 = True\".\n- Utilizes string splitting to extract relevant text from documentation files.\n- Compares lists of supported architectures using set operations.\n\nNotable Dependencies and Requirements:\n- `os.path.join` and `open` functions for file system interactions\n- `glob` function for pattern matching\n- `REPO_PATH` constant referencing the repository's root path\n- External dependency: FlashAttention documentation (`perf_infer_gpu_one.md`) and model files containing \"_supports_flash_attn_2 = True\"."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_support_list.py:64-91
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 91, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_support_list.py", "name": "check_sdpa_support_list", "start_line": 64, "type": "function", "chroma:document": "**Functionality Summary:**\n\nThe `check_sdpa_support_list` function checks if various Transformer models support SDPA (Software Distributed Performance Architecture) inference and training.\n\n### Main Purpose:\n\n1. Retrieves documentation text from a file (`docs/source/en/perf_infer_gpu_one.md`) to determine which architectures are supported by SDPA.\n2. Iterates through model files in the `src/transformers/models` directory and extracts model names that contain the line `_supports_sdpa = True`.\n3. Verifies if each detected model is listed in the retrieved documentation text, raising a ValueError if any are not found.\n\n### Key Implementation Details:**\n\n* The function uses regular expressions to extract specific lines from the documentation text.\n* It utilizes glob to find model files matching certain naming patterns (`modeling_*.py`) and excludes files using `FlashAttention`.\n* Model names are appended to a list (`archs_supporting_sdpa`) based on the presence of the `_supports_sdpa = True` line.\n\n### Notable Dependencies and Requirements:**\n\n* The function relies on the following external dependencies:\n\t+ `os.path.join()` for path manipulation\n\t+ `fnmatch` for glob pattern matching (not explicitly required, as Python's built-in `glob` module is used)\n\t+ `re` for regular expression operations (not explicitly required, but implied by string splitting and searching)\n\n### Contextual Considerations:\n\nThis function appears to be part of a automated documentation checking system, verifying the support list of various Transformer models against actual implementation."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:51-67
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 67, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "handle_test_results", "start_line": 51, "type": "function", "chroma:document": "**Summary:**\n\n`handle_test_results` function processes test results by:\n\n* Splitting into individual expressions containing \"failed\" and \"passed\"\n* Calculating total failed and success tests count\n* Extracting time spent in the output string, assuming it's surrounded by \"= =\" for short outputs\n\n**Implementation Details:**\n\n* Iterates through expressions using `enumerate`\n* Uses exact method signaturs to access values: `int(expression[i - 1])` \n\n**Dependencies/Requirements:**\n\n* Presumes test results are comma-separated strings\n* Expects output string to contain a specific format (`\"{value} = {expression}\"`)\n* No external dependencies listed, but assumes `test_results` input is valid."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:70-88
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 88, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "handle_stacktraces", "start_line": 70, "type": "function", "chroma:document": "**Function Summary**\n\n`handle_stacktraces`: Processes test results to extract and format error messages from stacktraces.\n\n### Functionality\n\n* Takes `test_results` as input\n* Splits input into lines, excluding the first and last (header/footer)\n* Iterates through each line, extracting:\n\t+ Line number from path element before first space character\n\t+ Error message from remaining path elements \n* Returns a list of formatted error messages\n\n### Key Implementation Details\n\n* `total_stacktraces` is extracted using `split(\"\\n\")`\n* Uses indexing to extract line and error parts of each stacktrace\n* Catches exceptions during processing, appending \"Cannot retrieve error message.\" if an error occurs\n\n### Notable Dependencies\n\n* No external dependencies referenced\n* Assumes input test results are structured according to specified architecture"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:91-100
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 100, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "dicts_to_sum", "start_line": 91, "type": "function", "chroma:document": "**Function Summary**\n\n`dicts_to_sum` function:\n\n* Takes a single `objects` parameter with two acceptable inputs:\n\t+ A dictionary of dictionaries (`Dict[str, Dict]`)\n\t+ A list of dictionaries (`List[dict]`)\n* If input is a dictionary, it extracts the list of inner dictionaries.\n* It uses `Counter` from `collections` to count occurrences of each key-value pair in each dictionary and then maps this counter across all lists using `functools.reduce` with `operator.add`, summing up the results.\n\n**Key Implementation Details:**\n\n* Uses `collections.Counter` for efficient counting\n* Applies a cumulative reduction using `functools.reduce` and `operator.add`\n\n**Notable Dependencies/R Requirements:**\n\n* `unico` (not 'Union', assumed typo, corrected to `Union`, actual is not present in this code snippet)\n    * No explicit dependencies, but it utilizes:\n        + `collections.Counter`\n        + `functools.reduce`\n        + `operator.add`\n        + Assume a dictionary `Dict[str, Dict]` for conversion from other list format or use `List[dict]` as stated in the documentation"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:103-824
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 824, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "Message", "start_line": 103, "type": "class", "chroma:document": "The provided code is a part of a Slackbot designed to report test results from machine learning jobs. The bot fetches the results from a database and formats them into threads that are sent as separate messages on Slack.\n\nKey Features:\n\n*   **Results Reporting:** The bot fetches and formats test results, which can be obtained from a database, into thread-like structures suitable for sending to Slack.\n*   **Timing Mechanism:** There is a one-second delay between each post operation, which implies multiple posts per minute, given the rate of results and other operations like sleeping between the posts.\n*   **Error Handling:** Missing results, CI run artifacts being None, or missing models' names are error conditions that need to be solved. Proper exception handling would greatly enhance this part's robustness.\n\nImprovement Suggestions:\n\n1.  Add Exception Handling:\n    *   Consider raising exceptions instead of silently failing on certain operations like fetching results where `ci_results_{job_name}` does not exist.\n2.  Optimize Post Operations:\n    *   Currently, you're sending a separate post for each model fail and its failure rate. This might be inefficient due to sending large blocks or multiple posts. Simplify your strategy if possible.\n\nHere's an example implementation of error handling with exceptions:\n\n```python\ntry:\n    # your operations here...\nexcept Exception as e:\n    print(f\"An error occurred: {e}\")\n```\n\nRemember that adding too many checks without balancing the code readability can make it difficult to understand and lead to better design choices. So pick wisely where you add exception handling to improve robustness."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:827-842
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 842, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "retrieve_artifact", "start_line": 827, "type": "function", "chroma:document": "**Function Summary**\n\n`retrieve_artifact`: Retrieves artifacts from a specified path.\n\n**Purpose and Functionality**\nExtracts artifact files from the given `artifact_path`, including file contents in decoded format. Handles invalid GPU configurations and non-existent paths.\n\n**Key Implementation Details**\n\n* Checks valid GPU configurations (`single`, `multi`, or `None`).\n* Uses `os.listdir` to get a list of files in the specified path.\n* Iterates through each file, attempts to open and read its contents using `with` statement.\n* Raises error on non-existent paths and fails to decode Unicode characters.\n\n**Notable Dependencies and Requirements**\n\n* `[os]` module for interacting with the file system.\n* `Optional[str]` type hint for indicating that `gpu` can be `None`.\n* `Unicode Decode Error` handling raised by Python."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:845-894
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 894, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "retrieve_available_artifacts", "start_line": 845, "type": "function", "chroma:document": "**Summary: `retrieve_available_artifacts` Function**\n\n retrieves and manages available artifacts, which are stored in the `_available_artifacts` dictionary.\n\n\n*   **Main purpose**: This function iterates through directories on disk and populates an internal data structure with artifact information.\n*   **Key implementation details**:\n    *   The `_available_artifacts` dictionary is populated with `Artifact` objects, where each `Artifact` represents a directory-based asset.\n    *   Directory names are used to determine the type of GPU required for the asset (single or multi).\n    *   Assets are associated with paths and gpu types using the `add_path` method.\n*   **Notable dependencies**:\n    *   `os` module for interacting with file systems\n    *   `filter` function from `itertools` for filtering directories\n    *   `Dict` ( dictionary ) type hint\n\n\n(Note: In a RAG system, this summary can be further used as metadata by developers to understand the functionality and implementation of this code chunk.)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py:897-917
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 917, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service.py", "name": "prepare_reports", "start_line": 897, "type": "function", "chroma:document": "**Function Summary: prepare_reports**\n\n**Purpose:** Generate a formatted report string with truncated content if necessary.\n\n**Primary Functionality:**\nPrepares a report by combining input `title`, `header`, and `reports` into a single string, truncating the output if it exceeds 3000 characters due to \"[Truncated]\" addition.\n\n**Key Implementation Details:**\n\n* Iterates through each report in descending order using `_report = header + \"\\n\".join(reports[: idx + 1])`.\n* Checks for truncation conditions inside an `if` block (`if len(new_report) > MAX_ERROR_TEXT:`).\n\t+ Truncates the output with \"[Truncated]\" if necessary.\n\n**Notable Dependencies/Requirements:**\n\n* No external dependencies listed.\n* Uses specific constants:\n\t+ `MAX_ERROR_TEXT` defined as a fixed value (3000 - 1).\n\nNote this summary excludes unnecessary or generic details, focusing on the primary purpose and key implementation mechanics of the provided function."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py:246-350
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 350, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py", "name": "check_attribute_being_used", "start_line": 246, "type": "function", "chroma:document": "**Summary**\n\n`check_attribute_being_used` function checks if any name in `attributes` is used in one of the strings in `source_strings`. It inspects source code for usage of attribute names, including variants like `getattr()`, and flags its presence or absence.\n\n**Main Purpose and Functionality**\nInspect source code for attribute usage (`config.xxx`, `getattr()` constructs)\n\n**Key Implementation Details**\n\n* Check for direct attribute mentions (`f\"config.{attribute}\" in modeling_source`)\n* Detect multi-line `getattr()` patterns\n* Handle specific use cases like `TextConfig` and `SequenceSummary`\n\n**Notable Dependencies or Requirements**\n- `re` (regular expression library)\n- `config_class` with its specific class methods (`__init__`, `get_text_config()`)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py:353-396
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 396, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py", "name": "check_config_attributes_being_used", "start_line": 353, "type": "function", "chroma:document": "**Summary: `check_config_attributes_being_used` Function**\n\nThis function checks if the configuration class's initialization arguments are used in the same directory's modeling files. It does this by:\n\n1. Analyzing the init signature of the config class, identifying parameter names and default values.\n2. Checking for existence and usage of attribute maps, which can map variant names to different attribute names.\n3. Scanning model source files to detect attribute appearances that match the calculated attributes.\n\n**Key Implementation Details:**\n\n* Checks `attribute_map` for variants, allowing one usage to pass even if multiple variants exist.\n* Utilizes `inspect.signature` and `os.path.join` for file path manipulation.\n* Calls `check_attribute_being_used` internally to verify attribute usage against modeling sources.\n\n**Notable Dependencies/Requirements:**\n\n* `inspect`: For accessing signature information and getting source files.\n* `os`: For handling file paths and directories.\n* `config_class`, which is expected to have an `attribute_map` dictionary.\n\nThis function returns a list of unused attributes, sorted for consistency."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py:399-426
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 426, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_config_attributes.py", "name": "check_config_attributes", "start_line": 399, "type": "function", "chroma:document": "**Function Summary:**\n\n`check_config_attributes`: Checks if configuration class arguments in `__init__` are used in corresponding Python files and raises an error for unused attributes.\n\n**Key Implementation Details:**\n\n* Iterates through all configuration classes and their submodules to identify potential issue sources\n* Uses `check_config_attributes_being_used` function to detect unused attributes in each configuration class\n\n**Notable Dependencies/Requirements:**\n\n* `CONFIG_MAPPING`: a dictionary mapping configuration classes (not defined in provided code snippet)\n* `PretrainedConfig` class (assumed to be defined elsewhere)\n\n**Primary Purpose:** To ensure that configuration class arguments are consistently used in corresponding Python files."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:12-35
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 35, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_jobs", "start_line": 12, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_jobs` extracts GitHub Actions workflow run jobs.\n\n**Purpose:**\nExtracts job data from a GitHub Actions workflow run API.\n\n**Key Implementation Details:**\n\n*   Uses the GitHub API to fetch job data with pagination.\n*   Utilizes the `requests` library to send GET requests and parse JSON responses.\n\n**Method Signatures:**\n\n*   `requests.get(url, headers=headers)`: Sends a GET request to the specified URL with provided headers.\n*   `.json()`: Parses the response as JSON.\n\n**Notable Dependencies:**\n\n*   `requests`\n*   `math` (for calculating total pages)\n\n**Interfaces Implemented:**\n\n*   GitHub API (`https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs`)\n\nThis function uses pagination to fetch job data from the GitHub Actions workflow run API. It handles exceptions and prints errors if an unknown error occurs during execution."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:38-61
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 61, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_job_links", "start_line": 38, "type": "function", "chroma:document": "**Function Summary: get_job_links**\n\n**Main Purpose and Functionality:**\nThe `get_job_links` function extracts job names and their corresponding GitHub Actions workflow run links from the Hugging Face Transformers repository.\n\n**Key Implementation Details:**\n\n* Uses GitHub API to fetch job data in paginated chunks (up to 100 jobs per page).\n* Supports token-based authentication for request headers.\n* Iterates over multiple pages of results to collect all job links.\n\n**Notable Dependencies and Requirements:**\n\n* Python 3.6+\n* `requests` library for HTTP requests\n* `math` library for calculating pagination parameters\n* Hugging Face Transformers repository GitHub API permissions ( token for authentication)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:64-87
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 87, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_artifacts_links", "start_line": 64, "type": "function", "chroma:document": "**Summary**\n\nThis function `get_artifacts_links` retrieves URLs for artifact downloads from GitHub Actions in Hugging Face's Transformers repository. It takes a workflow run ID and an optional authentication token.\n\n**Key Functionality:**\n\n*   Fetches GitHub API for archive download URLs of artifacts within a workflow run.\n*   Iterates over multiple pages to collect all required links until exhausted.\n*   Handles pagination, total count, and error cases.\n\n**Implementation Details:**\n\n*   Uses `requests` library with optional authentication token via `Authorization` header.\n*   Utilizes JSON parsing for receiving API response data.\n*   Performs dictionary updates for efficient collection of artifact URLs.\n\n**Notable Dependencies/Requirements:**\n\n*   `math` for calculating pages to iterate.\n*   `requests` and `json` for interacting with the GitHub API, handling responses.\n*   `traceback` for error reporting in case of an issue.\n\n**Primary Purpose:**\nTo fetch archive download URLs for artifacts from a specified GitHub Actions workflow run.\n\n**Key Method Signatures:**\n\n*   `requests.get()`: To fetch pages from the API.\n*   `dict.update()`: To add new entries to the dictionary of artifact links.\n*   `math.ceil()`: For calculating the number of pages to iterate over."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:90-106
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 106, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "download_artifact", "start_line": 90, "type": "function", "chroma:document": "**Function Summary: `download_artifact`**\n\nDownloads a GitHub Action artifact from a URL.\n\n**Primary Functionality:**\nThis function downloads a zip file from a GitHub Action artifact URL. It first retrieves the redirect URL and then downloads it to a specified output directory using the provided token for authentication.\n\n**Key Implementation Details:**\n\n* Uses the `requests` library to send HTTP requests\n* Sets headers with `Authorization` and `Accept` fields based on whether a token is provided\n* Sets `allow_redirects=False` initially, but switches to `True` when downloading the redirect URL\n* Retrieves the file path from the URL's `Location` header\n\n**Notable Dependencies:**\n\n* `requests`\n* `os`\n\n**Method Signatures and Interfaces:**\n- `requests.get()`: sends a GET request to a specified URL\n- `Authorization` flow for token authentication\n\nNote that interface implementation (i.e., how it interacts with the GitHub API) is limited, but this summary should give a clear overview of its main functionality and underlying mechanisms."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:109-153
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 153, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_errors_from_single_artifact", "start_line": 109, "type": "function", "chroma:document": "**Code Summary: `get_errors_from_single_artifact`**\n\nThe primary purpose of this function is to extract error messages and related test information from a ZIP archive downloaded as input.\n\n**Key Implementation Details:**\n\n* Uses the `zipfile` module to iterate over files in the ZIP archive.\n* Checks specific file contents (e.g., `failures_line.txt`, `summary_short.txt`, `job_name.txt`) for relevant data extraction.\n* Processes errors and failed test information using try-except blocks.\n* Raises an error if there's a mismatch between extracted error messages and failed tests.\n\n**Method Signatures and Interfaces:**\n\n* `get_errors_from_single_artifact(artifact_zip_path, job_links=None)`: The main function with two input parameters.\n* `zip.open(filename) as f`: Used to read file contents within the ZIP archive.\n* `job_links.get(job_name, None)`: Retrieves a job link based on a matching job name (if provided).\n\n**Notable Dependencies and Requirements:**\n\n* Python 3.x\n* `zipfile` library for working with ZIP archives.\n* External dependencies:\n\t+ `os` module for path operations.\n\n**Primary Purpose Statement:**\n\nExtracts errors from a downloaded artifact in XML format, mapping error lines to corresponding failed tests."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:156-165
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 165, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_all_errors", "start_line": 156, "type": "function", "chroma:document": "**Summary:**\n\nThis function retrieves all error messages from artifact files located at the specified `artifact_dir`. It searches for `.zip` files, extracts their error information using the `get_errors_from_single_artifact` function, and returns them in a list.\n\n**Key Implementation Details:**\n\n* Searches for `.zip` files recursively\n* Calls `get_errors_from_single_artifact` on each file with optional `job_links`\n* Extends errors list with results from all extracted artifacts\n\n**Notable Dependencies:**\n\n* `os.path.join`\n* `os.listdir`\n* `get_errors_from_single_artifact` ( referenced function, not shown in original snippet)\n\n**Primary Purpose:**\nExtract and return a list of error messages from artifact file zip archives."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:168-180
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 180, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "reduce_by_error", "start_line": 168, "type": "function", "chroma:document": "**Summary:**\n\nThe `reduce_by_error` function clusters test log errors into a dictionary, grouped by frequency and containing failed tests. It takes in:\n\n*   Logs with error codes as values\n*   Optional error filter list\n*   Counts each unique error using `Counter` and returns the results sorted by count in descending order.\n\n**Implementation Details:**\n\n*   Updates a `Counter` object to count unique errors from logs.\n*   Creates a dictionary (`r`) to store clusters of errors, where each cluster contains:\n    *   Error code\n    *   Count\n    *   Failed test(s) with original log index and error code\n*   Sorts the final dictionary by error count in descending order.\n\n**Dependencies/Requirements:**\n\n*   `Counter` from collections module for counting unique errors.\n*   List of expected error filter values (if provided)."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:183-191
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 191, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "get_model", "start_line": 183, "type": "function", "chroma:document": "**Summary:**\n\n* Function `get_model`: retrieves the model name from a test method or path.\n \n**Key Implementation Details:**\n \n* Splits input string by '::' and takes first part as model name\n* Checks if input starts with 'tests/models/' prefix, and splits by '/' to extract model name\n* Returns extracted model name, else returns None\n\n**Notable Dependencies/Requirements:**\n\n* None"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:194-213
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 213, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "reduce_by_model", "start_line": 194, "type": "function", "chroma:document": "**Function Summary:** `reduce_by_model`\n\n**Purpose:** Group and count errors by model, filtering out those excluded by the input `error_filter`.\n\n**Implementation Details:**\n\n* Uses list comprehension to enrich log data with the corresponding model type\n* Applies error filtering using `error_filter` if provided\n* Counts occurrences of each error in the `test` group using a `Counter`\n* Calculates the total number of errors for each test and stores them in a dictionary\n\n**Key Method Signatures:**\n\n* `most_common()`: Returns the most common values and their counts via `Counter`\n\n**Notable Dependencies/Requirements:**\n\n* Standard library: `collections.Counter`, function `get_model`\n* External dependency: None\n\n**Context:** This code appears to be part of a logging analysis workflow, grouping errors by model type for further analysis."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:216-225
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 225, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "make_github_table", "start_line": 216, "type": "function", "chroma:document": "**Function Summary: generateGitHubTable**\n\n**Purpose:** Generate a formatted table of GitHub errors with status and count.\n\n**Implementation Details:**\n\n* Uses a fixed header line with four columns.\n* Utilizes a separator line to split the data into rows.\n* Loops through each error in `reduced_by_error`, creating a single row per error with the error name truncated to 100 characters.\n* Joints all lines together using newline characters.\n\n**Key Features:**\n\n* `make_github_table` function takes a dictionary of errors as input (`reduced_by_error`).\n* Returns a string representing the formatted table.\n\n**Dependencies and Requirements:** \n\n* None explicitly defined, but assumes a Python environment with standard library access."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py:228-238
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 238, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_ci_error_statistics.py", "name": "make_github_table_per_model", "start_line": 228, "type": "function", "chroma:document": "**Summary**\n\n**Functionality**\nCreate a GitHub table per model with errors, displaying:\n- Model name\n- No. of errors\n- Major error description (truncated to 60 characters)\n- Error count\n\n**Implementation Details**\n- Iterates through `reduced_by_model` dictionary\n- Extracts key model data: error count and major error details\n- Builds table format string with dynamic values\n- Returns a formatted string with the table lines joined by newline (`\\n`)\n\n**Key Components**\n\n* `model`: reduced_by_model dictionary keys (model names)\n* `errors`: values for each model, containing error and count data\n\t+ error: first key-value pair item (assuming single major error per model)\n\t+ count: corresponding value\n\n**Notable Dependencies/Requirements**\n* None"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:70-85
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 85, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "update_version_in_file", "start_line": 70, "type": "function", "chroma:document": "**Summary:**\n\nUpdates the `version` parameter within a specified file of type `file_type`.\n\n**Key Implementation Details:**\n\n* Reads the contents of the file and replaces the `VERSION` placeholder with the provided `version`.\n* Uses regular expression substitution (`REPLACE_PATTERNS[file_type]`) to update the version in the code.\n* Writes the updated code back to the original file.\n\n**Notable Dependencies:**\n\n* Expects `REPLACE_PATTERNS` dictionary containing replacement patterns for different file types."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:88-103
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 103, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "update_version_in_examples", "start_line": 88, "type": "function", "chroma:document": "**Summary**\n\n`update_version_in_examples` function updates the version of Python files in all example files within the `PATH_TO_EXAMPLES` directory. It recursively walks through the directories, filtering out inactive projects and non-Python files.\n\n**Key Implementation Details**\n\n* Removes outdated project folders (\"research_projects\" and \"legacy\")\n* Iterates over file extensions (.py) to detect Python files\n* Calls `update_version_in_file` function for each Python file to update its version with the specified `version`\n\n**Notable Dependencies or Requirements**\n\n* Uses `os` module for walking directories and accessing paths\n* Utilizes `PATH_TO_EXAMPLES` environment variable, not defined in this code snippet.\n\n* `update_version_in_file` function is called but not defined within this scope. It's likely a separate function that updates the version of the specified file."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:106-118
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 118, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "global_version_update", "start_line": 106, "type": "function", "chroma:document": "**Function Summary:**\n\n`global_version_update`\n\nUpdates the specified version in all required files.\n\n* Parameters:\n\t+ `version`: New version string\n\t+ `patch`: Boolean indicating patch release (default: False)\n\nKey Implementation Details:\n\n* Iterates through `REPLACE_FILES` dictionary to update files with pattern matches.\n* Calls `update_version_in_file` method for each matched file.\n* Excludes updating examples if not a patch release.\n\nNotable Dependencies/Requirements:\n\n* `REPLACE_FILES` dictionary\n* `update_version_in_file` method\n* `update_version_in_examples` function"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:121-129
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 129, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "remove_conversion_scripts", "start_line": 121, "type": "function", "chroma:document": "**Summary:**\n`remove_conversion_scripts()` is a function that deletes Python scripts responsible for converting models from older, unsupported formats. The purpose is to prevent vulnerable file types from being included in release wheels, thereby avoiding compliance issues.\n\n**Key Details:**\n\n* Removes Python scripts (`convert*.py`) from the `PATH_TO_MODELS` directory using `globbing`.\n* Deletes each script found using `unlink()` after listing them for removal.\n\n**Notable Interface/Class:**\n\n* `Path`: provides file system path manipulation.\n* `glob()`: performs Unix shell-style string matching on paths.\n\n**External Dependencies:**\n\nNone noted."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:132-139
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "get_version", "start_line": 132, "type": "function", "chroma:document": "**Code Summary**\n\nRetrieves the current version from the `__init__.py` file and parses it into a `packaging.version.Version` object.\n\n**Key Details**\n\n* Reads content from `REPLACE_FILES[\"init\"]`\n* Searches for default version in `code` using regex pattern `REPLACE_PATTERNS[\"init\"][0]`\n* Returns parsed version using `packaging.version.parse()`\n\n**Dependencies**\n\n* Expects `packaging` library with `version` module\n* Uses regular expressions (`re`) with `search()` method"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:142-171
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 171, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "pre_release_work", "start_line": 142, "type": "function", "chroma:document": "**Function Summary: `pre_release_work`**\n\n### Main Purpose and Functionality\nThe `preReleaseWork` function performs necessary pre-release steps for maintaining version control and cleanup. It asks confirmation, updates versions, cleans up model lists in README files, and deletes conversion scripts.\n\n### Key Implementation Details\n\n* Returns a default minor release version if patching, otherwise increments the minor version.\n* Asks user confirmation to choose the release version.\n* Updates global `version` using `global_version_update`.\n* Removes conversion scripts using `remove_conversion_scripts`.\n\n### Notable Dependencies/Requirements\n\n* `get_version()` function\n* Global variables: `base_version`, `major`, `minor`, and `micro`\n\n### Interfaces Implemented/Inherited\n\n* None mentioned, but may be inherited from parent class or interface."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/release.py:174-192
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 192, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/release.py", "name": "post_release_work", "start_line": 174, "type": "function", "chroma:document": "**Summary: post_release_work Function**\n\n**Purpose and Functionality:**\nThe `post_release_work` function performs the necessary steps after a release, including:\n\n* Obtaining the current version\n* Confirming with the user that the next development version is correct\n* Updating all versions accordingly\n\n**Key Implementation Details:**\n\n* Calls `get_version()` to retrieve the current version.\n* Calculates the next development version by incrementing the minor version of the current version.\n* Queries the user to confirm the chosen version using `input()`.\n* Updates global versions using `global_version_update(version)`.\n\n**Notable Dependencies and Requirements:**\nNo external dependencies mentioned. However, it is assumed that:\n\n* `get_version()` function is available and returns a Version object with attributes such as `major`, `minor`, `base_version`.\n* `global_version_update()` function is available and accepts a version string as an argument.\n\n**Interface and Inheritance:**\n\n* Implements no explicit interfaces. However, it assumes the existence of custom attributes for the `Version` class returned by `get_version()`."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py:29-35
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 35, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py", "name": "count_lines", "start_line": 29, "type": "function", "chroma:document": "**Summary:**\n\n`count_lines` function counts the number of lines in a given file.\n \n**Key Implementation Details:**\n- Reads a file line by line using `open` and stores content in a variable (`f.read()`).\n- Splits content into lines using newline character (`\"\\n\"`).\n- Returns the count of lines.\n\n**Notable Dependencies:**\n- [Filesystem module](https://docs.python.org/3/library/os.html#module-os) for file operations.\n- This function does not use any external Python libraries, it is a fundamental file handling task."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py:38-43
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py", "name": "compute_parallel_nodes", "start_line": 38, "type": "function", "chroma:document": "**Summary:**\n\n* **Main Purpose:** Computes the minimum number of parallel nodes required to execute a given line count of code with a specified maximum tests per node.\n\n* **Key Implementation Details:**\n  - Calculates `num_nodes` based on `line_count` and average tests per nodes.\n  - Applies a soft cap at 1 node for small line counts (<4) to prevent division by zero errors.\n\n* **Notable Dependencies/Requirements:**\n  - `math.ceil()`: used to calculate ceiling of the division result\n  - `AVERAGE_TESTS_PER_NODES`, `MAX_PARALLEL_NODES`: constants imported from parent module, representing average tests per nodes and maximum parallel nodes allowed, respectively."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py:46-68
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 68, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/process_test_artifacts.py", "name": "process_artifacts", "start_line": 46, "type": "function", "chroma:document": "**Function Summary: `process_artifacts`**\n\n### Main Purpose and Functionality\n\nThe `process_artifacts` function reads JSON data from an input file, transforms the data based on specific rules, and writes the transformed data to an output file. The primary purpose is to process artifacts related to test preparation.\n\n**Transformation Rules:**\n\n* If an item's path contains a \"test_list\" key, extract the key by removing the file extension using `os.path.basename`.\n* Compute parallelism levels based on count of nodes in corresponding text files using `compute_parallel_nodes`.\n* Store transformed data in a new JSON structure with `transformed_data`.\n\n**Key Implementation Details:**\n\n* Uses `json.load` to read input JSON file.\n* Utilizes `count_lines` function to compute parallelism levels.\n\n**Notable Dependencies or Requirements:**\n\n* External dependency on the `os`, `json`, and `file_utils` (assuming `compute_parallel_nodes` is an external function).\n* Assumed implementation of `compute_parallel_nodes` function is not shown in this code snippet.\n\n### Additional Information:\n\nThe transformed data will be written to the output file using `json.dump`. The `\"generated_config\"` key from the input data will be removed if present during transformation."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/patch_helper.py:43-53
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 53, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/patch_helper.py", "name": "get_merge_commit", "start_line": 43, "type": "function", "chroma:document": "**Summary:**\n\n### Purpose:\n\nThis function retrieves the merge commit in a GitHub repository (using `git` commands) for a specific pull request (PR), given a tag range (`since_tag`).\n\n### Functionality:\n\n1. **Searches for merge commit**: Uses `repo.iter_commits` to find the first commit that matches the PR number within the specified tag range.\n2. **Catches errors**:\n\t* `StopIteration`: No matching commit found; returns `None`.\n\t* `GitCommandError`: Error during command execution (e.g., invalid syntax); prints error message and returns `None`.\n\n### Key Details:\n\n* Method: `repo.iter_commits`\n* Git log filter: `f\"v{since_tag}...origin/main and grep=f#{pr_number}\"`\n* Returns a single merge commit object on success or `None` on failure.\n\n**Notable Dependencies:** None\n\n**Interfaces and Requirements:** Uses the `GitCommandError` exception class, which is part of GitHub's Git API. The repository object (`repo`) is assumed to have an `iter_commits` method."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/patch_helper.py:56-84
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 84, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/patch_helper.py", "name": "main", "start_line": 56, "type": "function", "chroma:document": "**Summary:**\n\n### Main Functionality\n\nThe `main` function takes a list of PR numbers and generates Git cherry-pick commands to apply merge commits across tag ranges.\n\n**Key Implementation Details:**\n\n* **Git Repository Management**: The function initializes a Repo object for the current directory and iterates through GitHub tags to parse and sort valid version tags.\n* **Tag Range Iteration**: It finds merge commits by iterating through PR numbers, splitting them into commit hashes, and using `get_merge_commit` function to retrieve them from the Git repository.\n\n**Notable Dependencies or Requirements:**\n\n* `Repo()` object (assumed to be a GitHub API client)\n* External dependency on `version.parse()` for tag validation\n* Inherited interface from `get_merge_commit()` function, which is not provided in this code snippet\n\n### Code Summary:\n\n```\ndef main(pr_numbers):\n    # Initialize Repo and manage tags\n    repo = Repo(\".\")  # ...\n    tags = {}  # ...\n\n    # Find merge commits across PR tag ranges\n    for pr in pr_numbers:\n        commit = get_merge_commit(repo, pr, major_minor)\n        if commit:\n            merge_commits.append(commit)\n\n    # Sort commits by date and output commands\n    merge_commits.sort(key=lambda commit: commit.committed_datetime)\n    print(\"Git cherry-pick commands to run:\")\n    for commit in merge_commits:\n        print(f\"git cherry-pick {commit.hexsha} #{commit.committed_datetime}\")\n```\n\nThis summary aims to preserve the essence of the original code while omitting minor details and non-essential information."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:125-154
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 154, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "clean_code", "start_line": 125, "type": "function", "chroma:document": "**Function Summary:**\n\n`clean_code(content: str) -> str`\n\nRemoves docstrings, empty lines, and comments from the provided code content to detect true changes.\n\n**Implementation Highlights:**\n\n* Uses triple quotes (`\"\\\"\\\"\\\"\"`, `'\\'\\'\\''`) to split out docstrings\n* Removes empty lines by checking for non-whitespace characters\n* Removes comments using regular expression substitution (`#.*$`)\n\n**Notable Dependencies/Requirements:**\n\n* None listed, but likely uses built-in Python modules such as `re` (regular expressions)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:157-181
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 181, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "keep_doc_examples_only", "start_line": 157, "type": "function", "chroma:document": "**Function Summary: `keep_doc_examples_only`**\n\nRemoves non-documentation examples from provided code content, leaving only comments.\n\n**Key Implementation Details:**\n\n*   Splits input content by triple quotes (`\"\"\"`) to isolate documentation sections.\n*   Trims leading and trailing whitespace from each line in the split content to eliminate unnecessary characters.\n*   Removes commented-out lines by removing everything after a `#` sign using regular expressions.\n*   Returns the cleaned code content as a single string.\n\n**Notable Dependencies or Requirements:**\n\n*   None found.\n\n**Return Value Signature:**\n```python\ndef keep_doc_examples_only(content: str) -> str:\n```\n\n**Main Functionality:**\nThe function's primary purpose is to filter out non-documentation examples in the input code content, leaving only comments. The filtered comments can then be used to trigger doc tests or documentation checks without including unnecessary code snippets."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:184-210
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 210, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_all_tests", "start_line": 184, "type": "function", "chroma:document": "**Summary**\n\nThe `get_all_tests` function generates a list of files and subfolders to run tests, leveraging parallelism. It walks the `tests` folder, excluding subfolders like `models` and only including specific ones like `tokenization` and `pipelines`. Model-specific test folders (`bert`, `gpt2`) are also included.\n\n**Key Implementation Details**\n\n* Returns the following types of contents:\n\t+ Directly under `tests`: folders with directories or files starting with `test_`.\n\t+ Under `tests/models`: model-specific folder names.\n* Sorts and excludes certain items:\n\t+ Removes `.pyc` files from subfolders under `tests`.\n\t+ Excludes `models` subfolder from the main `tests` list.\n\t+ Removes the `sagemaker` package if found in the main test list.\n\n**Notable Dependencies or Requirements**\n\n* Uses `os.listdir` to interact with the file system.\n* References `PATH_TO_REPO`, `PATH_TO_TESTS`, and `SAGEMAKER Package` by exact name."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:213-236
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 236, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "diff_is_docstring_only", "start_line": 213, "type": "function", "chroma:document": "**Summary:**\n\n`diff_is_docstring_only` function checks if the diff between two commits in a Git repository is only docstrings and/or whitespace modifications in a specified filename.\n\n**Key Implementation Details:**\n\n* Compares old and new commit content using `clean_code()` function.\n* Uses `git.Repo` to access working directory and checkout specific branching point.\n* Opens files in same encoding for consistency.\n\n**Return Value:**\n\nReturns `bool` indicating whether the diff is docstring/comments only or not. \n\n**Notable Dependencies:**\n\n* `git.Repo`\n* `Path` from `pathlib`\n* `clean_code()` function (exact implementation unknown)"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:239-262
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 262, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "diff_contains_doc_examples", "start_line": 239, "type": "function", "chroma:document": "**_summary_**\n\n`diff_contains_doc_examples(repo, branching_point, filename)`: Compares two versions of a file within a Git repository to determine if the diff is exclusively code examples.\n\n**Key details:**\n\n* **Functionality**: Exclusively checks for changes in code snippets versus documentation examples.\n* **Implementation**:\n\t+ Retrieves file contents from working directory using `checkout_commit` and `open`.\n\t+ Applies `keep_doc_examples_only` function to clean up content by removing non-example sections.\n\t+ Returns `True` if differing example content is found, otherwise `False`.\n\n**External dependencies:**\n\n* `git.Repo`\n* `Path`\n* `keep_doc_examples_only` (assumed to be implemented elsewhere)\n\n**Interfaces/Methods used:**\n\n* `git.Repo.checkout_commit(branching_point)`\n* `github.Path.open(filename, encoding=\"utf-8\")`\n\nNote that specific implementation details like the `keep_doc_examples_only` function are assumed to be implemented elsewhere in the codebase."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:265-378
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 378, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_impacted_files_from_tiny_model_summary", "start_line": 265, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_impacted_files_from_tiny_model_summary` function returns a list of Python modeling files impacted by changes in `tiny_model_summary.json` between the current head and main branch. If `diff_with_last_commit=False`, it looks for changes in main branch; otherwise, it considers parent commits.\n\n**Key Functionality:**\n\n* Compares `tiny_model_summary.json` contents across multiple commits\n* Identifies changes to model classes and their defined modules\n* Uses a temporary `_import_structure` module to resolve modeling file paths\n\n**Implementation Details:**\n\n* Utilizes Git repository and merge_base functionality\n* Calculates differences between old and new JSON content using symmetric difference and intersection\n* Handles common keys with differing content separately\n* Resolves model class to module mapping using `_import_structure`\n* Uses temporary directory and importlib utilities to load modules and get internal file paths\n\n**Notable Dependencies:**\n\n* `git` for repository access and merge_base functionality\n* `json` and `re` libraries for JSON parsing and regular expression pattern matching\n* `tempfile` and `importlib` libraries for creating temporary directories and loading modules"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:381-420
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 420, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_diff", "start_line": 381, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_diff` function calculates the differences between a base commit (`base_commit`) and one or more commits (`commits`). It returns a list of Python files that show differences, including added, renamed, or deleted files.\n\n**Main Purpose and Functionality:**\n\n* Calculates Git diff between base commit and commit pairs\n* Filters out changes only in docstrings or comments in modified files\n\n**Key Implementation Details:**\n\n* Iterates over commits and calculates diffs using `basecommit.diff`\n* Adds new Python files (`change_type == \"A\"`), deletes old Python files (`change_type == \"D\"`), and handles modifications with rename detection (`diff_obj.change_type in [\"M\", \"R\"]`)\n* Uses `diff_is_docstring_only` function to check modifications are not only in docstrings or comments\n\n**Notable Dependencies and Requirements:**\n\n* `git.Repo`: A Git repository object\n* `diff_is_docstring_only`: a separate function (not included in this snippet) that determines if changes are only in docstrings or comments"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:423-450
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 450, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_modified_python_files", "start_line": 423, "type": "function", "chroma:document": "Here's a concise summary of the provided code chunk:\n\n**Function Summary:** `get_modified_python_files`\n\n**Purpose and Functionality:** Returns a list of Python files modified between the current head and either its main branch or previous commit (`diff_with_last_commit` flag).\n\n**Key Implementation Details:**\n\n* Uses Git repository manipulation to determine branching commits (if not using last commit)\n* Calculates differences in modified files with `get_diff(repo, repo.head.commit, [commit])` method\n* Prints intermediate commit information for debugging purposes\n\n**Notable Dependencies and Requirements:**\n\n* Git library (`Repo`) for interacting with the repository\n* `get_diff` function (not shown) for calculating file diffs"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:453-491
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 491, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_diff_for_doctesting", "start_line": 453, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_diff_for_doctesting` function generates a list of Python and Markdown files with differences between a base commit (`base_commit`) and one or several commits (`commits`). It focuses on changes to doc examples, returning modified files that contain doc examples.\n\n**Key Details:**\n\n*   **Function Signature:** `def get_diff_for_doctesting(repo: Repo, base_commit: str, commits: List[str]) -> List[str]`\n*   **Primary Purpose:** Identify and return Python and Markdown files with differences between base commit and comparison commits.\n*   **Implementation Details:**\n    *   Iterates over each commit in the list of commits.\n    *   Compares diff object of each commit against the base commit.\n    *   Applies filtering on file types (python.py, md) to exclude irrelevant changes.\n    *   Returns modified files containing doc examples using the `diff_contains_doc_examples` function.\n\n**External Dependencies:**\n\n*   `git.Repo`: Git repository class for retrieving information about commits.\n*   `List[str]`: List of commit IDs for comparison.\n*   `Repo` and other classes not mentioned here are assumed to be external dependencies."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:494-524
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 524, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_all_doctest_files", "start_line": 494, "type": "function", "chroma:document": "**Summary**\n\nThe `get_all_doctest_files` function retrieves the complete list of Python and Markdown files to run doctest. It filters out files based on their location, extensions, and excluded directories.\n\n*   **Purpose:** Returns a lista of paths by filtering python, markdown file from repository.\n*   **Functionality:**\n    *   Uses `glob` to find `.py` and `.md` files in `src/` or `docs/source/en/`.\n    *   Excludes files in `utils/not_doctested.txt`, `src/transformers/models/deprecated`, and init files.\n    *   Filters out files not marked for testing (`not_doctested.txt`).\n*   **Key Implementation Details:**\n    *   Uses list comprehensions to filter and transform file paths.\n    *   Checks file names against exclusion lists for filtering.\n\n**Dependencies/Requirements:**\n\n*   `glob`\n*   `os.path` (specifically, `relative_to`, `Partition`)\n*   `pathlib`\n\n**Notable Variables:**\n\n*      `PATH_TO_REPO`: repository path\n*      `_` not variable but alias\n\n**Interfaces/Superclasses:**\n\nNone\n\nThis code snippet appears to be part of a larger class or module that handles file system operations and doctest filtering."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:527-551
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 551, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_new_doctest_files", "start_line": 527, "type": "function", "chroma:document": "**Concise Summary**\n\n`get_new_doctest_files` function takes three commit references (`repo`, `base_commit`, and `branching_commit`) and returns a list of files that were removed from \"utils/not_doctested.txt\" between these commits.\n\n**Key Insights:**\n\n* The function loads the old and new contents of \"utils/not_doctested.txt\" for each merge commit.\n* It computes the removed lines by taking the difference between the two versions.\n* Notable dependencies:\n\t+ `Path` from `pathlib`\n\t+ `checkout_commit` from GitPython\n\t+ `diff` from GitPython\n\n**Return Value:** A list of strings representing the files that were removed from \"utils/not_doctested.txt\"."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:554-599
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 599, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_doctest_files", "start_line": 554, "type": "function", "chroma:document": "**Function Summary**\n\n`get_doctest_files(diff_with_last_commit: bool = False) -> List[str]`\n-------------------------\n\nThis function generates a list of Python and Markdown files with modified doctest examples between the current head commit and either:\n\n*   The main branch (if `diff_with_last_commit=False`) or\n*   Its parent commits (otherwise)\n\nThe function performs the following steps:\n\n1.  Checks if there are any modified doctest examples in the given commit range.\n2.  Identifies added, renamed, or modified files containing doctest examples.\n3.  Excludes slow documenting tests to be run on CircleCI.\n4.  Ensures that no removed test files are included.\n\n**Key Implementation Details**\n\n*   Uses `Repo` class from [GitPython](https://gitpython.readthedocs.io/en/stable/) for Git repository management.\n*   Utilizes `get_diff_for_doctesting` function to identify modified doctest examples.\n*   Employs set operations and filtering for efficient list construction.\n\n**Notable Dependencies or Requirements**\n\n*   [GitPython](https://gitpython.readthedocs.io/en/stable/) library for Git repository interactions\n*   External dependencies not specified in the provided code snippet."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:623-702
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 702, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "extract_imports", "start_line": 623, "type": "function", "chroma:document": "**Function Summary: `extract_imports`**\n\nExtracts the imports made by a given module from its source code.\n\n**Purpose**\nThe function takes a file name of a module and returns a list of tuples containing the imported module filenames and their corresponding import statements.\n\n**Key Implementation Details**\n\n1. **Relative Imports**: Extracts relative imports using regular expressions, filtering out docstrings and `tests_ignore` comments.\n2. **Direct Imports**: Finds direct imports by parsing lines similar to `import * or from ... import`. The function ensures that only actual import statements are extracted.\n3. **Cache Mechanism**: Stores previously computed results for module names to avoid redundant computations.\n\n**Notable Dependencies and Requirements**\n\n1. `os`: for interacting with the file system.\n2. `re`: for regular expression matches.\n3. `_re_single_line_relative_imports`, `_re_multi_line_relative_imports`, `_re_single_line_direct_imports`, and `_re_multi_line_direct_imports` (custom regexes).\n4. `PATH_TO_REPO` (absolute path to the repository root).\n\n**Primary Purpose**: Extracts imports from a module's source code, providing the necessary information for automated analysis or testing.\n\n```python\ndef extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> List[Tuple[str, List[str]]]:\n    # ...\n```\n\nNote that this summary highlights the key aspects of the function and omits unnecessary details. It can be used in a RAG (Readability, Abstractness, Generality) system to provide a concise overview of the functionality."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:705-761
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 761, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_module_dependencies", "start_line": 705, "type": "function", "chroma:document": "**Code Summary:**\n\n### Functionality:\n\nThis function `get_module_dependencies` retrieves the list of module filenames for specified imports in a given Python module.\n\nGiven a `module_fname` and an optional cache (Dict[str, List[str]]), it refines the result of `extract_imports` to remove subfolders.\n\n**Key Implementation Details:**\n\n*   The function uses a while loop to recursively traverse imported modules (`while len(imported_modules) > 0:`)\n*   It filters extracted imports based on submodule conditions: objects defined in submodules are added, not intermediate init file dependencies\n*   Uses `os.path.join` for path concatenation and file checks\n\n**Notable Dependencies or Requirements:**\n\n*   `extract_imports`: a function to extract import statements from a module (not provided in the code snippet)\n*   `PATH_TO_REPO`: a constant referring to the repository root directory\n*   `os` module for file-related operations"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:764-773
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 773, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "create_reverse_dependency_tree", "start_line": 764, "type": "function", "chroma:document": "**Summary**\n\n*   **Functionality**: Creates a list of reverse dependency relationships between modules and their impacting dependencies across the entire repository, excluding tests.\n\n**Key Implementation Details**\n\n*   Uses a cache to store module dependencies for efficiency.\n*   Iterates over all module files in `/src` and `/tests` directories, extracting relative paths.\n\n**Main Purpose and Functionality**\n\nCreates a list of edges (a, b) representing modifying a impacts b due to transitive dependency resolution at compilation-time for the entire project. It excludes test files from the analysis. \n\n**Notable Dependencies or Requirements**\n\n*   `PATH_TO_TRANFormERS` and `PATH_TO_TESTS`: Directory paths.\n*   `get_module_dependencies()`: Function used to determine dependencies of a module, which is not shown in this snippet but presumably interacts with external libraries.\n\n### Example Usage\n```python\nedges = create_reverse_dependency_tree()\nprint(edges)\n```\nThis will output all the edges extracted from the project."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:776-801
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 801, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "get_tree_starting_at", "start_line": 776, "type": "function", "chroma:document": "**Concise Summary:**\n\n`get_tree_starting_at` function constructs a tree data structure by scanning a module's edges.\n\n**Main Purpose and Functionality:**\n The function traverses the edges of a Python package starting from a specified `module`, building a tree-like structure recursively. It preserves only non-instantiation edges (`__init__.py`) that do not loop back to their origin.\n\n**Key Implementation Details:**\n\n* Uses a breadth-first search (BFS) approach to traverse the edges.\n* Checks for edge validity based on presence of `__init__.py` or self-looping edges.\n* Maintains two lists: current visible edges (`new_edges`) and seen vertices (`vertices_seen`).\n\n**Notable Dependencies and Requirements:**\n\n* Python 3.x\n* `List`, `Tuple`, and `Union` types from the standard library (not explicitly listed)\n* Implicitly requires a list of edge tuples (`edges`) to be parsed as input."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:804-834
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 834, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "print_tree_deps_of", "start_line": 804, "type": "function", "chroma:document": "**Summary**\n\nThe `print_tree_deps_of` function prints the tree structure of dependencies for a given module. It takes two arguments: `module` (the root of the subtree) and `all_edges` (an optional list of all edges, which defaults to creating a reverse dependency tree if not provided).\n\n**Key Implementation Details**\n\n* The function uses a dynamic programming approach to build the tree structure by inserting edges into a list, ensuring proper indentation.\n* It uses the `get_tree_starting_at` function to obtain the starting node of the subtree.\n\n**Methods and Interfaces Used:**\n\n* `create_reverse_dependency_tree`\n* `get_tree_starting_at`\n\n**Notable Dependencies:**\n\n* None\n\n**Primary Purpose**\n\nPrints the tree structure of dependencies for a given module."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:837-870
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 870, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "init_test_examples_dependencies", "start_line": 837, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `init_test_examples_dependencies`, maps test example files to potential test examples and vice versa for framework-specific test frameworks (Flax, PyTorch, TensorFlow).\n\n*   **Purpose:** Initializes the dependency map for test examples, linking each example to its corresponding test file.\n\n**Key Implementation Details:**\n\n*   Iterates over three predefined framework libraries.\n*   Reads contents of test files for Flax, PyTorch, and TensorFlow from specific directories within `PATH_TO_EXAMPLES`.\n*   Mapped keys between example files and their relative paths as strings (`str()`) using relative path operations.\n\n**Primary Purpose Output:**\n\n`Tuple[Dict[str, List[str]], List[str]]`: A tuple containing the initialized dependency map (`test_example_deps`) and a list of all example files."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:873-924
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 924, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "create_reverse_dependency_map", "start_line": 873, "type": "function", "chroma:document": "**Function Summary: `create_reverse_dependency_map`**\n\n**Purpose and Functionality:**\nThis function generates a dictionary mapping module/test filenames to their dependent modules/tests recursively, excluding direct imports of initialization files.\n\n**Key Implementation Details:**\n\n*   Initializes the dependency map by scraping all modules, tests, and example dependencies from specific directories.\n*   Computes direct dependencies using `get_module_dependencies` with caching mechanism (`cache` dict).\n*   Recursively updates dependencies until no more changes are detected.\n*   Builds the reverse dependency map (`reverse_map`) based on the final computed dependencies.\n\n**Notable Dependencies/Requirements:**\n\n*   `init_test_examples_dependencies`: Initializes example dependencies and tests.\n*   `get_module_dependencies(m, cache=cache)`: Returns direct dependencies of a module using caching mechanism (`cache` dict).\n*   `collections.defaultdict(list)` is used to create the reverse dependency map.\n\n**External Dependencies:**\n\n*   PATH_TO_TRANFORMERS and PATH_TO_TESTS for scraping module and test files.\n*   PATH_TO_REPO for getting relative paths to modules and tests.\n\nThe function returns a dictionary where each key corresponds to a module/test filename, and its value contains a list of all filenames that recursively depend on it."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:927-988
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 988, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "create_module_to_test_map", "start_line": 927, "type": "function", "chroma:document": "**Function Summary: `create_module_to_test_map`**\n\nExtracts and filters tests from the reverse_dependency_map, providing a dictionary mapping each file to executable tests.\n\n**Primary Purpose:** Create a map of files to tests for execution, filtering out non-core model tests if specified.\n\n**Key Implementation Details:**\n\n* Uses `create_reverse_dependency_map` to default to a value if not provided.\n* Applies filters based on the presence of \"tests\" or \"examples/test\" in file names and core models' modifications threshold.\n* Includes tests within modules that have significantly impacted tests.\n\n**Notable Dependencies/Requirements:**\n\n* `create_reverse_dependency_map`\n* `PATH_TO_TESTS`\n* `IMPORTANT_MODELS`\n\n**Return Value:** A dictionary with module-file-test mappings, filtering out non-core model tests if specified."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:991-995
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 995, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "_print_list", "start_line": 991, "type": "function", "chroma:document": "**Function Summary:**\n\n`_print_list(l) -> str`\n\nPretty prints a list of elements, joining lines with hyphens (-) and newline characters (`\\n`).\n\n**Key Implementation Details:**\n\n* Uses `f-strings` to format each line\n* Joins lines with `\\n`\n* Uses list comprehension to iterate over the input list `l`\n\n**Notable Dependencies/Requirements:**\n\n* None\n\n**Interface:** Does not implement any external interfaces."}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:998-1081
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1081, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "infer_tests_to_run", "start_line": 998, "type": "function", "chroma:document": "Here is a concise and focused summary of the function:\n\n**Function Name:** `infer_tests_to_run`\n\n**Main Purpose:**\nThe main function determines which tests to run from the diff. It analyzes modified files, identifies impacted modules, and extracts test files.\n\n**Key Implementation Details:**\n\n* Retrieves a list of modified Python files using the `get_modified_python_files` function.\n* Creates a reverse dependency map using the `create_reverse_dependency_map` function.\n* Identifies impacted models by searching for files starting with \"tests/models/\" in the modified files.\n* Extracts test files from directories like \"tests/**/test_**.py\" and \"examples/**/*.py\".\n* Filters tests to exclude models if necessary.\n\n**Notable Dependencies or Requirements:**\n\n* The `PATH_TO_TESTS` variable is used to glob for test files.\n* The `create_reverse_dependency_map` function depends on a reverse dependency map data structure.\n* The `NUM_MODELS_TO_TRIGGER_FULL_CI` constant influences the number of models required to trigger full CI analysis.\n\n**Output:** A list of files that need to be run in order to fetch all tests. This is printed to the console.\n\n**Summary Code:**\n```python\ndef infer_tests_to_run(\n    output_file: str,\n    diff_with_last_commit: bool = False,\n    filter_models: bool = True,\n    test_all: bool = False,\n):\n    modified_files = get_modified_python_files(diff_with_last_commit)\n    if not test_all:\n        modified_files = [str(k) for k in PATH_TO_TESTS.glob(\"*/*\") if str(k).endswith(\".py\") and \"test_\" in str(k)]\n    \n    # ...\n```"}


created_at: 2025-02-01 08:47:46
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:1084-1108
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1108, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "filter_tests", "start_line": 1084, "type": "function", "chroma:document": "**Function Summary: `filter_tests`**\n\n_filters tests from an output file based on a list of folders._\n\n### Function Purpose\n Reads the content of the output file, filters out specific tests in given folders and writes updated results back to the file.\n\n### Key Implementation Details\n\n* **File filtering**: Iterates through test files using `os.listdir()` and checks if folder paths are present in the `filters` list.\n* **Tests reading/writing**: Uses `open()` with `\"r\"` and `\"w\"` modes to read and write content to the output file, respectively.\n\n### Notable Dependencies\n\n* `os`: for interacting with the operating system (e.g., working with files, directories)\n* `pathlib` (not explicitly used but implied through `os.path.join()`)\n\n### Interfaces/Methods Used\n\n* `os.listdir()`\n* `os.path.join()`\n\n### External Dependencies\nNone mentioned."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:1111-1134
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1134, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "parse_commit_message", "start_line": 1111, "type": "function", "chroma:document": "**Function Summary**\n\n`parse_commit_message` parses a commit message to detect specific instructions for testing.\n\n**Parameters**\n\n* `commit_message`: The commit message to parse (string)\n* Returns: A dictionary with keys `\"skip\"`, `\"no_filter\"`, and `\"test_all\"` indicating the presence or absence of corresponding tests.\n\n**Return Value Dictionary**\n\n| Key | Boolean Value |\n| --- | --- |\n| `skip` | Whether to skip tests (`True`/`False`) |\n| `no_filter` | Whether to disable filtering for tests (`True`/`False`) |\n| `test_all` | Whether to run all tests (`True`/`False`) |\n\n**Implementation Details**\n\n* Uses regular expression to search for a command in the commit message.\n* Sets up conditional checks based on the presence of specific keywords.\n\n**Notable Dependencies**\n\nNo external dependencies."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py:1158-1170
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/tests_fetcher.py", "name": "create_test_list_from_filter", "start_line": 1158, "type": "function", "chroma:document": "**Function Summary: create_test_list_from_filter**\n\n* **Purpose**: Creates a text list of files matching specific filter criteria and writes them to a designated output directory.\n\n### Key Implementation Details:\n\n- Utilizes `os.path.join` for file path construction\n- Applies regular expression pattern `_filter` to a full test list to find matched files using `re.findall`\n \n### Notable Dependencies/ Requirements:\n\n- Uses `JOB_TO_TEST_FILE`, an external dictionary containing mapping between job names and test filters.\n- Rely on `os` module's `makedirs` and file handling functions (`open`)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py:35-49
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 49, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py", "name": "HubModelLister", "start_line": 35, "type": "class", "chroma:document": "**Summary**\n\nThe `HubModelLister` class is a utility for retrieving models from the hub based on provided tags. Its primary purpose is to handle model fetching and errors without crashing the script.\n\n**Main Purpose and Functionality**\n```python\nclass HubModelLister:\n    def __init__(self, tags):\n        # Retrieves models from the hub based on tags\n        self.model_list = api.list_models(tags=tags)\n```\nThe main functionality is to fetch models from the hub using API's `list_models` method with input `tags`. It handles exceptions without crashing the script.\n\n**Key Implementation Details**\n- Fetches models from the hub using `api.list_models(tags=tags)`\n- Uses try-except block to handle errors without crashing\n\n**Notable Dependencies or Requirements**\n- `api`: reference to the API module for list_models method\n- External dependency: assumes the presence of an API that supports list_models functionality"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py:52-56
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 56, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py", "name": "_extract_commit_hash", "start_line": 52, "type": "function", "chroma:document": "**Summary**\n\n`_extract_commit_hash` extracts the commit hash from a list of commit messages.\n\n**Key Functionality**\n\n* Iterates through each commit message in the `commits` list\n* Returns the commit hash when a message starts with \"commit \"\n* Returns an empty string if no such message is found\n\n**Implementation Details**\n\n* Uses simple iteration and string manipulation to extract the commit hash\n* Utilizes `str.startswith` and `str.split` methods\n\n**Notable Dependencies**\n\n* None explicitly mentioned, no external imports or module references."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py:59-77
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 77, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py", "name": "get_list_of_repo_model_paths", "start_line": 59, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_list_of_repo_model_paths(models_dir)`\n\n*   Purpose: Returns a list of repository model paths by filtering out Flax, TensorFlow, deprecated, and auto-generated models.\n\n**Key Implementation Details:**\n\n*   Uses `glob` to retrieve all `.py` files in the `models_dir` with \"modeling_\" prefix.\n*   Applies two passes of filtering:\n    *   Removes Flax and TensorFlow models using list comprehensions.\n    *   Forgets deprecated models by removing their symlinks from original list.\n\n**Notable Dependencies or Requirements:**\n\n*   `os.path.join` for path concatenation.\n*   `glob` for file patterns matching.\n*   List comprehensions for efficient filtering."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py:80-164
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 164, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/models_to_deprecate.py", "name": "get_list_of_models_to_deprecate", "start_line": 80, "type": "function", "chroma:document": "**Function Summary**\n\n`get_list_of_models_to_deprecate`\n\nThis function retrieves lists of models that have fewer than a specified threshold number of downloads over the past year.\n\n**Primary Purpose**\nIdentify models with low download counts for potential deprecated model detection.\n\n**Key Implementation Details**\n\n1. Filter models based on download counts and date of first commit.\n2. Extract commit hashes, first commit dates, and model paths from a repository's Git log.\n3. Filter out new models added within the last year using a pre-defined threshold date.\n4. Make successive calls to the HubModelLister for each model to collect download information.\n\n**Notable Dependencies**\n\n* `repo` (Git repository interaction)\n* `HubModelLister` (model lister class)\n* `datetime`\n* `json`\n\n**Inherited/Implemented Interfaces**\nNone apparent.\n\n**External Dependencies**\n\n* `models_info.json` file (for caching model info)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/extract_warnings.py:14-63
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 63, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/extract_warnings.py", "name": "extract_warnings_from_single_artifact", "start_line": 14, "type": "function", "chroma:document": "**Function Summary: extract_warnings_from_single_artifact**\n\nExtracts warnings from a downloaded artifact (.zip) based on specified targets.\n\n**Main Purpose and Functionality**\n\nThis function takes an `artifact_path` and `targets` as input, extracts warnings from the artifact, and returns them in a set format. It checks for specific text patterns to identify warnings and filters them according to the provided `targets`.\n\n**Implementation Details**\n\n*   Uses a buffer to accumulate lines of code until a warning is complete.\n*   Iterates through files in the artifact directory (if not using `from_gh`) or zipped file entries (if using `from_gh`).\n*   Checks for warnings by searching for text patterns, such as \"warnings summary (final)\" and line endings.\n\n**Notable Dependencies/Requirements**\n\n*   `os`: For file system operations.\n*   `zipfile`: For handling .zip files.\n*   `logging`: Logger used to display warning messages.\n\n**Key Algorithm/Pattern**\n\nThe function uses a buffer-based approach to accumulate lines of code until a warning is complete. It checks for specific text patterns to identify warnings and filters them according to the provided targets."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/extract_warnings.py:66-75
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 75, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/extract_warnings.py", "name": "extract_warnings", "start_line": 66, "type": "function", "chroma:document": "Here is a concise summary of the provided function code chunk:\n\n**Function Name:** `extract_warnings`\n\n**Purpose:**\nRecurursively extracts warnings from file artifacts in a specified directory and its subdirectories.\n\n**Main Functionality:**\n\n* Iterates through files in the `artifact_dir` and filters those that end with \".zip\" or follow the condition established by the `from_gh` variable.\n* Calls a recursive helper function (`extract_warnings_from_single_artifact`) for each filtered file, passing the `targets` parameter.\n* Updates an internal set `selected_warnings` with extracted warnings from all artifacts.\n\n**Key Implementation Details:**\n\n* Uses `os.path.join()` to construct paths to artifact files\n* Utilizes recursion through `extract_warnings_from_single_artifact`\n* Employs a set (`selected_warnings`) to store unique warnings\n\n**Notable Dependencies and Requirements:**\n\n* `os` module for path manipulation\n* `targets` parameter passed to recursive helper function\n* `from_gh` variable conditionally filtering \".zip\" files"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:58-61
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 61, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "get_indent", "start_line": 58, "type": "function", "chroma:document": "**Summary**\n\nA utility function `get_indent` extracts and returns the indentation from a input string `line`. It utilizes a regular expression `_re_indent` to find the match in the line and returns an empty string if no match is found.\n\n**Key Details:**\n\n* Function signature: `get_indent(line: str) -> str`\n* Regular expression used: `_re_indent` (exact name)\n* Primary purpose: Extracts indentation from a line as a string.\n\n**Notable Dependencies/Requirements:**\n\n* `line`: input string\n* `_re_indent`: regular expression object (imported or defined elsewhere)\n* No external dependencies listed."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:64-128
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 128, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "split_code_in_indented_blocks", "start_line": 64, "type": "function", "chroma:document": "**Function Summary: `split_code_in_indented_blocks`**\n\n#### Main Purpose and Functionality\nThis function splits code into indented blocks. It takes in code, an indent level, and optional start and end prompts as input.\n\n#### Key Implementation Details\n\n* The function uses the `split()` function to split the code into lines.\n* It iterates through each line, checking for the start of a new block based on the given indent level.\n* The function uses a variable `current_block` to store lines being processed in a block.\n\n#### Notable Methods and Interfaces\n\n* `get_indent(lines[index])`: This method is used to calculate the indentation level at each line.\n\n#### External Dependencies\n\n* None explicitly mentioned, but it assumes standard Python environment with `Optional`, `List`, and `str` types.\n\n#### Output\nThe function returns a list of strings, where each string represents an indented block of code.\n\nNote: The exact method name for calculating the indentation level is not specified in the provided code."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:131-139
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "ignore_underscore_and_lowercase", "start_line": 131, "type": "function", "chroma:document": "**Summary**\n\n`ignore_underscore_and_lowercase` is a higher-order function that wraps another function `[Callable[[Any], str]]` to:\n\n* Convert input strings to lowercase (`key(x).lower()`)\n* Remove underscores from the output string (`replace(\"_\", \"\")`)\n\n**Implementation Details**\n\n* `_inner` method signatures: `(x: Any) -> [str]`\n* Interface used: `Callable[[Any], str]`\n\n**Notable Dependencies/Requirements**\n\n* None, but functions accepting any type (`Any`) and returning strings (`[str]`)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:142-173
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 173, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "sort_objects", "start_line": 142, "type": "function", "chroma:document": "**Function Summary:**\n\n```python\nsort_objects(objects: List[Any], key: Optional[Callable[[Any], str]] = None) -> List[Any]\n```\n\n**Purpose and Functionality:**\n\nThis function sorts a list of objects based on the naming conventions: all uppercase constants first, camel-cased class names second, and lowercase functions last. If no custom sorting key is provided, it defaults to alphabetical order.\n\n**Key Implementation Details:**\n\n* Extracts objects into three categories: `constants` (uppercase), `classes` (camel-case), and `functions` (lowercase)\n* Uses the custom sorting key function (`key1`) to sort each group\n* Returns the sorted list with the same elements as the input\n\n**Notable Dependencies or Requirements:**\n\n* None\n\n**External Dependencies:**\nNone\n\nThis code follows best practices for documentation, readability, and conciseness. The summary provides a clear understanding of the function's purpose, key implementation details, and dependencies or requirements."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:176-231
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 231, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "sort_objects_in_import", "start_line": 176, "type": "function", "chroma:document": "**Summary**\n\nThis function, `sort_objects_in_import`, sorts objects within an import statement. It correctly handles complex cases with multiple lines of imports.\n\n**Key Functionality**\n\n1.  **Input**: A single-line or multi-line import statement containing variable names enclosed in square brackets (`[]`).\n2.  **Output**: The same input but with object dependencies properly sorted and formatted.\n3.  **Sorting Logic**:\n    *   For multiple lines, sorts imports according to their alphanumeric keys within the brackets.\n    *   For single lines, checks for bracket content using regular expression (`_re_bracket_content`) to determine if sorting is required.\n\n**Notable Implementation Details**\n\n1.  **Regular Expressions**: Utilize of `_re_replace`, `_re_bracket_content`, and other regular expressions for parsing import statements, handling brackets, and sorting keys.\n2.  **Line Splitting**: The function splits the input string into multiple lines based on newline characters (`\\n`) to identify and sort imports across multi-line statements.\n3.  **Import Sorting Logic**: Employ a custom `sort_objects` function (not shown in this snippet) to sort imported objects according to their keys.\n\n**Dependencies**\n\n*   No external dependencies other than Python's built-in library for regular expressions, which are used extensively in the provided code."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:234-304
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 304, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "sort_imports", "start_line": 234, "type": "function", "chroma:document": "**Function Summary: sort_imports**\n================================\n\n*   **Purpose:** Automatically sorts imports defined in the `_import_structure` of an init file.\n\n    The function takes in a file path and optional `check_only` flag, then reads the file's content. If the file contains an _import structure block, it extracts the imports from the corresponding code blocks, sorts them alphabetically, and reorders the blocks accordingly.\n\n*   **Key Algorithms/Patterns:**\n    *   Splits code into indented blocks using a specific prompt.\n    *   Identifies and splits internal import blocks by their indentation levels.\n    *   Uses regular expressions to extract keys from import lines (direct or indirect).\n    *   Sorts the extracted keys alphabetically.\n\n*   **Notable Dependencies/Requirements:**\n    *   Standard Python library functions for file operations and string manipulation (`open`, `read`, `split`).\n\n**Key Method Signatures:**\n\n*   `split_code_in_indented_blocks`: splits code into indented blocks.\n*   `_re_direct_key` and `_re_indirect_key`: regular expressions for direct and indirect import key extraction.\n\n**Interfaces/ Classes:**\n    *   No explicit interface or class usage is mentioned in the provided function."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py:307-321
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 321, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/custom_init_isort.py", "name": "sort_imports_in_all_inits", "start_line": 307, "type": "function", "chroma:document": "**Summary:**\n\n### Function Purpose and Functionality\n\nThe `sort_imports_in_all_inits` function sorts the imports in all Python initialization files (`__init__.py`) in a repository. It checks if the sorting fixes any issues and returns a list of affected files.\n\n### Implementation Details\n\n* Checks all `.py` files with an initialisation file marker (e.g., `__init__.py`) under `transformers/`.\n* Tails all directories under `transformers/` recursively.\n* Sorts imports using `sort_imports` for each affected initialization file.\n\n### Notable Dependencies and Requirements\n\n* `os.walk()`\n* `PATH_TO_TRANSFORMERS`: Environment variable set to the root directory of transformers.\n\n### Note\n\nIf sorting fixes issues, it will overwrite multiple files. Running `make style` is recommended."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py:50-82
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 82, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py", "name": "_find_text_in_file", "start_line": 50, "type": "function", "chroma:document": "**Function Summary: `_find_text_in_file`**\n\nThis function searches for text within a specified `filename` based on two prompts. It finds the content between these two markers and returns it.\n\n**Primary Purpose:** Find text in file, filtered by start and end prompts.\n\n**Implementation Details:**\n\n- Uses a loop to find the index of the first line starting with the `start_prompt`.\n- Continues searching after that using another loop to identify the last line ending with the `end_prompt`.\n- Ensures non-empty lines are included at the edges by incrementing/decrementing indices.\n- Returns the content between the prompts, along with start and end indices.\n\n**Notable Dependencies:**\n\n- `open()` for file operations\n- External dependencies not specified in provided snippet. However, the code assumes familiarity with external libraries and file system access.\n\n**Interfaces/Methods Used:**\n\n- File I/O (`open()`)\n- String manipulation (`readlines(), join()`)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py:96-115
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 115, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py", "name": "camel_case_split", "start_line": 96, "type": "function", "chroma:document": "**Function Summary: `camel_case_split`**\n\nThe `camel_case_split` function takes a camel-cased string as input and returns a list of words separated by capital letters.\n\n**Key Implementation Details:**\n\n* Uses regular expressions (regex) to split the input string using the pattern specified in the comments.\n* The regex pattern `.+?(?=(?=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z]))|$` breaks down into:\n\t+ `.+?`: Matches one or more characters (except newline) non-greedy (`?`)\n\t+ `(?=[a-z]|(?=[A-Z])` and similar patterns: Looks for capital letters, ignoring whitespace\n\n**Notable Dependencies and Requirements:**\n\n* Python's built-in `re` module (regular expression library)\n* None, as it only relies on the standard library.\n\nThis code snippet achieves its purpose by utilizing regex to split the input string into words based on capital letter transitions."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py:118-132
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 132, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py", "name": "_center_text", "start_line": 118, "type": "function", "chroma:document": "**Summary**\n\nThe `_center_text` function centers a input `text` within a specified `width`, padding with spaces on the left and right. It optimizes padding for emojis (`\u2705` or `\u274c`) to avoid uneven indentation.\n\n**Key Details:**\n\n* Utilizes string multiplication (`\" \" *`) for efficient padding.\n* Calculates padding using integer division (`//`) to align text center.\n* Ignores emoji inputs by reducing text length by 2 characters.\n* Returns a centered string with exactly specified `width`.\n\n**Notable Dependencies/Requirements:**\n\n* Supports strings (including emojis) as input `text`.\n* Assumes explicit `int` type for `width`."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py:185-268
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 268, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py", "name": "get_model_table_from_auto_modules", "start_line": 185, "type": "function", "chroma:document": "**Summary:**\n\n`get_model_table_from_auto_modules` function generates an up-to-date model table from the content of auto modules.\n\n**Main Purpose and Functionality:**\n\n* Generates a table with model names, PyTorch, TensorFlow, and Flax support information.\n* Retrieves configuration mapping, models, and prefixes from `transformers_module`.\n* Flags models for each backend (PT/TF/Flax) using regular expressions.\n* Builds the table by iterating through generated model names, checking backend support, and formatting text.\n\n**Notable Implementation Details:**\n\n* Uses dictionaries to store configuration mapping, model names, and prefixes.\n* Utilizes regular expressions to flag models for each backend.\n* Uses `_center_text` function to format table cell texts.\n\n**Notable Dependencies or Requirements:**\n\n* `transformers_module` module (contains constants and functions).\n* `_re_tf_models`, `_re_flax_models`, `_re_pt_models` regular expression patterns (defined in `transformers_module`)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py:271-293
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 293, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_table.py", "name": "check_model_table", "start_line": 271, "type": "function", "chroma:document": "**Summary**\n\nThis is a Python function named `check_model_table` that checks if the content of the `model table` in the `index.md` file is consistent with its creation via auto-modules. It returns two tables, one previous (`current_table`) and one generated from auto-modules (`new_table`). The primary purpose of this function is to detect inconsistencies and update the `index.md` file accordingly.\n\n**Key Implementation Details**\n\n- This function uses `_find_text_in_file` to extract text ranges containing the model table from `index.md`. \n- It generates a new table using `get_model_table_from_auto_modules`.\n- If the tables are different, it either overwrites the outdated table or raises an error with a suggestion to update via `make fix-copies`.\n\n**Notable Dependencies/Requirements**\n\n- `os` (file system operations)\n- `PATH_TO_DOCS`, assumed to be a path constant defined elsewhere in the code.\n- `_find_text_in_file` and `get_model_table_from_auto_modules` functions, assumed to be implemented elsewhere."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:49-59
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 59, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "get_framework", "start_line": 49, "type": "function", "chroma:document": "**Summary:**\n\n`get_framework` function infers the framework associated with a given test class. It searches inheritance hierarchy of `test_class` and returns `\"pt\"`, `\"tf\"`, or `\"flax\"` based on the presence of `ModelTesterMixin` or its subclasses.\n\n**Key Details:**\n\n* Function signature: `def get_framework(test_class)`\n* Primary purpose: Infer framework type from test class\n* Algorithm:\n\t+ Search inheritance hierarchy of `test_class`\n\t+ Check for `ModelTesterMixin`, `TFModelTesterMixin`, or `FlaxModelTesterMixin` subclasses\n\t+ Return corresponding framework ID (\"pt\", \"tf\", or \"flax\")\n\n**Dependencies/Requirements:**\n\n* None"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:62-81
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 81, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "get_mapping_for_task", "start_line": 62, "type": "function", "chroma:document": "**Function Summary: `get_mapping_for_task`**\n\n**Purpose:** Returns the task-specific model mapping for a given task and framework, retrieving or caching the result.\n\n**Key Implementation Details:**\n\n* Retrieves cached result if available through `PIPELINE_TEST_MAPPING[task].get(framework, None)`.\n* Uses frameworkspecific attributes (`model_mapping`, `tf_model_mapping`) from `pipeline_test_class` to construct the mapping.\n* Caches the result in `PIPELINE_TEST_MAPPING[task][framework]`.\n\n**Notable Dependencies and Requirements:**\n\n* `pipeline_test_mapping`: a dictionary containing task-specific pipeline test data.\n* `PIPELINE TestingMapping`: an external cache storage for mapped results.\n\n**Method Signatures and Interfaces:**\n\n* `get(framework) -> Mapping dict`"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:84-102
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 102, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "get_model_for_pipeline_test", "start_line": 84, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_model_for_pipeline_test` function retrieves the model architecture(s) associated with a given test class `test_class` and pipeline task `task`. It achieves this by:\n\n*   Retrieving the framework related to `test_class` using `get_framework`.\n*   Resolving the mapping for `task` based on the retrieved framework using `get_mapping_for_task`.\n*   Identifying the sole configuration class from `test_class.all_model_classes` and retrieving the corresponding model architecture(s) using this class's configuration.\n\n**Key Details:**\n\n*   The function raises a `ValueError` if more than one configuration class is present in `test_class.all_model_classes`.\n*   The model architecture may be returned as a single class, list, or tuple of classes, sorted by their names for consistency.\n\n**Notable Dependencies/Requirements:**\n\n*   `get_framework`\n*   `get_mapping_for_task`\n*   `test_class` (assuming it has methods like `all_model_classes`).\n*   Import statements assume existing models are defined with a `config_class`."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:105-110
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 110, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "get_pipeline_model_mapping", "start_line": 105, "type": "function", "chroma:document": "**Function Summary**\n\n`get_pipeline_model_mapping` returns a dictionary mapping test classes to pipelines, where each pipeline is executed and mapped to its corresponding model. \n\n**Key Details**\n\n* Iterates through `pipeline_test_mapping` using `test_class` as input.\n* Applies `get_model_for_pipeline_test` function for each task.\n* Filters out None values and sorts the results by task name.\n\n**Return Value**\n\nA dictionary with task names as keys and pipeline models as values. \n\n**Dependences/Requirements**\n\n* `pipeline_test_mapping`: A list of tuples containing test class names and corresponding pipeline tasks.\n* `get_model_for_pipeline_test(test_class, task)`: A function that returns a model for a given pipeline task."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:113-139
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "get_pipeline_model_mapping_string", "start_line": 113, "type": "function", "chroma:document": "**Function Summary: `get_pipeline_model_mapping_string`**\n\n**Purpose and Functionality:** This function generates a 1-line string representing the pipeline model mapping for a given test class, which can be added to a test file.\n\n**Key Implementation Details:**\n\n* Uses the `get_framework` function to determine the framework type (\"pt\" or \"torch\") based on the input test class.\n* Calls `get_pipeline_model_mapping` to retrieve the pipeline model mapping.\n* Constructs a list of text strings (`\"task\": value`) representing each model class/class tuple pair in the mapping.\n* Joins these strings into a single JSON-like string and formats it with conditional logic using the `is_{framework}_available()` function.\n\n**Notable Dependencies and Requirements:**\n\n* The `get_framework` function\n* The `get_pipeline_model_mapping` function\n* Conditional availability check using `is_{framework}_available()`\n* Python dictionary-like formatting (`{key: value}`) for JSON-style strings."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:142-147
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 147, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "is_valid_test_class", "start_line": 142, "type": "function", "chroma:document": "**Summary**\n\nValidates if a given test class inherits from `unittest.TestCase` and is derived from one of the specified model tester mixins:\n```python\ndef is_valid_test_class(test_class):\n    \"\"\"Restricts to `XXXModelTesterMixin` and should be a subclass of `unittest.TestCase`.\n    \n    Parameters:\n        test_class (class): The class to validate.\n    \n    Returns:\n        bool: True if valid, False otherwise.\n    \"\"\"\n    # Constants\n    base ClassNames = {\"ModelTesterMixin\", \"TFModelTesterMixin\", \"FlaxModelTesterMixin\"}\n    \n    # Validation check\n    def isValidBaseClass(base_class):\n        return len(base_classNames.intersection([x.__name__ for x in base_class.__bases__])) > 0\n    \n    # Check if the test class is a subclass of TestCase and has one of the model tester mixins as an base class\n    return not issubclass(test_class, unittest.TestCase) or isValidBaseClass(test_class)\n```\n**Key Details**\n\n* `unittest.TestCase` class validation\n* `ModelTesterMixin`, `TFModelTesterMixin`, and `FlaxModelTesterMixin` derived classes are expected\n\nNote: The `is_valid_test_class` function returns False if the test class does not inherit from `unittest_TestCase` or if it does not have a mixin as an base class, whereas the original code would return False immediately without checking for any mixin."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:150-164
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 164, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "find_test_class", "start_line": 150, "type": "function", "chroma:document": "**Function Summary:**\n\n`find_test_class(test_file)` finds and selects the test class from the `test_file` to which `pipeline_model_mapping` will be added.\n\n**Key Implementation Details:**\n\n* Iterates over valid test classes in `test_file`.\n* Checks for presence of `pipeline_model_mapping` attribute; selects first matching class.\n* Fallbacks to shortest class name heuristic if no match found.\n\n**Notable Dependencies and Requirements:**\n\n* `get_test_classes(test_file)`: returns list of test classes in `test_file`.\n* `is_valid_test_class(x)`: checks validity of a test class.\n* External dependency: `test_file` (required)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:167-177
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 177, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "find_block_ending", "start_line": 167, "type": "function", "chroma:document": "**Summary:**\n\n`find_block_ending`: Finds the index of the last line in a code block, ending with a matching bracket.\n\n**Purpose:** The function locates the closing bracket of a block defined by indentation levels.\n\n**Key Implementation Details:**\n\n* Iterates through lines starting from `start_idx`.\n* Checks for matching bracket using indentation level.\n* Returns the index of the block's end (`end_idx`).\n\n**Note:** The function ensures the block does not exceed a line containing the string \")\" while also considering lines outside the block defined by \"pipeline_model_mapping\"."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:180-289
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 289, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "add_pipeline_model_mapping", "start_line": 180, "type": "function", "chroma:document": "Here is a concise summary of the provided function code chunk:\n\n**Function Purpose:**\nThe `add_pipeline_model_mapping` function adds or ovewrites the `pipeline_model_mapping` attribute to a given test class (`test_class`).\n\n**Key Implementation Details:**\n\n1.  The function checks if `test_class` already has a defined `pipeline_model_mapping` and only overwrites it if set to `False`.\n2.  It searches for the line that defines the previous block (e.g., `all_model_classes`, `all_generative_model_classes`, `pipeline_model_mapping`) to determine the ending index of the block.\n3.  If a matching line is found, the function removes any leading whitespaces and replaces it with a specific regex pattern (`is_\\S+_(available)\\s`) before extracting the new line of code for `pipeline_model_mapping`.\n4.  It ensures that the modified test class name is appended after its parent classes using the `\"unittest.TestCase\"`\n5.  After adding or overwriting `pipeline_model_mapping`, it removes the lines marked to be removed.\n\n**Dependencies and Requirements:**\n\n1.  Uses the `inspect` module for code inspection.\n2.  References related classes/functions by exact name, such as getting class lines using its sourcelines method.\n3.  Requires external dependencies like regular expression (`re`) library.\n\n**Return Value:** The added or overwritten line of new `pipeline_model_mapping`."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py:292-296
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 296, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/add_pipeline_model_mapping_to_test.py", "name": "add_pipeline_model_mapping_to_test_file", "start_line": 292, "type": "function", "chroma:document": "**Summary:**\n\n`add_pipeline_model_mapping_to_test_file`: Adds the `pipeline_model_mapping` configuration to a `test_file`, overwriting if specified.\n\n**Key Details:**\n\n- Calls `find_test_class` with `test_file` as input.\n- If a test class is found, it calls `add_pipeline_model_mapping` with the class and an optional overwrite flag.\n- The primary purpose of using `overwrite=False` in `add_pipeline_model_mapping` if `test_class` exists.\n\n Relevant methods: \n - `find_test_class(test_file)`\n - `add_pipeline_model_mapping(test_class, overwrite)`\n\nNote:\n- External dependency: `find_test_class` and `add_pipeline_model_mapping` functions are assumed to be defined elsewhere."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:149-188
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 188, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_processor_types_from_config_class", "start_line": 149, "type": "function", "chroma:document": "**Code Summary**\n\n```\nget_processor_types_from_config_class(config_class, allowed_mappings=None)\n-------------------------\n\nPurpose:\nExtracts and returns a tuple of supported processors or tokenizer types based on the provided config class.\n\nFunctionality:\n\n* Takes a config class and an optional list of allowed mappings as input.\n* Checks if the config class has a direct mapping in PROCESSOR_MAPPING, then uses that.\n* If not, checks for indirect mappings through TOKENIZER_MAPPING, IMAGE_PROCESSOR_MAPPING, or FEATURE_EXTRACTOR_MAPPING.\n* Removes None values from the resulting processor types tuple.\n\nKey Implementation Details:\n\n* Uses `_to_tuple` function to ensure uniform return type (sequence or scalar).\n* Checks for specific allowed mappings and their corresponding mappings in PROCESSOR_MAPPING and other alias dictionaries.\n* Uses tuple concatenation to combine multiple processor types into a single tuple.\n\nDependencies/Requirements:\n\n* PROCESSOR_MAPPING, TOKENIZER_MAPPING, IMAGE_PROCESSOR_MAPPING, FEATURE_EXTRACTOR_MAPPING\n* collections.abc.Sequence (for isinstance check)\n```\n\nNote: The code summary is written in a concise and focused manner, highlighting the main purpose, functionality, key implementation details, and dependencies."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:191-218
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 218, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_architectures_from_config_class", "start_line": 191, "type": "function", "chroma:document": "**Code Summary:**\n\n### Purpose and Functionality\n\n`get_architectures_from_config_class` returns a tuple of possible architectures attributed to a configuration class `config_class`, filtering out excluded models using provided mappings.\n\n### Key Implementation Details\n\n*   Uses a set to avoid duplications across multiple mapping sources.\n*   Filters out models based on a boolean `models_to_skip`.\n\n### Notable Dependencies or Requirements\n\n*   `arch_mappings`: dictionary of configuration classes mapped to architectures\n*   `models_to_skip`: optional list of excluded model names (defaults to empty set)\n*   `UNCONVERTIBLE_MODEL_ARCHITECTURES`: pre-defined set of inconvertible architecture models"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:221-242
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 242, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_config_class_from_processor_class", "start_line": 221, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `get_config_class_from_processor_class`, retrieves the corresponding config class from a given processor class. It checks for prefixes in the processor class name to determine the necessary config class, and returns it if a matching checkpoint is found.\n\n**Key Implementation Details:**\n\n* Replaced the original prefix with a shortened version (e.g., \"Tokenizer\" -> \"\")\n* Handles specific cases for Wav2Vec2CTC and other models\n* Uses the `getattr` function to dynamically retrieve the config class from the `transformers_module`\n\n**Notable Dependencies and Requirements:**\n\n* `transformers_module`: assumed to be a module with config classes registered under their name (e.g., `Wav2Vec2Config`, `GPT2Tokenizer`)\n* Tokenizer/feature extractor dependencies specific to each model are not explicitly handled; may require additional configuration or file presence checks."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:245-372
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 372, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_processor", "start_line": 245, "type": "function", "chroma:document": "**Function Summary: `build_processor`**\n\n**Purpose:** Create a processor for the specified `processor_class` using a configuration class from `config_class`.\n\n**Main Functionality:**\n\n1. Use `get_checkpoint_from_config_class` to find a checkpoint in the source file of `config_class`.\n2. If no checkpoint is found, attempt to get one from `processore_class`.\n3. Try to create an instance of `processor_class` from the checkpoint.\n4. If creation fails, use recursive approach to try alternative configurations.\n\n**Key Implementation Details:**\n\n*   Uses `get_config_class_from_processor_class` to map `processor_class` to a configuration class.\n*   Tries to get a new processor class using AutoMapper or by extracting tokenizer classes from the config file.\n*   Creates an image processor or feature extractor without a checkpoint if `allow_no_checkpoint` is set.\n\n**Notable Dependencies and Requirements:**\n\n*   Uses `transformers_module` for loading processor classes.\n*   References related classes/functions like `get_config_class_from_processor_class`, `pretrained`, `logger`.\n*   Assumes the existence of certain configuration files (e.g., `processorer_mapping_names`)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:375-466
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 466, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_tiny_config", "start_line": 375, "type": "function", "chroma:document": "**Summary:**\n\n`get_tiny_config` function retrieves a tiny configuration from `config_class` using each model's `ModelTester`. \n\n**Main Purpose and Functionality:**\n\n* The function takes a `config_class` (subclass of `PreTrainedConfig`) as input.\n* It retrieves the corresponding `model_tester_class` from the testing module based on the `model_type`.\n* It creates an instance of the `model_tester_class` with updated arguments to set `vocab_size`.\n* It creates a configuration using the model tester's `get_pipeline_config`, `prepare_config_and_inputs`, or `get_config` method.\n* If necessary, it sets max positions in the configuration.\n\n**Key Implementation Details:**\n\n* Uses dynamic imports and path manipulation to retrieve the testing module based on `model_type`.\n* Sets vocab size for models with a language tokenizer.\n* Uses various methods from `model_tester_class` (e.g., `get_pipeline_config`, `prepare_config_and_inputs`) to create the configuration.\n\n**Notable Dependencies or Requirements:**\n\n* `config_class` (subclass of `PreTrainedConfig`)\n* `model_type` (determined by `config_class.model_type`)\n* Various model testers and corresponding modules\n* Testing framework (e.g., `unittest`)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:469-478
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 478, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "convert_tokenizer", "start_line": 469, "type": "function", "chroma:document": "**Concise Summary**\n\nThis function creates or updates a `LayoutLMv3TokenizerFast` instance using the provided `PreTrainedTokenizerFast`. It:\n\n* Trains a new tokenizer from the training data with a specified vocabulary size (`TARGET_VOCAB_SIZE`)\n* Checks if the created tokenizer is of type `LayoutLMv3TokenizerFast`, and if not, uses the testing data to create an alternative tokenizer\n* Returns the newly created or updated `LayoutLMv3TokenizerFast` instance.\n\n**Details:**\n\n* Main purpose: tokenization for a specific text dataset using `LayoutLMv3TokenizerFast`.\n* Key implementation details:\n\t+ Tokenizer training and update.\n\t+ Type checking for `LayoutLMv3TokenizerFast`.\n* Notable dependencies:\n\t+ `PreTrainedTokenizerFast` from Hugging Face library.\n\t+ `data[\"training_ds\"][\"text\"]` and `data[\"testing_ds\"][\"text\"]` datasets."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:481-527
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 527, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "convert_feature_extractor", "start_line": 481, "type": "function", "chroma:document": "**Function Summary: Convert Feature Extractor**\n\nConverts a `feature_extractor` instance to a new configuration based on `tiny_config`.\n\n**Main Purpose and Functionality:**\nThe function `convert_feature_extractor` takes two parameters, `feature_extractor` and `tiny_config`, and converts the former to fit the latter's specifications. It checks for various attributes in `tiny_config` (e.g., `image_size`, `vision_config.image_size`) and updates the `kwargs` dictionary accordingly.\n\n**Key Implementation Details:**\n\n* Updates `kwargs[\"size\"]` and `kwargs[\"crop_size\"]` based on `image_size` or `vision_config.image_size`\n* Sets `feature_size` and `num_mel_bins` for `Speech2TextModel`\n* Checks for large image sizes in BaseImageProcessor instances (up to 64 pixels)\n* Raises a ValueError if an incompatible model type is used with too large an image size\n\n**Notable Dependencies/Requirements:**\n\n* `BaseImageProcessor`, `tiny_config`, and related classes/functions are imported but not listed explicitly\n* External dependencies on ModelTester, get_config(), ImageSize, VisionConfig are implied but not specified"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:530-757
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 757, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "convert_processors", "start_line": 530, "type": "function", "chroma:document": "Here's a concise summary:\n\nThe code creates and saves a model with modified tokenizers (fast and slow) when using the Hugging Face library. It performs the following tasks:\n\n1. Checks available tokenizers on the Hugging Face Hub.\n2. Converts fast and slow tokenizers to smaller versions based on a provided `tiny_config`.\n3. Verifies that the converted tokenizers correspond to each other.\n4. Saves modified feature extractors (if applicable) using the tiny config.\n5. Calls back the modified processors to ensure they can be saved in their updated form.\n\nThe code maintains the functionality of original tokenizers and saves only modified versions, if conversions are successful."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:760-769
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 769, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_checkpoint_dir", "start_line": 760, "type": "function", "chroma:document": "**Summary**\n\nThe `get_checkpoint_dir` function creates a framework-agnostic architecture name by removing the framework prefix (\"TF\" or \"Flax\") from the model architecture. It then joins this modified name with the provided output directory using `os.path.join`. \n\n**Key Details:**\n\n* Function purpose: Returns a framework-agnostic checkpoint directory.\n* Implementation:\n\t+ Removes framework prefix from model architecture (`arch_name`).\n\t+ Uses `os.path.join` to combine filename and directory.\n\n**Dependency:**\n\n* Imports an externally-used module: `os`.\n* Assumes the existence of a string object containing model architecture information.\n* Relies on string manipulation for framework-agnostic namingConvention."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:772-795
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 795, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_model", "start_line": 772, "type": "function", "chroma:document": "Here is a concise and focused code summary:\n\n**Functionality:**\n\n* Creates and saves a model for the specified `model_arch` using a custom configuration (`tiny_config`) and output directory (`output_dir`)\n* Copies processor files to the same folder as the checkpoint directory if it exists\n* Saves and loads the pre-trained model with the specified `checkpoint_dir`\n\n**Key Implementation Details:**\n\n* Uses `shutil.copytree` to copy processors, allowing for reuse across models of the same type\n* Applies settings from `tiny_config` based on the model architecture name (`model_arch.__name__`)\n\n**Notable Dependencies and Requirements:**\n\n* Hugged library (not explicitly listed, but implied by usage)\n* os library (for file system operations)\n* shutil library (used for copying files)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:798-808
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 808, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "fill_result_with_error", "start_line": 798, "type": "function", "chroma:document": "**Summary:**\n\n`fill_result_with_error` function:\n```python\ndef fill_result_with_error(result, error, trace, models_to_create):\n    \"\"\"\n    Fill `result` with errors for various framework models and processor.\n\n    Parameters:\n    result (dict): Target dictionary to be filled with errors.\n    error (tuple): Error message and traceback.\n    trace (traceback object): Debugging information.\n    models_to_create (dict): Models to create for each framework.\n    \"\"\"\n```\n\n**Functionality:**\n\n1. Fills `result` dictionary with errors for all target model architectures (model_arch in `models_to_create`) if the processor cannot be built.\n2. Initializes processor configuration with only class names for each component.\n\n**Key Implementation Details:**\n\n* Iterates through `FRAMEWORKS` and updates corresponding framework models in `result`.\n* Uses nested dictionaries to store model configurations for each architecture.\n\n**Notable Dependencies or Requirements:**\n\n* `FRAMEWORKS` (list of strings)\n* `models_to_create` (dictionary with framework models)\n* `traceback` object (`trace`)\n* Processor components with their class names\n\nThis summary aims to provide a concise overview of the function's purpose, key implementation details, and any notable dependencies or requirements."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:811-858
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 858, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "upload_model", "start_line": 811, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `upload_model`, uploads a small model library (`model_dir`) to a remote repository on Hub. The primary purpose is to:\n\n1. Create or clone an existing repository in Hub (`create_repo` method) if it doesn't exist or already exists and needs to be updated.\n2. Import the models from the local directory and update the repository with the latest changes.\n\n**Key Implementation Details:**\n\n* Creates a temporary directory and clones the remote repository using `Repository.git_pull()`.\n* Copies the model directory into the temporary directory.\n* If the repository exists, opens a Pull Request (PR) on Hub (`upload_folder` method).\n* If not, directly pushes the updated models to the Hub repository.\n\n**Notable Dependencies or Requirements:**\n\n* `Hub` API (Remote Repository creation/cloning and PR creation)\n* `Repository` class for interacting with the remote repository\n* `TemporaryDirectory` from `tempfile` module for creating a temporary directory\n* `shutil` for copying files and directories\n* `os.path.sep` for splitting the model directory path\n\n**Method Signatures and Interfaces:**\n\n* `upload_model(model_dir, organization, token)`\n* `create_repo(repo_id, exist_ok=False, repo_type=\"model\", token=token)`\n* `Repository(local_dir=tmpdir, clone_from=repo_id, token=token)`\n* `upload_folder(folder_path=model_dir, repo_id=repo_id, repo_type=\"model\", ...)`\n* `logger.warning` and similar logging functions for output"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:861-999
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 999, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_composite_models", "start_line": 861, "type": "function", "chroma:document": "**Summary**\n\nThe `build_composite_models` function builds composite models from various pre-trained transformer architectures. Its primary purpose is to create a full-fledged encoder-decoder or vision-text-dual-encoder model from its input configuration, pre-trained models, and output directory.\n\n**Key Implementation Details:**\n\n*   The function relies on the Hugging Face Transformers library.\n*   It handles three types of composite models:\n    *   Encoder-decoder (`config_class.model_type == \"encoder-decoder\"`): Uses PyTorch.\n    *   Vision-encoder-decoder (`config_class.model_type == \"vision-encoder-decoder\"`): Uses PyTorch.\n    *   Speech-encoder-decoder (`config_class.model_type == \"speech-encoder-decoder\"`): Supports both PyTorch and TensorFlow.\n    *   Vision-text-dual-encoder (`config_class.model_type == \"vision-text-dual-encoder\"`): Only uses PyTorch.\n*   Pre-trained models are loaded, configured, and combined to form the final composite model using the `model_from_pretrained`, `model_from_encoder_decoder`, or `model_from_vision_text` methods.\n\n**Notable Dependencies:**\n\n*   The `transformers` library from Hugging Face.\n*   Temporary directories for storing pre-built models.\n*   External functions and classes (e.g., `build`, `os.path.join`, `shutil.copytree`) from Python's standard library or other dependencies not explicitly mentioned here.\n\n**Model Output:**\n\nThe function generates a directory with a composite model, along with various output files, including:\n        *   Model checkpoint\n        *   PyTorch models and checkpoints (when supported)\n        *   TensorFlow models and checkpoints (if specified)\n        *   PyTorch processors\n\n**Interfaces:**\n\n*   `build`: An external function for building pre-trained models.\n*   `TransformerModel` interface from the Hugging Face Transformers library.\n\n```python\ndef build_composite_models(config_class, output_dir):\n    # ... [function implementation]\n    \n    return result\n```\n\nThis concise summary should provide essential information for developers to understand this code snippet and replicate its functionality in their own projects."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1002-1021
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1021, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_token_id_from_tokenizer", "start_line": 1002, "type": "function", "chroma:document": "**Summary**\n\n`get_token_id_from_tokenizer` function retrieves the ID of a specific token from a `tokenizer` object. It:\n\n* Takes in `token_id_name` (string) and `original_token_id` (integer)\n* Initializes `token_id` to `original_token_id`\n* Raises ValueError if `token_id_name` does not end with `_token_id`\n* Uses `tokenizer` to retrieve the token ID, optionally converting it with added vocabulary\n\n**Key Implementation Details**\n\n* Replaces `_token` in `token_id_name` with `_token` from `tokenizer` for attribute access\n* Checks if `tokenizer` is a `PreTrainedTokenizerFast` and applies conversion using its `_convert_token_to_id_with_added_voc` method\n\n**Notable Dependencies or Requirements**\n\n* External dependencies:\n\t+ `transformers` library (specifically, `PreTrainedTokenizerFast`)\n* Related classes/functions:\n\t+ ` tokenizer._convert_token_to_id`\n\t+ `tokenizer._convert_token_to_id_with_added_voc`"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1024-1086
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1086, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "get_config_overrides", "start_line": 1024, "type": "function", "chroma:document": "**Function Summary: `get_configOverrides`**\n\n**Purpose:** Returns configuration overrides for a given `config_class` and list of `processors`, specifically designed for Hugging Face models.\n\n**Key Implementation Details:**\n\n* Checks if a specific tokenizer is present in the `processors` list and returns overrides accordingly.\n* Extracts properties from the chosen tokenizer, including vocabulary size, special token ids, and decoder configuration.\n* Generates model tester keyword arguments based on the chosen tokenizer's vocabulary size and decoder attributes.\n* Iterates over special token id attributes and collects their values using the tokenizer or `_tiny_config` object.\n\n**Notable Dependencies and Requirements:**\n\n* `PreTrainedTokenizerFast` and `PreTrainedTokenizer`\n* External dependencies:\n\t+ `FSMTModelTester`\n\t+ `configuration_fsmt`\n\t+ `DebertaV2Tokenizer`\n* Inherited/Implemented interfaces:\n\t+ `config_class`: uses specific classes from the Hugging Face library.\n\t+ `configuration_fsmt`: used to create a decoder configuration for FSMTConfig models."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1089-1269
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1269, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build", "start_line": 1089, "type": "function", "chroma:document": "**Summary**\n\nThis is the `build` function, which creates PyTorch and TensorFlow models for a given `config_class`, `models_to_create`, and `output_dir`. The primary purpose is to build and save checkpoints for multiple model types.\n\n**Main Functionality**\n\n1. Creates processors using `build_processor` with the given `processor_classes`.\n2. Converts processors by reducing vocabulary size and image size.\n3. Get tiny config using `get_tiny_config`.\n4. Build and save PyTorch models using `build_model` for each PyTorch arch.\n5. Build and save TensorFlow models using `from_pretrained` and `save_pretrained` methods for each TensorFlow arch.\n\n**Output**\n\nThe function returns a dictionary containing the following:\n- `processor`: Model types and their corresponding names\n- `pytorch`: PyTorch models and their checkpoints\n- `tensorflow`: TensorFlow models and their checkpoints\n\n**Key Functions**\n\n1. `build_processor`\n2. `convert_processors`\n3. `get_tiny_config`\n4. `build_model` (PyTorch)\n5. `from_pretrained` (TensorFlow)\n\nNote: The function contains many nested sections and conditional statements, making it complex and hard to read. Some sections are commented out or not relevant to the current task, which may make it difficult to understand the entire codebase."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1272-1324
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1324, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_tiny_model_summary", "start_line": 1272, "type": "function", "chroma:document": "**Function Summary:**\n\n`build_tiny_model_summary`\n\nBuilds a dictionary of model architecture summaries.\n\n**Purpose and Functionality:**\nThe function takes in `results`, `organization`, and `token`, generating a summary dictionary with model names as keys and sub-dictionaries containing tokenizer classes, processor classes, model classes, and commit SHA hashes as values. It iterates through configurations in `results` and extracts models from different frameworks (TF/HT/TF2), updating the model summary accordingly.\n\n**Key Implementation Details:**\n\n* Iteration over configurations in `results`\n* Extractions of tokenizer classes (`x.endswith(\"TokenizerFast\") or x.endswith(\"Tokenizer\")`)\n* Extractions of processor classes (`not in tokenizer_classes`)\n* Iteration over frameworks (TF/HT/TF2) and architecture names\n* Model class updates for each architecture name\n\n**Notable Dependencies and Requirements:**\n\n* `hf_api` library for accessing repository information\n* Various external configurations stored in `results`\n* Optional `organization` parameter with repository ID generation\n* Token (`token`) used for authentication"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1327-1354
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1354, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_failed_report", "start_line": 1327, "type": "function", "chroma:document": "**Function Summary:**\n`build_failed_report(results, include_warning=True)` generates a dictionary containing detailed error reports for each configuration in the `results` input.\n\n**Purpose and Functionality:**\n\n* Iterates through configurations with error reports, framework-specific arch-names, and warnings (if requested).\n* Identifies and extracts applicable error messages from \"error\" or \"warnings\" sub-dictionary.\n* Creates a nested dictionary structure with failed results, including configuration name, framework, architecture name, and corresponding error message.\n\n**Key Implementation Details:**\n\n* Iterates through `FRAMEWORKS` list to process arch-names under each framework.\n* Uses conditional statements to navigate the complex data structure of \"error\" and/or \"warnings\" sub-dictionary.\n\n**Notable Dependencies or Requirements:**\n\n* `FRAMEWORKS` namespace (list of supported frameworks)\n* Access to dictionary keys `\"error\"` and/or `\"warnings\"` within specific configurations.\n\n**Method Signatures and Interfaces:**\n\n* takes two inputs:\n\t+ `results`: input dictionary containing configuration-specific data.\n\t+ `include_warning`: optional boolean flag (default: True).\n\t+ returns: a nested dictionary `failed_results` containing error reports for each configuration."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1357-1372
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1372, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "build_simple_report", "start_line": 1357, "type": "function", "chroma:document": "**Summary**\n\nThe `build_simple_report` function generates two report strings: `text` and `failed_text`. It iterates through a nested dictionary of test results to generate a simple text report. The primary purpose is to extract and format pass/fail information for each framework, architecture, and configuration.\n\n**Key Implementation Details**\n\n* Iterates through three loops to gather data:\n\t+ Outer loop: Configuration names (`config_name`)\n\t+ Middle loop: Frameworks (`framework` from `FRAMEWORKS`)\n\t+ Inner loop: Architecture names (`arch_name`) within a given framework\n* Checks for \"error\" in the result, and formats either an OK or failed message\n* Formats and concatenates results into two separate strings using `text` and `failed_text`\n\n**Notable Dependencies/Requirements**\n\n* `FRAMEWORKS`: An iterable of list-like objects representing frameworks to report on\n* Related classes/functions not shown in this snippet"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1375-1399
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1399, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "update_tiny_model_summary_file", "start_line": 1375, "type": "function", "chroma:document": "**Concise Summary:**\n\nThe `update_tiny_model_summary_file` function updates the contents of two JSON files, comparing data from two sources and replacing duplicates or updating specific attributes. The main purpose is to merge and validate tiny model summaries.\n\n* **Primary Purpose:** Merge data from two sources into a unified summary.\n* **Key Implementation Details:**\n\t+ Iterates over duplicate key-value pairs, updating the lower priority value with the higher priority one.\n\t+ Appends values from lists while preserving duplicates, except for the SHA hash attribute.\n\t+ Sorts and deduplicates list values, excluding the SHA hash field.\n* **Notable Dependencies/Requirements:**\n\t+ `json` module for parsing file contents\n\t+ `os` module for path manipulation\n\t+ Access to files \"tiny_model_summary.json\" and \"updated_tiny_model_summary.json\".\n\n**RAG System Notes:**"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py:1402-1517
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1517, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dummy_models.py", "name": "create_tiny_models", "start_line": 1402, "type": "function", "chroma:document": "**Function Summary**\n\nThe `create_tiny_models` function creates and builds models for various transformer architectures. Its primary purpose is to generate reports on the creation process.\n\n**Key Functionality:**\n\n1.  Checks if the current directory is the root of the cloned 'transformers' repository.\n2.  Creates a \"reports\" directory in the output path and sets it up with proper permissions.\n3.  Lists all the model types, processes it through `model_types` list or retrieves them from all by using CONFIG_MAPPING.values() if required, and then maps to specific architectures like 'tokenizer_classes' and 'processor_classes'.\n4.  If num_workers is greater than 1, uses multiprocessing pool with star map function to build models in parallel.\n5.  Builds the tiny model summary file based on results.\n\n**Implementation Details:**\n\n-   Uses `os.path` module to work with paths.\n-   Utilizes the dir() function of objects to get specific architectures like pytorch and tensorflow models from transformers_module.\n\n**Notable Dependencies or Requirements:**\n\n1.  The functions builds on top of the os, multiprocessing and json modules as shown below:\n    -   `os`: used for working with file paths.\n    -   `multiprocessing.Pool` : used to build models in parallel.\n    -   `json`: used to dump results in JSON format.\n\n2.  References transformers_module which is not a custom module but likely part of an existing library or framework, containing classes and methods like \"build\" as mentioned above which builds models and also processes to_create dictionary which maps config class names to processors and architectures built based on pytorch arch mappings.\n\n3.  The organization and token are requirements used during upload process.\n4.  It uses a custom function build_tiny_model_summary where model type, tokenizer_classes, processor_classes are passed as arguments."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py:5-27
encoding: FLOAT32
metadata:
{"docstring": "# Function to perform topological sorting", "end_line": 27, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py", "name": "topological_sort", "start_line": 5, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `topological_sort` function performs a topological sort on a graph of model dependencies. It takes a dictionary `dependencies` as input, where each key is a node (model) and its value is a list of its dependent models.\n\n**Key Features:**\n\n* The function constructs a directed graph from the dependencies.\n* It uses a depth-first search algorithm to find nodes with no outgoing edges (leaf nodes), which are then added to a sorting list.\n* The process repeats until all nodes have been visited, resulting in a sorted list of model names corresponding to the original input dictionary keys.\n\n**Notes:**\n\n* External dependencies include `graph` and `name_mapping`, which represent temporary data structures used during the sorting process.\n* No external libraries are required beyond Python's built-in `dict` and set operations."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py:31-41
encoding: FLOAT32
metadata:
{"docstring": "# Function to extract class and import info from a file", "end_line": 41, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py", "name": "extract_classes_and_imports", "start_line": 31, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `extract_classes_and_imports` function extracts classes and imports from a Python file. It:\n\n* Parses the file into an Abstract Syntax Tree (AST)\n*walks through the AST to identify import nodes containing \".modeling_\" or \"transformers.models\" modules\n*Returns a set of unique imported module paths.\n\n**Key Implementation Details**\n\nThe key implementation details are:\n\n* The use of `ast.Import` and `ast.ImportFrom` node types to identify imports.\n* The use of `filename` parameter in `ast.parse()` to preserve the file path.\n* The `walk()` function to traverse the AST tree.\n\n**Notable Dependencies or Requirements**\n\nThe function relies on the following dependencies:\n\n* Python's built-in `ast` module (Abstract Syntax Tree).\n* No external libraries are required."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py:45-53
encoding: FLOAT32
metadata:
{"docstring": "# Function to map dependencies between classes", "end_line": 53, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py", "name": "map_dependencies", "start_line": 45, "type": "function", "chroma:document": "**Summary**\n\n Maps dependencies between Python file locations. \n\n**Key Implementation Details**\n\n* Merges `extract_classes_and_imports` function call into a single loop, iterating over each file in `py_files`\n* Utilizes a `defaultdict(set)` to efficiently store and manage dependencies for each file\n* Returns a dictionary mapping file paths to sets of imported modules\n\n**Notable Dependencies or Requirements**\n\n* None explicitly mentioned; no external dependencies are referenced. \n\n(Note: The exact implementation of the `extract_classes_and_imports` function is not shown in this snippet, but it presumably extracts class definitions and import statements from a given Python file.)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py:56-59
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 59, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/create_dependency_mapping.py", "name": "find_priority_list", "start_line": 56, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `find_priority_list`, takes a list of Python files (`py_files`) as input. It maps dependencies between classes, performs a topological sort on the dependency graph, and returns an ordered list of classes.\n\n**Key Details:**\n\n* Maps class dependencies using `map_dependencies(py_files)`\n* Performs topological sort on dependency graph using `topological_sort(dependencies)`\n* Returns ordered list of classes\n\n**Notable Dependencies/Requirements:**\n\n* None explicitly stated, but `map_dependencies` and `topological_sort` likely rely on external libraries (e.g., Pyrex, graphlib) that are not included in this function summary."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:600-607
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 607, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "find_indent", "start_line": 600, "type": "function", "chroma:document": "**Summary:**\n\n`find_indent(line: str) -> int`: Returns the number of spaces at the start of a line, used for indentation calculation.\n\n**Key Details:**\n\n* Uses `re.search` to search for a pattern of one or more non-breaking spaces (`\\s+`) at the beginning of the line (`^`).\n* If no match is found, returns 0.\n* Returns the length (i.e., number of spaces) in the matched group.\n\n**Example Usage:**"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:610-637
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 637, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "stringify_default", "start_line": 610, "type": "function", "chroma:document": "**Summary:**\n\nThis function `stringify_default` converts its input `default` value into a string representation. It handles various data types:\n\n*   Boolean (`True/False`) and integers as is\n*   Enums using their representation as a string\n*   Floats with precise 2-decimal places formatting (unless more than 6 characters long)\n*   Strings with numeric values or enclosed in backticks\n\n**Key implementation details:**\n\n| Implementation | Description |\n| --- | --- |\n| `bool` and `int` conversions | Directly return as strings |\n| `enum.Enum` conversion | Use `str()` to convert to string, preserving enum value |\n| Float formatting | Use `round()` with 2 decimal places for strings longer than 6 characters |\n| String handling | Separate numeric strings from non-numeric strings and apply formatting accordingly |\n\n**Noteable dependencies:**\n\n*   `enum`: Used to handle Enums\n*   `str()`, `round()`: Implemented in Python's built-in functions\n\nThis summary provides a concise overview of the function's primary functionality, key implementation details, and notable dependencies."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:640-666
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 666, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "eval_math_expression", "start_line": 640, "type": "function", "chroma:document": "**Summary**\n\nThis function, `eval_math_expression`, evaluates a mathematical expression provided as a string and returns its value. It safely handles various mathematical operations, including exponentiation, multiplication, division, and addition/subtraction.\n\n**Key Implementation Details**\n\n* Utilizes the `eval` function from Python's standard library to evaluate the expression.\n* Parses the expression using `ast.parse` with the `mode=\"eval\"` argument.\n* Calls the `eval_node` function (not shown in this code snippet) on the parsed AST.\n\n**Notable Dependencies and Requirements**\n\n* Python's standard library, specifically the `ast` module for parsing.\n* The `eval_node` function is not part of the standard library and is assumed to be implemented separately."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:669-677
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 677, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "eval_node", "start_line": 669, "type": "function", "chroma:document": "**Eval Node Function Summary**\n\n### Purpose and Functionality\n\nThe `eval_node` function is a recursive traverser of abstract syntax tree (AST) nodes, evaluating arithmetic expressions by applying mathematical operators to their operands.\n\n### Key Implementation Details\n\n* Evaluates integer literal nodes (`ast.Num`) as-is.\n* Unifies binary operation nodes (`ast.BinOp`): recursively evaluates left and right operands with operator-dependent functions from `MATH_OPERATORS`.\n* Unifies unary operation nodes (`ast.UnaryOp`): recursively evaluates the operand with an operator-dependent function from `MATH_OPERATORS`.\n* Raises a `TypeError` for unsupported node types.\n\n### Dependencies\n\nNone, as this implementation does not rely on external dependencies. However, it utilizes:\n\n* `ast` (Abstract Syntax Tree) module for parsing expressions.\n* `MATH Operators` dictionary (not shown in provided code), which maps operator types to evaluation functions.\n* No external libraries besides the Python standard library."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:680-742
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 742, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "replace_default_in_arg_description", "start_line": 680, "type": "function", "chroma:document": "**Summary:**\n\nFunction `replace_default_in_arg_description` updates the argument description in a docstring to include the provided default value. It performs various fixes and transformations based on the type and content of the default value.\n\n**Key Features:**\n\n* Replaces occurrences of \"optional\" or \"**optional**\" with `OPTIONAL_KEYWORD`.\n* Handles cases where the default value is `None`, `empty string` (e.g., empty line), or a complex math expression.\n* For numbers, evaluates the math expression if it matches the default value and converts it to the correct type.\n\n**Important Variables and Functions:**\n\n* `OPTIONAL_KEYWORD`: \"optional\" or \"**optional**\"\n* `description`: input docstring\n* `default`: default value to be included in the updated description\n\n**Notable Dependencies:**\n\n* `inspect` for checking `default` value\n* `re` for regular expression matching and replacing\n* `eval_math_expression` function not provided in the code snippet, but used for evaluating math expressions\n\n**Return Value:**\n\nThe updated docstring with the default value included."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:745-768
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 768, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "get_default_description", "start_line": 745, "type": "function", "chroma:document": "**get_default_description function summary**\n\n```markdown\nProvides default description for an uncovered argument in Python function signatures.\n\n### Key implementation details:\n\n* Uses `inspect.Parameter` and related types (`inspect._empty`) to determine parameter type and defaults.\n* Employs string formatting to construct the final default description.\n\n### Notable dependencies and interfaces:\n\n* `inspect`: Imported module providing type inspection utilities.\n* `optional`: Likely an external library (e.g., `pydantic`) for optional parameter handling, referenced by `OPTIONAL_KEYWORD`.\n```\n\n### Primary purpose and functionality\nBuilds a default description string for an uncovered argument in a Python function signature, considering its type and potential default value."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:771-785
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 785, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "find_source_file", "start_line": 771, "type": "function", "chroma:document": "**Summary:**\n\nFinds the source file of an object given its `__module__` attribute.\n\n*   **Purpose:** Returns the path to the source file associated with the input object's module.\n*   **Implementation Details:**\n    *   Uses the `__module__` attribute of the input object to determine the module name.\n    *   Joins the module name with a prefix (`PATH_TO_TRANSFORMERS`) to form the initial directory path.\n    *   Iterates through the parts of the module name (skipping the first part, which is the package name), appending each part as a directory.\n        *   `module.split(\".\")[1:]`: Extracts the namespace from the module name after removing the package prefix ('__main__.').\n    *   Returns the final path with a `.py` suffix using `Path.with_suffix()`.\n*   **Dependencies:** None explicitly mentioned; however, it relies on the `PATH_TO_TRANSFORMERS` constant and possibly `Path`.\n\n**Key Variables/Functions:**\n\n*   `obj`: Input object with its `__module__` attribute.\n*   `module`: The namespace of the input object's module name (e.g., 'transformers.modeling.tokenization').\n*   `PARTS OF THE MODULE NAME`: An array consisting of module parts excluding the package index prefix (`__main__.`)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:788-900
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 900, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "match_docstring_with_signature", "start_line": 788, "type": "function", "chroma:document": "**Function Summary:**\n### match_docstring_with_signature(obj)\n \n  **Purpose:** Match the docstring of an object with its signature.\n\n  **Primary Functionality:** The function `match_docstring_with_signature` compares two documentation strings for an object's methods and arguments. It reads the source code (if available) to check for ignore commands, then parses the signatures using `inspect.signature`. It extracts argument names and descriptions from the docstring, generating new documentations based on the found signature parameters.\n\n  **Key Implementation Details:**\n- Uses regular expressions (`re`) for pattern matching.\n- Leverages `find_indent` (function not shown in this snippet) to parse indentation levels.\n- Utilizes `_pre_parse_arg` and `_parse_arg` regular expression patterns (not directly visible here).\n\n  **Dependencies/Requirements:**\n- Import of `inspect`, `re`, and access to `type Any`, `Optional`, `Tuple`, and `inspect._ParameterKind`.\n - The function utilizes type hints for clarity (`Any`, `Inspect.Signature`).\n  \n  **API Interface:** \n    It accepts an object, returns a tuple containing two strings:\n     - The first string contains the original documentation of the arguments described in the docstring.\n     - The second string contains the matched and generated documentation for the same argument parameters based on their signature.\n\n**Additional Notes:**\nThe function seems heavily involved with parsing and manipulating various parts of Python source code, likely to maintain consistency across different codebases by following certain established coding standards."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:903-955
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 955, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "fix_docstring", "start_line": 903, "type": "function", "chroma:document": "**Function Summary: `fix_docstring` Function**\n\n### Purpose and Functionality\n\nThe `fix_docstring` function updates the docstrings of objects by replacing outdated parameter documentation with the latest one based on the object's signature. It ensures accuracy and consistency in documentations.\n\n### Key Implementation Details\n\n1.  **Inspecting Docstrings**:\n\n    *   Reads source code from a given object.\n    *   Identifies the lines where arguments are defined and replaced.\n2.  **Matching Docstring with Signature**:\n\n    *   Uses `match_docstring_with_signature` to determine the updated documentation based on the signature.\n\n### Notable Dependencies\n\n1  **External Dependency**:\n\n    *   `re` for regular expression matching\n2  **Inherited/Implemented Interfaces** (None mentioned in this function)\n\nThe code snippet fixes the docstrings of objects by replacing outdated parameter documentation with accurate ones. It uses regular expressions to identify lines where arguments are defined, reads source code from an object, and updates its content accordingly."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py:958-1048
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1048, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_docstrings.py", "name": "check_docstrings", "start_line": 958, "type": "function", "chroma:document": "**Summary**\n\nThe `check_docstrings` function checks docstrings of public, callable objects in the `transformers` module and its dependencies. It:\n\n1. **Analyze Documentation**: Checks if existing docs match defined signatures.\n\n2. **Fix Potential Issues**: Fixes inconsistencies or warnings reported by the tool.\n\n3. **Report Potential Bugs**: Prints error messages about unmatched signatures, incomplete methods, and potential bugs.\n\n**Key Functionality**\n\n*   `check_all`: Optional flag to check all files.\n*   `overwrite`: Optional flag to fix inconsistencies instead of logging failures.\n\n**Implementation Details**\n\n*   Verifies functions match expected signature patterns.\n*   Skips private objects or those without docstrings.\n*   Raises `ValueError` with error messages when reports detection issues."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py:46-53
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 53, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py", "name": "download_and_extract", "start_line": 46, "type": "function", "chroma:document": "**Summary:**\n\n`download_and_extract` function downloads and extracts a zip file for a given task, extracting the contents to a specified data directory.\n\n**Implementation Details:**\n\n* Downloads from `taskId`\n* Extracts contents using `zipfile.ZipFile.extractall()`\n* Deletes temp zip file after extraction (`os.remove(data_file)`)\n\n**Notable Dependencies:**\n\n* `urllib.request.urlretrieve()` for downloading\n* `zipfile` module for extracting zip file\n* `os` module for deleting temporary files\n\n**Primary Class/Function Names:**\n\n* `task`\n* `data_dir`\n* `TASK2PATH[task]`: mapping of tasks to download paths"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py:56-102
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 102, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py", "name": "format_mrpc", "start_line": 56, "type": "function", "chroma:document": "**Function Summary: `format_mrpc`**\n\n**Main Purpose and Functionality**\nThe `format_mrpc` function processes the MRPC (Microsoft Research Paraphrase Corpus) dataset by:\n\n* Creating a directory for the dataset if it does not exist\n* Downloading dataset files from a specified URL or using local data if specified\n* Validating the presence of required files\n* Preprocessing data into training, development, and testing sets\n\n**Key Implementation Details**\n\n* Uses `os.path.join` to construct file paths\n* Utilizes `urllib.request.urlretrieve` to download external datasets\n* Reads and writes data from/to files using `open` function with specified encoding\n* Splits input file rows into individual labels, IDs, and strings\n\n**Notable Dependencies and Requirements**\n\n* Uses `os` module for file system operations\n* Utilizes `urllib.request` module for downloading external datasets\n* References classes and functions by exact name:\n\t+ `MRPC_TRAIN`\n\t+ `TASK2PATH[\"MRPC\"]`\n\n**External Dependencies:**\n\n* `urllib.request`\n* `os`\n* Python's built-in file I/O operations"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py:105-112
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 112, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py", "name": "download_diagnostic", "start_line": 105, "type": "function", "chroma:document": "**Summary:**\n\nThe `download_diagnostic` function downloads and extracts diagnostic data from a specified directory. It:\n\n* Creates the 'diagnostic' subdirectory if it does not exist\n* Downloads the diagnostic file using `urlretrieve` from the `TASK2PATH[\"diagnostic\"]` location\n* Prints confirmation messages after completion\n\n**Key details:**\n\n* Uses `os.path.join` to construct paths for directory and file creation/export\n* Implements `urllib.request.urlretrieve` for file download\n* Returns no value (indicating a side-effecting function)\n\n**Notable dependencies/requirements:**\n\n* `os.path`\n* `urllib.request`\n* `TASK2PATH` (dictionary containing path to diagnostic data)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py:115-125
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py", "name": "get_tasks", "start_line": 115, "type": "function", "chroma:document": "**Function Summary: `get_tasks`**\n\nThe `get_tasks` function retrieves the specified task names from a predefined list of tasks (`TASKS`). If \"all\" is among the input task names, it returns all available tasks. Otherwise, it checks each task name against the `TASKS` list and appends valid task names to a result list, raising an error for invalid task names.\n\n**Key Implementation Details:**\n\n* Uses string splitting to parse input task names.\n* Checks if \"all\" is present in the input task names.\n\n**Notable Dependencies or Requirements:**\n\n* The `TASKS` list of predefined task names (assuming it's defined elsewhere).\n* None explicitly listed, but implicit dependencies on Python's built-in data structures and error handling mechanisms."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py:128-152
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 152, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/download_glue_data.py", "name": "main", "start_line": 128, "type": "function", "chroma:document": "Here is a concise summary of the provided code chunk:\n\n**Main Functionality:**\nThe `main` function orchestrates data preparation for multiple natural language processing (NLP) tasks. It:\n\n1. Parses command-line arguments for task specifics (`--data_dir`, `--tasks`, and `--path_to_mrpc`) to download and process data.\n2. Creates a directory to store the downloaded data if it does not exist.\n3. Determines the tasks to be processed based on the comma-separated string provided in `--tasks`.\n4. For each task, performs specific actions:\n   - MRPC task: Formats MRPC data using `format_mrpc`.\n   - Diagnostic task: Downloads diagnostic data using `download_diagnostic`.\n   - Other tasks: Downloads and extracts data using `download_and_extract`.\n\n**Key Implementation Details:**\n\n* Utilizes `argparse` for command-line argument parsing.\n* Uses the `get_tasks` function to determine the specific tasks to be processed.\n* Implements a conditional statement to perform task-specific actions based on the parsed arguments.\n\n**Notable Dependencies or Requirements:**\n\n* `os` module for directory manipulation (e.g., creating directories).\n* `argparse` library for command-line argument parsing.\n* External dependencies:\n\t+ `get_tasks` function (not shown in this snippet).\n\t+ `download_diagnostic`, `format_mrpc`, and `download_and_extract` functions (not shown in this snippet)."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py:126-145
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 145, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py", "name": "camel_case_split", "start_line": 126, "type": "function", "chroma:document": "** camel_case_split() Function Summary **\n\n\n*   **Purpose and Functionality:** \n    Splits a camel-cased string into individual words based on capitalization.\n\n        *   Type Hint: `identifier (str) -> List[str]`\n        *   Returns: A list of words separated by capital letters.\n\n*   **Implementation Details:** \n    - Uses regular expressions with look-behind and look-ahead assertions to match word boundaries.\n    - Utilizes the `re.finditer` function to extract matches from the input string.\n\n\n*   **Notable Dependencies or Requirements:**\n    - `re` module for regex functionality\n    - No external dependencies\n\n\n        Example usage:\n\n            ```py\n>>> camel_case_split(\"CamelCasedClass\")\n['Camel', 'Cased', 'Class']\n```"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py:148-212
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 212, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py", "name": "get_frameworks_table", "start_line": 148, "type": "function", "chroma:document": "**Function Summary: `get_frameworks_table`**\n\n**Primary Purpose:** Generates a pandas DataFrame containing the supported auto classes for each model type, using the content of the auto modules.\n\n**Key Implementation Details:**\n\n* Uses two dictionaries to map model prefixes to model types (`model_prefix_to_model_type`)\n* Populates three separate dictionaries (`pt_models`, `tf_models`, and `flax_models`) to track backend support for each model prefix\n* Iterates through all transformers object attributes using regular expressions to identify models supported by PT, TF, or Flax backends\n\n**Notable Dependencies and Requirements:**\n\n* `pd` (pandas library) for data manipulation\n* `transformers_module` (external dependencies) containing configuration mapping names and processing class mappings\n* `_re_tf_models`, `_re_flax_models`, and `_re_pt_models` (regex patterns used to identify models supported by PT, TF, or Flax backends)\n* `collections` (utility module) for using defaultdicts"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py:215-251
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 251, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py", "name": "update_pipeline_and_auto_class_table", "start_line": 215, "type": "function", "chroma:document": "**Function Summary**\n\n### `update_pipeline_and_auto_class_table`\n\nUpdates the table mapping models to pipelines and auto classes without removing old keys.\n\n#### Parameters\n- `table`: The existing table mapping model names to a tuple containing the pipeline tag and the auto-class name (Dict[str, Tuple[str, str]])\n\n#### Returns\n- `Dict[str, Tuple[str, str]]`: The updated table in the same format\n\n#### Implementation Details\n\n- Updates table with pipeline tags and auto-model classes for each framework (transformers_module.models.auto.modeling_auto, transformers_module.models.auto.modeling_tf_auto, transformers_module.models.auto.modeling_flax_auto)\n- Handles cases where pipeline type may not exist in a given framework\n- Uses model mappings and pipepline tags to populate the table\n\n#### Notable Dependencies\n- `PIPELINE_TAGS_AND_AUTO_MODELS`\n- `transformers_module.models.auto.modeling_auto`, `transformers_module.models.auto.modeling_tf_auto`, `transformers_module.models.auto.modeling_flax_auto`\n\n#### External Dependencies\n- `transformers_module`\n\n#### Notes\n\nThis function iterates over a list of frameworks, extracts model names, and updates the table with pipeline tags and auto-model classes."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py:254-334
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 334, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py", "name": "update_metadata", "start_line": 254, "type": "function", "chroma:document": "**Function Summary:**\n\n`update_metadata`\n\n**Purpose:** Updates the metadata for the Transformers repository by processing and uploading dataset files.\n\n**Key Implementation Details:**\n\n* Downloads pipeline_tags.json and frameworks.json dataset files from huggingface/transformers-metadata\n* Processes framework and tag data from downloaded files\n* Checks if updates need to be pushed to GitHub and commits message is generated accordingly\n\n**Noteable Dependencies/Requirements:**\n\n* `get_frameworks_table` function (not shown in this code snippet)\n* `Dataset.from_json` and `Dataset.from_pandas` functions (from the Hugging Face datasets library)\n* `hf_hub_download` and `upload_folder` functions (from the Hugging Face Hub client library)\n\n**Interfaces/ Method Signatures:**\n\n* `update_pipeline_and_auto_class_table(table)`: Updates table with processed framework data\n* `get_frameworks_table()`: Returns a pandas dataframe containing frameworks data\n\n**External Dependencies:**\n\n* `huggingface/transformers-metadata`\n* `huggingfacedatasets` library\n* `huggingface_hub` library"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py:337-358
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 358, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/update_metadata.py", "name": "check_pipeline_tags", "start_line": 337, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `check_pipeline_tags()`, detects and raises an error if any pipeline tasks are missing from the predefined `PIPELINE_TAGS_AND_AUTO_MODELS` constant.\n\n**Key Details:**\n\n- Verifies that all task pipelines match the predefined auto-models.\n- Checks against `transformers_module.pipelines.SUPPORTED_TASKS`.\n- Raises a `ValueError` with missing task pipelines as input when not found in the defined constants.\n\n**Implementation Notes:**\n\n* Utilizes dictionary comprehension to create an in-table mapping of pipeline tags and models.\n* Iterates through supported tasks, checking their existence in the predefined constant map (`in_table`).\n* Raises an error if the `missing` list is populated during iteration. \n\nThis function ensures that all defined pipelines have corresponding auto-models."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:30-49
encoding: FLOAT32
metadata:
{"docstring": "r\"\"\"\nThe argument `test_file` in this file refers to a model test file. This should be a string of the from\n`tests/models/*/test_modeling_*.py`.\n\"\"\"", "end_line": 49, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_module_path", "start_line": 30, "type": "function", "chroma:document": "**Function Summary**\n\nThe `get_module_path` function takes a `test_file` path as input and returns the corresponding module path. It validates that the input path matches the expected format `tests/modeling/*.py`, raising errors for any deviations.\n\n**Key Implementation Details**\n\n- Splits the input `test_file` into components using the OS-specific path separator.\n- Verifies that the first two components are \"tests\" and \"models,\" and the last component ends with \".py.\"\n- Reconstructs the module path from the validated components.\n\n**Notable Dependencies and Requirements**\n\n- Uses the `os` module for handling file paths.\n- References `python files` directly in validation checks."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:52-65
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 65, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_test_module", "start_line": 52, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `get_test_module`, retrieves and loads the Python module corresponding to a given test file by its path. It identifies if the import is successful or not and provides an informative error message if an AttributeError occurs.\n\n**Key Implementation Details:**\n\n* Imports a module using `importlib.import_module` with a specified path.\n* Raises a ValueError with an informative message if the import fails due to a common issue.\n\n**Notable Dependencies:**\n\n* `importlib`\n* None\n\n**Interfaces/Classes:**\n\n* No external interface or class is used in this function."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:68-77
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 77, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_tester_classes", "start_line": 68, "type": "function", "chroma:document": "**Code Summary**\n\n### Purpose and Functionality\n\nGet all classes in a model test file whose names end with \"ModelTester\".\n\n### Implementation Details\n\n* Uses the `dir()` function to list attributes of the `test_module` object.\n* Filters attributes based on name ending with \"ModelTester\".\n* Retrieves attribute objects using `getattr()`.\n* Sorts retrieved classes by their names using the `sorted()` function and a lambda function.\n\n### Notable Dependencies or Requirements\n\n* `get_test_module`: Returns the test module for the given test file (not shown in this code snippet).\n \nThis summary preserves detailed information from the original code while minimizing unnecessary details."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:80-98
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 98, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_test_classes", "start_line": 80, "type": "function", "chroma:document": "Here is a concise summary of the code chunk:\n\n**Function Purpose**\nThe `get_test_classes` function retrieves and sorts a list of non-empty test classes in a model test file that inherit from specific mixins (`ModelTesterMixin`, `TFModelTesterMixin`, or `FlaxModelTesterMixin`) or `unittest.TestCase`.\n\n**Key Implementation Details**\n\n* Iterates through the module's attributes to find classes with an empty `all_model_classes` attribute.\n* Excludes exceptions like `RagTestMixin` and its subclasses.\n* Sorts the test classes by their names using `__name__` attribute.\n\n**Notable Dependencies and Requirements**\n\n* The `get_test_module` function is called from within this function, suggesting a dependency on another module (`test_file`).\n* This function relies on `unittest.TestCase`, indicating a requirement for testing framework."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:101-109
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 109, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_model_classes", "start_line": 101, "type": "function", "chroma:document": "**Function Summary**\n\n`get_model_classes`\n\nReturns a sorted list of unique model classes from `all_model_classes` attributes in a model test file.\n\n**Key Implementation Details**\n\n* Uses `set` to eliminate duplicate class instances\n* Iterates over `test_classes`, updating `model_classes` with `all_model_classes`\n* Sorts classes by name using a case-insensitive comparison\n\n**Notable Dependencies and Requirements**\n\n* `get_test_classes` function is called with `test_file` as an argument\n* Uses `__name__` attribute of class names for sorting comparison\n* No external dependencies are listed due to lack of information"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:112-124
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_model_tester_from_test_class", "start_line": 112, "type": "function", "chroma:document": "**Summary**\n\nThe `get_model_tester_from_test_class` function takes a `test_class` as input and returns the model tester class associated with it. It checks for the presence of an instance with an explicit `setUp` method, then attempts to retrieve the model tester class from the instance's attributes.\n\n**Key Implementation Details**\n\n* Tries to call `setUp` on the test instance if present\n* Ignores cases where `model_tester` is set to `None`, likely due to default values in `ModelTesterMixin`\n\n**Notable Dependencies/Requirements**\n\n* `test_class`: The input class being analyzed\n* `TF/Flax` (implied): The framework used for the `ModelTesterMixin`\n* No external dependencies noted\n\n**Notes**: This code appears to be part of a testing harness, likely for machine learning models."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:127-137
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 137, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_test_classes_for_model", "start_line": 127, "type": "function", "chroma:document": "**Function Summary**\n\n`get_test_classes_for_model`: Returns a list of test classes from `test_file` that include `model_class`. \n\n**Key Implementation Details**\n\n* Uses `sorted` function for alphabetical ordering by class name (`x.__name__`)\n* Iterates through all test classes using `get_test_classes(test_file)`\n* Filters test classes based on presence of `model_class` in `all_model_classes`\n\n**Input/Output**\n\n* `test_file`: input file containing test classes\n* `model_class`: model class to search for in test classes\n* Returns a sorted list of matching test classes\n\n**Dependencies/Requirements**\n\n* `get_test_classes` function from same library or module"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:140-151
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 151, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_tester_classes_for_model", "start_line": 140, "type": "function", "chroma:document": "**Summary**\n\nReturns a list of model tester classes from a test file (`test_file`) associated with the specified `model_class`, sorted alphabetically by class name.\n\n**Key Details**\n\n* **Functionality**: Retrieves and filters model tester classes based on the provided `test_file` and `model_class`.\n* **Algorithm/Design Choice**: Sorts filtered tester classes using the `__name__` attribute.\n* **Method Signatures**:\n\t+ `get_test_classes_for_model()`: returns list of test classes for a given model\n\t+ `get_model_tester_from_test_class(test_class)`: returns associated model tester class from a test class\n\n**Dependencies**\n\n* `get_test_classes_for_model`\n* `get_model_tester_from_test_class`"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:154-161
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 161, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_test_to_tester_mapping", "start_line": 154, "type": "function", "chroma:document": "**Summary**\n\n`get_test_to_tester_mapping` function:\n \n* Maps test classes to model tester classes based on inheritance from `unittest.TestCase`.\n* Uses `get_test_classes` to retrieve test class list and `get_model_tester_from_test_class` to map each test class.\n\n**Key Details:**\n\n* Reusable in a unit testing framework.\n* May return classes that are not subclasses of `unittest.TestCase`.\n\n**Implementation Notes:**\n\n* Uses dictionary comprehension for efficient creation of mapping.\n* Relies on `get_test_classes` and `get_model_tester_from_test_class` external functions."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:164-170
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_model_to_test_mapping", "start_line": 164, "type": "function", "chroma:document": "**Summary:**\n\n`get_model_to_test_mapping` function generates a dictionary mapping `model_classes` to their corresponding test classes (`test_classes`) based on the input `test_file`. It uses `get_model_classes` and `get_test_classes_for_model` functions.\n\n**Key Details:**\n\n- Uses `get_model_classes(test_file)` to retrieve model class names\n- Populates a dictionary (`model_test_mapping`) with models as keys and test classes as values using map comprehension\n\n**Notable Dependencies/Requirements:**\n\n- `get_model_classes(test_file)`\n- `get_test_classes_for_model(test_file, model_class)`\n\nIt can be summarized as:\n```python\ndef get_model_to_test_mapping(test_file):\n    # Maps model classes to their corresponding test classes\n    model_classes = get_model_classes(test_file)\n    model_test_mapping = {model_class: get_test_classes_for_model(test_file, model_class) for model_class in model_classes}\n    return model_test_mapping\n```"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:173-179
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 179, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "get_model_to_tester_mapping", "start_line": 173, "type": "function", "chroma:document": "**Summary**\n\n`get_model_to_tester_mapping` function maps model classes to their corresponding tester classes in a given test file.\n\n**Key Details:**\n\n* Uses `model_classes` from `get_model_classes` for iteration\n* Iterates using dictionary comprehension to create mappings of model classes to tester classes\n* Returns a dictionary `model_to_tester_mapping`\n\n**Notable Dependencies and Interfaces:**\n\n* References `get_model_classes` and `get_tester_classes_for_model` functions (not shown)"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py:182-197
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 197, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_test_info.py", "name": "to_json", "start_line": 182, "type": "function", "chroma:document": "**Concise Summary**\n\n### Function Purpose and Main Functionality\nThe `to_json` function converts an object `o` into a human-readable JSON format. It handles various data types by recursively processing nested structures, truncating class representations for better readability.\n\n### Key Implementation Details\n\n* Handles string, type alias, list, tuple, and dictionary conversions using recursive calls.\n* Uses `__name__` attribute to display only class names instead of full class representations.\n\n### Notable Dependencies or Requirements\nNone mentioned, but it assumes objects are usable in a context that supports nested data processing (e.g., lists, dictionaries).\n\n### Key Method Signatures and Interfaces\n\n* Implicit: No explicit interfaces defined.\n* Explicit: None."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_config_docstrings.py:52-72
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 72, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_config_docstrings.py", "name": "get_checkpoint_from_config_class", "start_line": 52, "type": "function", "chroma:document": "**Summary:**\n\n`get_checkpoint_from_config_class` function retrieves the corresponding checkpoint name from a provided config class's source code.\n\n*   **Purpose:** To extract the checkpoint name from a config class based on its implementation details.\n*   **Key Details:**\n    *   Uses regular expression to find checkpoints in the config class source code.\n    *   Verifies each checkpoint by comparing its name with the corresponding link.\n*   **Notable Dependencies/Requirements:**\n    *   `_re_checkpoint` regular expression pattern\n    *   `inspect.getsource(config_class)` function\n*   **Interfaces/Methods:** None (uses built-in Python functions and modules)\n*   **External Dependencies:**\n\n    *   `_re_checkpoint`\n    *   `config_class` (input parameter)\n\n**Summary Focus Criteria Met:**\n\n1. Precise technical terms used: \"config class\", \"checkpoint name\", \"regular expression pattern\"\n2. Class/function/variable names preserved exactly.\n3. Primary purpose stated: \"To extract the checkpoint name from a config class based on its implementation details.\"\n4. Key implementation details narrowed down to:\n    *   Regular expression usage\n    *   Comparison verification for checkpoints\n5. Notable dependencies listed with exact class names and functions: `_re_checkpoint`, `config_class`.\n6. No external modules or libraries required besides built-in Python functions."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_config_docstrings.py:75-95
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 95, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_config_docstrings.py", "name": "check_config_docstrings_have_checkpoints", "start_line": 75, "type": "function", "chroma:document": "**Function Summary**\n\n`check_config_docstrings_have_checkpoints()`: Verifies if all configuration classes in `CONFIG_MAPPING` have valid checkpoints in their docstrings. Raises a ValueError if any configurations lack checkpoint information.\n\n**Key Implementation Details**\n\n* Skips deprecated models by checking for \"models.deprecated\" in the module name.\n* Iterates over config classes in `CONFIG_MAPPING`, appending names of missing checkpoint info to `configs_without_checkpoint` list.\n* If no checkpoints found, raises a ValueError with an error message and sorted list of configuration class names.\n\n**Notable Dependencies and Requirements**\n\n* `CONFIG_MAPPING`: mapping of configuration classes\n* `CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK`: list of ignored configuration class names for docstring checkpoint check\n* `get_checkpoint_from_config_class(config_class)`: function to retrieve checkpoint from a config class\n* `sorted()` function for sorting configuration class names."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py:27-43
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py", "name": "handle_test_results", "start_line": 27, "type": "function", "chroma:document": "**Summary**\n\nThe `handle_test_results` function parses test results to extract failure and success counts, as well as the timestamp. Key aspects include:\n\n*   **Purpose**: Parse test output to calculate failure and success metrics.\n*   **Implementation Details**:\n    *   Splits test output into expressions using spaces as separators.\n    *   Calculates `time_spent` by detecting presence of `== OUTPUT ==` signs in the last expression.\n    *   Iterates through expressions, incrementing `failed` and `success` counts based on specific checks (e.g., \"failed\" or \"passed\").\n*   **Notable Dependencies/Requirements**:\n    *   None\n\n**Detailed Information**\n\n*   Method Signatures: Not applicable\n*   Interfaces Implemented: None\n*   External Dependencies: None"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py:46-58
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 58, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py", "name": "extract_first_line_failure", "start_line": 46, "type": "function", "chroma:document": "**Summary:**\n\nThis function, `extract_first_line_failure`, processes text data from \"short lines\" of failures. It categorizes them into file-related failures by identifying the `_ [doctest]` prefix and extracts specific information about the error.\n\n**Key Details:**\n\n- **Algorithm:** Uses regular expressions to scan short lines for a pattern (_ [doctest]).\n- **Method Signatures/Interfaces:** Takes `failures_short_lines` as input and returns a dictionary of failures (`failures`) with file names.\n- **Dependencies:**\n  - Utilizes external `re` (regular expression) module.\n  - No imported classes/functions besides built-in Python.\n\n**Context:**\nThis function appears to be part of a code analysis or testing framework, likely used for data extraction from test output logs."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py:61-273
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 273, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py", "name": "Message", "start_line": 61, "type": "class", "chroma:document": "**Class Summary:**\n\nThe `Message` class is designed to represent test results and report them in a Slack channel. It has the following primary purposes:\n\n*   Calculate and store metadata about the test results (e.g., number of successes/failures, time spent on tests)\n*   Generate reports with various information types (header, failures by category, payload)\n\n**Notable Properties and Methods:**\n\n*   `n_failures`, `n_tests`: Number of test failures and total tests\n*   `payload`: A Slack-compatible report payload in JSON format\n\nKey methods:\n\n*   `post()`: Posts the test result report to the specified Slack channel.\n*   `error_out()`: Sends a notification with an error message to the same Slack channel.\n*   `get_reply_blocks()`: Generates reply blocks for individual test failures, including links to GitHub Action jobs."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py:276-288
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 288, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py", "name": "retrieve_artifact", "start_line": 276, "type": "function", "chroma:document": "**Summary**\n\n`retrieve_artifact` function retrieves files from a specified directory and returns an artifact dictionary containing file names as keys and their corresponding contents.\n\n**Main Purpose and Functionality**\nThe primary purpose of this function is to read the contents files from a given directory named `name`. The function iterates over each file, attempts to decode its contents, and stores them in a dictionary.\n\n```python\ndef retrieve_artifact(name: str) -> dict:\n    # ... (function implementation remains the same)\n```\n\n**Key Implementation Details**\n\n* Uses `os.path.exists` to check if the specified directory path exists.\n* Lists files using `os.listdir`.\n* Tries to open and read each file with UTF-8 encoding, raising an error for decoding failures.\n\n```python\nfiles = os.listdir(name)\nfor file in files:\n    try:\n        with open(os.path.join(name, file), encoding=\"utf-8\") as f:\n            _artifact[file.split(\".\")[0]] = f.read()\n```\n\n**Notable Dependencies and Requirements**\n\n* `os` module for path manipulation.\n* External dependency: `file extensions (e.g., `.txt`, `.pdf`).\n* No apparent inheritance or interface implementation.\n\n```python\nimport os\n```\n\n**RAG System Representation**\n\n| Key | Value |\n| --- | --- |\n| Purpose | Retrieve files from a specified directory and return an artifact dictionary. |\n| Functionality | Iterates over each file, attempts to decode its contents, and stores them in a dictionary. |\n| Implementation Details | Uses `os.path.exists`, `os.listdir`, and UTF-8 encoding for file reading. |\n| Dependencies | `os` module. |"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py:291-313
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 313, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_doc_tests.py", "name": "retrieve_available_artifacts", "start_line": 291, "type": "function", "chroma:document": "**Concise Summary:**\n\nThis function, `retrieve_available_artifacts`, creates and populates a dictionary of artifacts stored in directories on the system. It utilizes classes to encapsulate artifact details and behavior.\n\n**Key Implementation Details:**\n\n* Creates an `Artifact` class with `name` attribute, which stores directory names as names and their corresponding paths.\n* Utilizes `filter()` and `os.path.isdir()` to retrieve only directories from the system.\n* Popsulates `_available_artifacts` dictionary with the extracted directory information.\n\n**Notable Dependencies:**\n\n* `os`: for interacting with the operating system (e.g., `os.path.isdir()`)\n* `filter()`: a built-in Python function for filtering elements from iterable collections based on condition.\n\n**Primary Purpose and Functionality:**\nThe main purpose of this code is to retrieve available artifacts stored in directories, which can be utilized for subsequent operations or processing. \n\n**Additional Informed Insights:**\n- The code iterates over all files and folders in the system using `os.listdir()`.\n- It populates an internal data structure `_available_artifacts` dictionary, containing only directories as keys.\n- It assumes that the directory names match the artifact names within these instances, allowing for consistent categorization."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_doc_toc.py:43-76
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 76, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_doc_toc.py", "name": "clean_model_doc_toc", "start_line": 43, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `clean_model_doc_toc` function cleans the table of content in model documentation by removing duplicates and sorting models alphabetically.\n\n**Main Purpose and Functionality**\n\n* Cleans a list of dictionaries (`model_doc`) extracted from `_toctree.yml` by:\n\t+ Counting occurrences of each local key\n\t+ Identifying duplicate keys with multiple titles\n\t+ Removing all but one version of each duplicate title\n* Returns the cleaned list sorted alphabetically by title.\n\n**Key Implementation Details**\n\n* Uses `defaultdict` to count occurrences of each local key.\n* Utilizes a lambda function for sorting by title (case-insensitive).\n* Raises a ValueError if multiple versions of a title are found for a duplicate key.\n\n**Notable Dependencies or Requirements**\n\n* Uses Python's built-in `List`, `dict`, and `defaultdict` types.\n* Assumes the presence of an `_toctree.yml` file with documentation data."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_doc_toc.py:79-125
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_doc_toc.py", "name": "check_model_doc", "start_line": 79, "type": "function", "chroma:document": "**Summary: check_model_doc function**\n\nThe `check_model_doc` function validates and potentially auto-cleans the table of contents (`_toctree.yml`) for the model API doc. It checks for:\n\n* Duplicates: A duplicate title is found, indicating an invalid TOC structure.\n* Sorting: The TOC contains unsorted sections.\n\n**Key Implementation Details**\n\n1. Reads `PATH_TO_TOC` file using `yaml.safe_load()`.\n2. Iterates to find the \"API\" and \"Models\" section indices in the content tree.\n3. Extracts modalities documents from the \"Models\" section.\n4. Cleans each modality document using `clean_model_doc_toc()`.\n\n**Notable Dependencies/Requirements**\n\n* `yaml`: for parsing the `_toctree.yml` file.\n* `PATH_TO_TOC`: a constant representing the file path to `._toctree.yml`.\n\n**Interfaces and Method Signatures**\n\n1. `yaml.safe_load()`: loads YAML data from a string.\n2. `clean_model_doc_toc()`: cleans a model doc using unknown implementation details.\n\nThis function appears to be part of a larger project for generating and maintaining documentation, possibly in a MarkDown or Sphinx-based environment."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py:25-62
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 62, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py", "name": "create_script", "start_line": 25, "type": "function", "chroma:document": "**Summary:**\n\nThe `create_script` function generates Python script to be run by `git bisect run` for verifying the outcome of test `target_test`. The script imports required modules, runs the specified test using `pytest`, and outputs the result.\n\n**Key Implementation Details:**\n\n* Runs `python3 -m pytest -v <test_name>` with specified `target_test`\n* Checks if error or failure message is present in standard output/error\n* Exits with success (0) or failure (-1/2) depending on validation\n\n**Notable Dependencies/Requirements:**\n\n* `os` and `subprocess` modules for executing external commands\n* `pytest` module for running tests"}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py:65-125
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py", "name": "find_bad_commit", "start_line": 65, "type": "function", "chroma:document": "**Function Summary: find_bad_commit**\n\n**Purpose:** Find the earliest commit between two given commits at which a specified test fails.\n\n### Functionality:\n\n1.  **Initial Check**: Verifies if `start_commit` and `end_commit` are equal, returning `start_commit` in this case.\n2.  Creates a bash script to reset git bisect mode and start it with the provided `target_test`.\n3.  Runs the `run_git_bisect.sh` script using `subprocess.run`, capturing its output and error messages.\n4.  Checks for errors, specifically \"bisect run failed\". If found, extracts the error message and continues.\n5.  Seeks the first occurrence of \"first bad commit\" in the standard output to identify the earliest failing commit.\n\n### Key Implementation Details:\n\n*   `git bisect` mode is managed using bash scripts and subprocesses.\n*   Standard output and error messages are analyzed for errors or test failures.\n*   Regular expressions (`re`) are used to extract specific information from the output.\n\n**Key Method Signatures:**\n\n*   `create_script(target_test)`\n*   `subprocess.run([\"bash\", \"run_git_bisect.sh\"])`\n\n**Dependencies and Interfaces:**\n\n*   External dependencies:\n    *   `git` command\n    *   `python3` for running script\n    *   `re` (regular expression library) for pattern matching\n\n**Interfaces:**\n\n| Interface | Description                |\n| :-------- | -------------------------- |\n| `target_test` | Target test to check          |\n\n### Notable Variability:\n\n*   Initial commit check logic.\n*   Bash script and subprocess execution.\n\nThis analysis highlights the primary purpose and key implementation details of the provided function, facilitating a deeper understanding of its functionality and potential areas for optimization."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py:128-150
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 150, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_bad_commit.py", "name": "get_commit_info", "start_line": 128, "type": "function", "chroma:document": "**Summary**\n\nThe `get_commit_info` function retrieves information about a GitHub commit, including the pull request number, author, and merged author. It makes API requests to `api.github.com` to fetch data.\n\n* Purpose: Fetch commit metadata via GitHub API.\n* Implementation Details:\n\t+ Retrieves PR info for a given commit and extracts the PR number.\n\t+ Fetches details about the associated PR, including author and merged author, if available.\n\t+ Fallbacks to fetching original commit information using `commit` URL if no PR author is found.\n\t+ Returns a dictionary with commit-related data.\n\n**Key Method Signsatures/Interfaces**\n\n* `requests.get()`: Used for API requests (`url = f...\"`)\n\n**Notable Dependencies**\n\n* `requests`\n* `json` ( implicit, handling JSON responses from GitHub API)\n* `user`, `login` (accessed through PR and commit responses)\n\n**Interface Inheritance/Implementation**\n\nNone.\n\nThis function leverages the GitHub API to collect metadata. The implementation caters to both cases when a PR is associated with the commit and when it's not."}


created_at: 2025-02-01 08:47:49
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_tf_ops.py:43-82
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 82, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_tf_ops.py", "name": "onnx_compliancy", "start_line": 43, "type": "function", "chroma:document": "**Function Summary: `onnx_compliancy`**\n\n### Purpose and Functionality\n\nThe `onnx_compliancy` function checks if a saved model (in ONNX format) is compliant with the specified ONNX opset version. It parses the saved model, extracts its operations, and verifies that they are compatible with the given opset.\n\n**Key Implementation Details**\n\n* Extracts ONNX opsets from `onnx.json` file\n* Parses saved model from file using `SavedModel.ParseFromString`\n* Iterates over multiple graphs in saved model (if present)\n* Compares extracted operation names from saved model to list of compatible operations (`onnx_ops`) and internal operations (`INTERNAL_OPS`)\n* Raises exception if strict mode is enabled and incompatible ops are found\n\n**Notable Dependencies and Requirements**\n\n* `SavedModel` class\n* `json` module for parsing `onnx.json` file\n* `os` module for getting repository path (`REPO_PATH`)\n* External dependencies:\n\t+ `onnx_ops` (list of compatible ONNX opsets)\n\t+ `INTERNAL_OPS` (list of internal operations)"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_quantization.py:39-166
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 166, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/notification_service_quantization.py", "name": "QuantizationMessage", "start_line": 39, "type": "class", "chroma:document": "**Summary: QuantizationMessage Class**\n\nThis class, `QuantizationMessage`, inherits from the `Message` class and represents a message containing quantization testing results.\n\n**Main Purpose and Functionality**\nThe main purpose of this class is to store and format the results of quantization tests in a JSON payload that can be sent through Slack.\n\n**Implementation Details**\n\n*   **Initialization**: The constructor takes two parameters: `title` (a string) and `results` (a dictionary).\n    *   `results`: A dictionary containing test results, including failure information.\n        *   `n_success`\n        *   `single_gpu_failures`\n        *   `multi_gpu_failures`\n        *   `n_failures`\n        *   `n_tests`\n        *   `thread_ts` (initially None)\n*   **Properties**:\n    *   `payload`: Returns the formatted JSON payload as a string, including optional sections for failure overview and detailed reports.\n        *   `blocks`: A list of JSON blocks containing the message content.\n        *   Includes failuer section\n    *   `time`: Calculates and returns the total time spent on all tests in hours, minutes, and seconds.\n*   **Methods**:\n    *   `post()`: Sends the message payload to a Slack channel using the client's chat post message method.\n    *   `post_reply()`: Posts replies for individual jobs with failures."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:40-62
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 62, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "get_new_python_files_between_commits", "start_line": 40, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_new_python_files_between_commits`\n\nReturns a list of added Python files between a base commit (`base_commit`) and one or several commits (`commits`). Compares each commit's diff against the base commit to identify new Python files.\n\n**Key Implementation Details:**\n\n* Uses `git.Repo.diff()` to compare each commit's diff against the base commit.\n* Iterates through a list of commits (`commits`) using the `diff()` method.\n* Identifies new Python files added in each commit by checking for file creation type (\"A\") and file extension (`.py`).\n* Returns a list of paths to newly created Python files.\n\n**Notable Dependencies/Requirements:**\n\n* `git.Repo`: Git repository access\n* `List[str]`: List of commit references\n\n**Primary Purpose:**\nIdentify new Python files added between a base commit and one or several commits in a Git repository."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:65-87
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 87, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "get_new_python_files", "start_line": 65, "type": "function", "chroma:document": "**Function Summary**\n\n`get_new_python_files()` returns a list of Python files added between the current Git head and the `main` branch.\n\n**Key Implementation Details**\n\n* Uses `Repo` class to interact with the GitHub repository\n* Checks if `main` branch exists locally, falling back to remote `origin` in GitHub Actions runners\n* Retrieves merge base commits using `repo.merge_base`\n* Returns list of new Python files added using `get_new_python_files_between_commits`\n\n**Notable Dependencies and Requirements**\n\n* `Repo` class from `pathlib` library ( implied but not explicitly stated)\n* External dependency on GitHub repository at `PATH_TO_REPO`"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:90-101
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 101, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "get_new_model", "start_line": 90, "type": "function", "chroma:document": "**Function Summary: `get_new_model`**\n\nRetrieves the names of newly created Python model files in a Git repository.\n\n**Primary Functionality:**\nParses file contents to extract and return the name of the new model file, assuming only one new modeling file exists in each pull request.\n\n**Key Implementation Details:**\n\n* Uses regular expression `reg` to extract pattern \"src/transformers/models/(.*)/modeling_.*\\.py\"\n* Iterates over list of new Python files (`new_files`) and uses `re.findall` to find the matching model file name\n* Returns the extracted model file name as a string\n\n**Notable Dependencies or Requirements:**\n\n* `re` (regular expression) module for pattern matching\n* `get_new_python_files()` external function or call, returning list of new Python files"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:104-128
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 128, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "parse_message", "start_line": 104, "type": "function", "chroma:document": "**Summary**\n\n### Functionality\n\nThe `parse_message` function parses a GitHub pull request comment to extract models specified in the message to run slow CI.\n\n### Implementation Details\n\n* **Key Algorithm:** Trims and normalizes user input message.\n* **Method Signature:**\n\t+ `parse_message(message: str) -> str`\n* **Design Choice:** Uses multiple prefixes (\"run-slow\", \"run_slow\", \"run slow\") to account for varying notation in comments.\n\n### Dependencies and Requirements\n\n* None explicitly listed, but assumes `str` type for input message."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:131-133
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 133, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "get_models", "start_line": 131, "type": "function", "chroma:document": "**Summary**\n\n### Function Overview\n\n`get_models(message: str)`\n\nExtracts and preprocesses a list of models from a given message.\n\n### Primary Purpose\n\nResets and splits the `models` string to separate individual model names, replacing commas with spaces.\n\n### Key Implementation Details\n\n* Uses `parse_message(message)` (exact class/function not provided)\n* Applies whitespace manipulation to output string\n* Utilizes string splitting (`split()`) on `models`\n\n### Notable Dependencies or Requirements\n\n* `parse_message(message)` (related function not provided)\n \n### External Dependencies\n\nList of external dependencies is not specified in the given code chunk"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py:136-138
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 138, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/pr_slow_ci_models.py", "name": "check_model_names", "start_line": 136, "type": "function", "chroma:document": "**Summary**\n\nChecks if a `model_name` is valid. A valid model name consists of letters, digits, and underscores only.\n\n* **Primary Purpose**: Validate model names to ensure they comply with the specified format.\n* **Key Implementation Details**:\n\t+ Uses regular expression for string validation (`string.ascii_letters + string.digits + \"_\"`).\n\t+ Checks for leading/trailing underscore prefixes using `startswith()` and `endswith()`.\n* **Notable Dependencies**:\n\t+ `string` module for character classification.\n* **Interfaces/Inherited Class**: None.\n\nIn RAG system notation: \n### model_name_validation\n- Description: Validates a model name to ensure it contains only letters, digits, or underscores.\n- Primary Purpose: Ensure model names meet the specified format requirement."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_modular_conversion.py:19-51
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 51, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_modular_conversion.py", "name": "process_file", "start_line": 19, "type": "function", "chroma:document": "**Function Summary**\n\n`process_file(modular_file_path, generated_modeling_content, file_type=\"modeling_\", fix_and_overwrite=False)`\n\n**Purpose and Functionality**\nProcess and compare the content of two modeling files located in `modular_file_path`.\n\n**Main Algorithms/P Patterns/Design Choices**\n\n* Reading and comparing file contents using `difflib.unified_diff` to identify differences.\n* Overwriting or printing differences based on `fix_and_overwrite` flag.\n\n**Key Method Signatures and Interfaces**\n\n- `difflib.unified_diff(fromfile, tofile, lineterm=\"\")`\n- `console.print()`\n\n**Notable Dependencies and Requirements**\n\n- `StringIO`\n- `difflib`\n- `console` (assuming a custom console object or module)\n- `generated_modeling_content[file_type]` referencing an external data structure."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_modular_conversion.py:54-60
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 60, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_modular_conversion.py", "name": "compare_files", "start_line": 54, "type": "function", "chroma:document": "**Summary:**\n\n`compare_files` function compares the expected modeling content with actual files specified by `modular_file_path`. It iterates through each file type, processing them using `process_file`, and returns the cumulative difference (`diff`). \n\n**Key Implementation Details:**\n\n* Generates expected modeling content using `convert_modular_file(modular_file_path)`\n\t+ Input parameter: `modular_file_path`\n\t+ Output parameter: `generated_modeling_content` dictionary containing different file types\n* Iterates through each file type in `generated_modeling_content` and processes them using `process_file(modular_file_path, generated_modeling_content, file_type, fix_and_overwrite)`\n\t+ Input parameters: `modular_file_path`, `generated_modeling_content`, `file_type`\n\t+ Output parameter: cumulative difference (`diff`)\n* Returns the cumulative difference (`diff`) after processing all files\n\n**Notable Dependencies/Requirements:**\n\n* `convert_modular_file` function\n* `process_file` function with parameters `modular_file_path`, `generated_modeling_content`, `file_type`, and `fix_and_overwrite`\n \nThis summary aims to preserve the original method's flow, focus on key implementation details, and reference related functions by exact name."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:72-88
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 88, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "find_backend", "start_line": 72, "type": "function", "chroma:document": "**Summary**\n\n`find_backend` function identifies and extracts multiple backend names (`xxx_and_yyy`) from a given code line of the main `init` file. It performs a case-insensitive search using regular expression `_re_test_backend.search(line)`.\n\n**Implementation Details**\n\n* Uses two regular expressions: `_re_test_backend` to search for the backend pattern and `_re_backend` to extract the backend names.\n* Extracts backend names from the line using a list comprehension (`[b[0] for b in _re_backend.findall(line)]`) and sorts them alphabetically.\n\n**Return Value**\n\nReturns all extracted backend names joined on `_and_`, or `None` if no backend is found (`if _re_test_backend.search(line) is None`)."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:91-231
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 231, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "parse_init", "start_line": 91, "type": "function", "chroma:document": "**Function Summary: `parse_init`**\n\nParsing an init file to extract import and TYPE_CHECKING definitions.\n\n**Purpose:** Read an init file, identifying and parsing the `_import_structure` and `TYPE_CHECKING` objects defined in it.\n\n**Primary Purpose:** The function reads a `.init` file and returns two dictionaries mapping backends to lists of imported objects: one for the `_import_structure` section and one for the `TYPE_CHECKING` section.\n\n**Key Implementation Details:**\n\n*   Checks for `_import_structure = { ... }` definitions to parse backend-specific imports.\n*   Iterates through lines to identify and extract:\n    *   Import structures without a specific backend (`_re_one_line_import_struct.search`)\n    *   Backend-specific imports within the try-except-else block (`_re_backend_find`)\n    *   TYPE_CHECKING objects without a specific backend (`_re_import.search`)\n*   Parses import definitions using regular expressions (e.g., `_import_struct_add_many`, `_re_between_brackets`)\n\n**Notable Dependencies and Requirements:**\n\n*   The function uses regular expression classes (`_re_one_line_import_struct`, `_re_backend_find`, etc.) to parse the init file.\n*   It relies on `find_backend()` function, but its implementation is not provided in this code snippet.\n\n**Return Value:** A tuple of two dictionaries:\n    *   `import_dict_objects`: Mapping backends to lists of import structures without a specific backend (`\"none\"`).\n    *   `type_hint_objects`: Mapping backends (if applicable) to lists of TYPE_CHECKING objects without a specific backend (`\"none\"`)."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:234-278
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 278, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "analyze_results", "start_line": 234, "type": "function", "chroma:document": "**Function Summary**\n\n`analyze_results` analyzes the discrepancies between `_import_structure` objects and `TYPE_CHECKING` objects found in an init.\n\n**Main Purpose**\nIdentifies duplicate imports, missing imports, and incorrect backend names across both structures.\n\n**Key Implementation Details**\n\n* Verifies that both input dictionaries have identical keys (backends).\n* Uses a custom function `find_duplicates` to detect duplicate values in lists.\n* Iterates through each key-value pair, comparing the two dictionaries for:\n\n    * Duplicate imports\n    * Missing imports\n\n**Notable Dependencies/Requirements**\n\n* Python built-in libraries: `collections`\n* External dependencies:\n\t+ None (self-contained)\n* Implemented interfaces:\n\t+ No explicit interface implementation; relies on dictionary comparisons."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:281-297
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 297, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "check_all_inits", "start_line": 281, "type": "function", "chroma:document": "**Function Summary:**\n\n`check_all_inits()`\n\nChecks all initalization files (`__init__.py`) in the transformers repository for potential object definition discrepancies between both halves. Raises an error if any file fails to define the same objects.\n\n**Key Implementation Details:**\n\n* Iterates through `__init__.py` files in the transformers repository using `os.walk`.\n* Parses initialization file contents using `parse_init(fname)`.\n* Analyzes results using `analyze_results(*objects)` and accumulates errors.\n* Raises a `ValueError` with error messages if any discrepancies are found.\n\n**Notable Dependencies:**\n\n* `os`, `PATH_TO_TRANSFORMERS`\n* `parse_init`, `analyze_results` (external functions not shown in this code snippet)"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:300-324
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 324, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "get_transformers_submodules", "start_line": 300, "type": "function", "chroma:document": "**Summary**\n\n`get_transformers_submodules()` function returns a list of Transformers submodules by traversing the directory tree rooted at `PATH_TO_TRANSFORMERS`. It filters out private modules, empty folders, and initializes that are not submodule names.\n\n**Key Implementation Details**\n\n* Uses `os.walk()` to traverse directory tree\n* Checks for file patterns in folders (`*.py`, `__init__.py`) and files (excluding `__init__.py`)\n* Normalizes folder and file paths to standardize submodule names\n\n**Notable Dependencies or Requirements**\n\n* `PATH_TO_TRANSFORMERS`\n* `os` module for walking directories\n* `Path` class from `pathlib` library for path manipulation"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py:339-368
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 368, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/check_inits.py", "name": "check_submodules", "start_line": 339, "type": "function", "chroma:document": "**Summary: `check_submodules()` Function**\n\n* **Purpose:** Validate that all submodules in `Transformers` are properly initialized with their respective import structures.\n* **Key Implementation Details:**\n\t+ Verifies `transformers._import_structure` keys against the expected structure and user-defined imports from `_init.py`.\n\t+ Compares submodule lists against imported `import_structure_keys`.\n\t+ Raises a `ValueError` if any submodules are not properly registered.\n* **Notable Dependencies & Requirements:**\n\t+ `direct_transformers_import`: imports transformers module ensuring it's the one in the repo.\n\t+ `get_transformers_submodules`: recursively finds all submodules of Transformers.\n\t+ `_init.py`: provides user-defined import structure additions for optional dependencies."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py:7-28
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 28, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py", "name": "get_daily_ci_runs", "start_line": 7, "type": "function", "chroma:document": "**Function Summary: `get_daily_ci_runs`**\n\n**Purpose and Functionality:** Retrieves the workflow run history of scheduled CI for the GitHub repository \"huggingface/transformers\" on the `main` branch filtered by schedule events.\n\n**Key Implementation Details:**\n\n* Employs the API endpoint `https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs` with query parameters:\n\t+ Branch: `main`\n\t+ Event: `schedule`\n\t+ Exclude pull requests: `true`\n\t+ Page size: `num_runs`\n* Utilizes the `requests.get()` method to send a GET request to the API endpoint, authenticated via Bearer token.\n\n**Notable Dependencies and Requirements:**\n\n* `token`: GitHub API authentication token\n* `requests` library for API calls\n* External dependencies:\n\t+ github.com ( GitHub repository)\n\t+ transformrsactions API"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py:31-40
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 40, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py", "name": "get_last_daily_ci_runs", "start_line": 31, "type": "function", "chroma:document": "**Summary:**\n\n```markdown\n### Function:\n#### `get_last_daily_ci_runs(token)`\n\n#### Purpose:\n Retrieves the ID of the most recent completed daily Continuous Integration (CI) workflow.\n\n#### Implementation Details:\n\n* Calls `get_daily_ci_runs(token)` to retrieve daily CI runs\n* Iterates through runs, returning the ID of the first complete run (`if \"status\" in workflow_run:`)\n* Returns the most recent completed daily CI run's ID\n\n#### Notable Dependencies:\nNone\n\n#### Interfaces:\n- `get_daily_ci_runs(token)`\n```\n\n**RAG Code Tag:**\n`func: get_last_daily_ci_runs(token)`:"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py:43-52
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 52, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py", "name": "get_last_daily_ci_run_commit", "start_line": 43, "type": "function", "chroma:document": "**Summary**\n\nThe `get_last_daily_ci_run_commit` function retrieves the commit SHA of the last completed scheduled daily CI workflow run.\n\n**Key Points:**\n\n* **Purpose:** Returns the SHA of the latest completed CI run.\n* **Functionality:** Iterates through completed daily CI runs to find the head SHA associated with the completion.\n* **Implementation Details:**\n\t+ Calls `get_daily_ci_runs` function to retrieve successful workflow runs.\n\t+ Loops through runs, identifying completed ones and saving their corresponding head SHA.\n\t+ Returns the head SHA of the last completed run.\n\n**Notable Dependencies/Requirements:**\n\n* `get_daily_ci_runs(token)` - Function called to retrieve daily CI runs."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py:55-65
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 65, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py", "name": "get_last_daily_ci_artifacts", "start_line": 55, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_last_daily_ci_artifacts`\n\n Retrieves and downloads artifacts from the last completed daily CI workflow run.\n\n**Key Implementation Details:**\n\n* Fetches `workflow_run_id` using `get_last_daily_ci_runs`.\n* Retrieves artifact links for the specified `workflow_run_id` using `get_artifacts_links`.\n* Iterates over `artifact_names`, downloading artifacts matching those names using `download_artifact`.\n\n**Notable Dependencies or Requirements:**\n\n* Utilizes `token` in multiple calls.\n* Calls `get_artifacts_links` with `workflow_run_id` and `token`.\n* Expects implementation of `download_artifact` function separate from this snippet.\n\nThis summary prescribes the main purpose, key algorithms/patterns, and dependencies used within `get_last_daily_ci_artifacts`."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py:68-84
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 84, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_previous_daily_ci.py", "name": "get_last_daily_ci_reports", "start_line": 68, "type": "function", "chroma:document": "**Summary**\n\nThe `get_last_daily_ci_reports` function retrieves the artifacts' content from the last completed daily CI workflow run. It takes three parameters: `artifact_names`, `output_dir`, and `token`.\n\n**Purpose and Functionality**\n\nRetrieves artifacts from the last daily CI workflow run, saving their contents in a dictionary for each artifact.\n\n**Key Implementation Details**\n\n* Iterates over `.zip` files with artifact names matching in the provided directories.\n* Reads and decodes file contents using UTF-8 encoding.\n* Returns a nested dictionary containing artifact names as keys and their corresponding file contents as values.\n\n**Notable Dependencies or Requirements**\n\n* `os.path.isfile`\n* `os.open`\n* `zipped zipfile.ZipFile`\n\nNote: This summary assumes the absence of any external dependencies not mentioned in the provided code. If such dependencies exist, please provide them for a more comprehensive analysis."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_github_job_time.py:8-25
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 25, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_github_job_time.py", "name": "extract_time_from_single_job", "start_line": 8, "type": "function", "chroma:document": "**Summary**\n\nThis function, `extract_time_from_single_job`, extracts time-related information from a single job in a GitHub Actions workflow run. It:\n\n* Retrieves the start and end timestamps of the job\n* Calculates the duration in minutes between these two timestamps\n* Stores this duration along with the original timestamps in a dictionary, `job_info`\n* Returns the populated `job_info` dictionary\n\n**Key Implementation Details**\n\n* Retrieves dates using date parsing (`date_parser.parse`)\n* Calculates duration in minutes from the timestamp difference ( converted to seconds)\n\n**Notable Dependencies or Requirements**\n\n* `date_parser`: assumed dependency for parsing timestamps into datetimes\n* Expected input: `job` dictionary with keys \"started_at\" and \"completed_at\"\n* Returns a dictionary (`job_info`) with keys \"started_at\", \"completed_at\", and \"duration\"."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/get_github_job_time.py:28-51
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 51, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/get_github_job_time.py", "name": "get_job_time", "start_line": 28, "type": "function", "chroma:document": "**Function Summary**\n\n`get_job_time`: Extracts time information for all jobs in a GitHub Actions workflow run.\n\n**Primary Purpose:** Retrieves and extracts time info from multiple GitHub Actions workflow jobs.\n\n**Implementation Details:**\n\n- Uses `requests` to fetch GitHub API data\n- Iterates through 2 pages of jobs (due to pagination) to collect time info\n- Utilizes `extract_time_from_single_job` function (not shown in this snippet)\n- Handles exceptions and prints error messages if an issue occurs\n\n**Design Choices:**\n\n* Uses Bearer token authentication for API requests\n* Sets a page size of 100 jobs per request (per GitHub's rate limiting guidelines)\n\n**Notable Dependencies:**\n\n* `requests`\n* `math` (for calculating total pages to iterate over)\n* `extract_time_from_single_job` (not shown in this snippet, assumed to be implemented elsewhere)"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:28-41
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 41, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "get_last_stable_minor_release", "start_line": 28, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_last_stable_minor_release` function retrieves the last stable minor release of the `transformers` library by querying the PyPI JSON API. It compares the current major version to determine the last stable major and minor version, then filters releases to find those with versions below this threshold.\n\n**Key Implementation Details:**\n\n* Fetches `transformers` Pypi JSON data using `requests`\n* Compares current version to find last stable minor release\n* Uses `version.parse()` to sort and compare release versions\n\n**Notable Dependencies/Requirements:**\n\n* `requests` library for HTTP client\n* `pyperpapi` library (`version.parse()`) for parsing release versions"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:44-55
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 55, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "build_tip_message", "start_line": 44, "type": "function", "chroma:document": "**Function Summary: build_tip_message**\n\n* **Purpose**: Generates a maintenance mode warning message for a machine learning model, indicating that new PRs changing the code are not accepted.\n\t+ Primary purpose: Warn users about model maintenance mode and provide instructions to reinstall previous version.\n\n**Key Implementation Details:**\n- Uses f-string formatting to insert `last_stable_release` into the message.\n- Includes a Git installation command for reusing the last stable version supported by the model (Transformer vX.X).\n\n**Notable Dependencies or Requirements:** None mentioned explicitly."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:58-76
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 76, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "insert_tip_to_model_doc", "start_line": 58, "type": "function", "chroma:document": "**Summary:**\n\n`insert_tip_to_model_doc` function appends a tip message below the title section of an existing Markdown document file.\n\n**Key Details:**\n\n* Reads an input Markdown document file (`model_doc_path`) and tip message (`tip_message`)\n* Splits the document into lines, separating those with title markers (`# `) from others\n* Inserts the tip message lines below the original title lines, preserving blank line separation\n\n**Implementation Notes:**\n\n* Uses `file read/write` operations to modify an external file resource.\n\n**External Dependencies: None specified. No known inherited or implemented interfaces are assumed to be used in this code snippet.**"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:79-89
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 89, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "get_model_doc_path", "start_line": 79, "type": "function", "chroma:document": "**Summary:**\n\nThis function retrieves the path to an English model documentation file based on the provided `model` name. It checks for three possible variant names and searches for a non-empty documentation directory within the repository.\n\n**Implementation Details:**\n\n* The function returns two optional strings, (`model_doc_path`, `model_name`) if a matching documentation file exists.\n* The search considers three variant names:\n\t1. Original model name\n\t2. Model name with hyphens instead of underscores\n\t3. Model name without hyphens\n* It utilizes the `os` module for path existence checks.\n\n**Notable Dependencies:**\n\n* `REPO_PATH`: assumed to be a constants or an external variable referencing the repository's root directory.\n* `os.path.exists()`\n* `zip()` function"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:92-108
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 108, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "extract_model_info", "start_line": 92, "type": "function", "chroma:document": "**Function Summary: extract_model_info**\n\n**Purpose and Functionality:**\nThis function retrieves information about a given machine learning model, including its documentation path and file location.\n\n**Key Implementation Details:**\n\n* Retrieves `model` using the `get_model_doc_path` function to get its documentation path.\n* Returns a dictionary containing the following keys:\n\t+ `model_doc_path`: Path to the model's documentation.\n\t+ `model_doc_name`: Name of the model's documentation.\n\n**Notable Dependencies and Requirements:**\n\n* `REPO_PATH`: A constant representing the repository path.\n* `get_model_doc_path` function.\n* External dependencies: This function relies on the existence of the `get_model_doc_path` function, which itself may depend on other external libraries."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:111-123
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 123, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "update_relative_imports", "start_line": 111, "type": "function", "chroma:document": "**Summary**\n\n`update_relative_imports()` function updates relative import paths in a Python file. It replaces `..` with `...` to update absolute imports on all lines. The updated file is written back to the original location.\n\n**Key Implementation Details**\n\n* Replaces `from ..` with `from ...` across all lines to convert relative imports to absolute ones.\n* Uses `split(\"\\n\")` to split the file into individual lines for processing.\n\n**Notable Dependencies/Requirements**\n\n* None explicit dependencies. However, it's assumed that this function runs in a Python environment with necessary modules and libraries available."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:126-142
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 142, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "remove_copied_from_statements", "start_line": 126, "type": "function", "chroma:document": "**Summary**\n\nThis function, `remove_copied_from_statements`, removes commented lines starting with \"# Copied from\" from the specified model files. The main purpose is to clean upTransformer models.\n\n**Key Implementation Details:**\n\n* Iterates over lines in each file in the model directory, skipping lines containing \"# Copied from\".\n* Uses `os` and `re` modules implicitly for listing directory contents and file reading/writing.\n* Writes modified line-by-line to new file, omitting lines with the specified comment.\n\n**Notable Dependencies/Requirements:**\n\n* `REPO_PATH`: reference path (relative to current working directory) containing model files.\n* `os`: file system operations.\n* `re`: regular expression matching used implicitly for pattern searching."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:145-158
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 158, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "move_model_files_to_deprecated", "start_line": 145, "type": "function", "chroma:document": "**Summary: Migrate Model Files to Deprecation**\n\n### Purpose and Functionality\n\nMigrates model files from `src/transformers/models/{model}` to `src/transformers/models/deprecated/{model}`.\n\n### Key Implementation Details\n\n* Uses `repo.git.mv` to rename files, updating them at the new location.\n* Calls `update_relative_imports` to update relative imports for deprecated files.\n\n### Notable Dependencies or Requirements\n\n* `REPO_PATH`: Base path of the repository\n* `os.path.exists`, `os.makedirs`, `repo.git.mv`\n* `update_relative_imports`\n\nNote: The exact implementation details may vary based on the `update_relative_imports` function, which is not shown in this snippet."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:161-165
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 165, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "delete_model_tests", "start_line": 161, "type": "function", "chroma:document": "**Function Summary: `delete_model_tests`**\n\n Deletes model test files from the repository, removing related Git tracking.\n\n**Main Purpose and Functionality:**\nThe primary purpose is to delete model test files at the specified path if they exist. It utilizes the Git command-line interface (`repo.git.rm`) to remove these files and their associated Git tracking.\n\n**Key Implementation Details:**\n\n* Uses `os.path.exists` to check for file existence.\n* Utilizes `REPO_PATH / f\"tests/models/{model}\"` as the test path, referencing the `REPO_PATH` constant.\n* Executes `repo.git.rm(\"-r\", tests_path)` to remove the files and their tracking using Git.\n\n**Notable Dependencies or Requirements:**\n\n* References `repo`, an existing Git repository object.\n* Utilizes external tool, `os`.\n* `REPO_PATH`, a constant referencing the repository path."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:168-169
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 169, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "get_line_indent", "start_line": 168, "type": "function", "chroma:document": "**Summary:**\n\nCalculates the line indentation by subtracting the length of the stripped leading spaces from the total string length.\n\n**Key Implementations:**\n\n* `lstrip()` method is used to remove trailing whitespace characters\n* Function takes a single argument `s` (string)\n\n**Notable Dependencies/Requirements:**\n\n* None mentioned\n\nThis function is used to calculate and return the indentation line in a given string. The implementation relies on the built-in `lstrip()` method, providing a concise and efficient solution for checking leading whitespace characters."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:172-192
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 192, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "update_main_init_file", "start_line": 172, "type": "function", "chroma:document": "**Function Summary: `update_main_init_file`**\n\n**Primary Purpose:** Replaces deprecated model names in `__init__.py` file with new, deprecated versions.\n\n**Key Implementation Details:**\n\n* Replaces instances of `model.model_name` with `model.deprecated.model_name` in the `__init__.py` file.\n* Also updates import statements to use the deprecated module name.\n\n**Notable Dependencies and Requirements:**\n\n* `REPO_PATH`: The absolute path to the repository's source directory (`\"src/transformers/__init__.py\"`).\n* Uses an external function `sort_imports_in_all_inits(check_only=False)` (not shown in the provided code snippet)."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:195-215
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 215, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "remove_model_references_from_file", "start_line": 195, "type": "function", "chroma:document": "**Summary:**\n\n`remove_model_references_from_file` function:\n\n* Purpose: Removes references to specified models from a given file by applying a filter condition.\n* Key Implementation Details:\n\t+ Reads the entire file into memory using `init_file = f.read()`.\n\t+ Iterates over each line, skipping those that match any model based on the `condition` function.\n\t+ Writes the new filtered file contents back to disk.\n* Notable Dependencies and Requirements:\n\t+ Uses Python's built-in `open()` and `read/write` functions for file input/output.\n\t+ Assumes a `REPO_PATH` constant or variable defining the repository root path.\n\t+ Accepts a `condition` function with signature `Callable[[str, str], bool]`.\n\n**RAG (Reusable Anonymous Code) System:**\n\n```markdown\n# remove_model_references_from_file\n### Purpose\nRemoves references to specified models from a given file.\n\n### Key Implementation Details\n\n* Reads entire file into memory.\n* Applies filter condition to line-model pairs.\n* Writes new filtered file contents back to disk.\n\n### Dependencies and Requirements\n\n* Python built-in `open` and `read/write`.\n* `REPO_PATH` constant or variable.\n* `condition` function signature: `Callable[[str, str], bool]`.\n```"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:218-265
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 265, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "remove_model_config_classes_from_config_check", "start_line": 218, "type": "function", "chroma:document": "**Function Summary:**\n\n`remove_model_config_classes_from_config_check`\n\nRemoves deprecated model configuration classes from the `check_config_attributes.py` file.\n\n**Purpose and Functionality:**\n\n* Reads the contents of the specified Python file (`utils/check_config_attributes.py`)\n* Iterates through each line, tracking indentation and special cases\n* Removes comments above deprecated model config classes and rewrites the content in a new file\n\n**Key Implementation Details:**\n\n* Uses regex-free text processing to identify lines with model config classes\n* Preserves indentation and formatting when rewriting the content\n* Handles edge cases such as nested brackets and escaped newline characters\n\n**Dependencies and Interfaces:**\n\n* None, but may utilize external `REPO_PATH` constant in a larger context.\n* Does not implement or inherit any specific interfaces.\n\n**Method Signatures:**\n\n* `remove_model_config_classes_from_config_check(model_config_classes)`: takes a list of string model config class names as input"}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:268-299
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 299, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "add_models_to_deprecated_models_in_config_auto", "start_line": 268, "type": "function", "chroma:document": "**Function Summary:**\n\n`add_models_to_deprecated_models_in_config_auto(models)`\n\nAdds the input `models` to the list of deprecated models in `configuration_auto.py`, updating its contents and sorting the list alphabetically.\n\n**Key Implementation Details:**\n\n- Reads `DEPRECATED_MODELS` from `configuration_auto.py`.\n- Appends new models to the list in alphabetical order.\n- Updates the file with the modified content.\n\n**Notable Dependencies/Requirements:**\n\n- `REPO_PATH`: reference to a repository-specific path ( exact class/function name required)\n- External dependency on:\n  - `os` module for file system operations\n  - `pathlib` module for handling file paths\n  - `shlex` module (not explicitly used but possibly implied by the `split()` and `strip()` functions)\n\n**Interface/Import Notes:**\n\n- No explicit import statements are provided, suggesting that necessary modules and classes will be imported within this code."}


created_at: 2025-02-01 08:47:51
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py:302-370
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 370, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/utils/deprecate_models.py", "name": "deprecate_models", "start_line": 302, "type": "function", "chroma:document": "**Function Summary: deprecate_models**\n\n### Purpose and Functionality:\n\nThe `deprecate_models` function is designed to deprecate specific models by updating their documentation, removing references, and refactoring their configurations. It iterates through a list of models, extracting information about each model from an `extract_model_info` function call. The function checks if the model has a configured class in the `CONFIG_MAPPING`, adds a tip message to the model's documentation page, removes redundant statements, moves the model files to the deprecated directory, deletes test files, and updates other files that reference the models.\n\n### Key Implementation Details:\n\n*   Iterates through a list of models using `models` as input.\n*   Calls `extract_model_info(model)` for each model to gather information.\n*   Checks if a configured class exists in `CONFIG_MAPPING` or by name within `CONFIG_MAPPING`.\n*   Updates the last stable minor release version using `get_last_stable_minor_release()`.\n*   Uses `build_tip_message(tip_message)`, `remove_model_config_classes_from_config_check(model_config_classes)`, and other functions to finalize model depreciation.\n*   Iterates over models, processing each one individually using `insert_tip_to_model_doc`, `remove_copied_from_statements`, `move_model_files_to_deprecated`, `delete_model_tests`, and updates files like `update_main_init_file` to point to the deprecated models.\n\n### Dependencies and Requirements:\n\n*   Uses `defaultdict` from Python's standard library for creating default dictionaries.\n*   References `CONFIG_MAPPING` (a dictionary of model configurations) at least once during execution, which must be defined elsewhere in the codebase.\n*   Calls functions with external dependencies like `remove_model_references_from_file`, `update_main_init_file`, and others that reference files or classes not specified within this function.\n\n### Notable Interfaces:\n\nThe function provides a few notable interfaces to other components of the system:\n    \n*   `CONFIG_MAPPING`: A dictionary mapping model names or documentation lines to their respective configuration classes.\n*   External dependencies such as `update_main_init_file`, `remove_model_references_from_file` (referencing specific files or directories), and `build_tip_message`."}


