# embedding_metadata

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**Summary**

The `check_missing_backends` function checks if all required backends (PyTorch, TensorFlow, and Flax) are installed. It:

* Compares availability of each backend using `is_`:tch_available`, `is_tf_available`, and `is_flax_available`
* If any backend is missing, it appends the name to `missing_backends` list
* Raises an exception in CI if all backends are not installed or a warning for users otherwise
* Provides instructions on how to install missing backends using pip

**Key Implementation Details**

* Checks availability of three backends using separate functions (`is_torch_available`, `is_tf_available`, and `is_flax_available`)
* Uses an exception to raise error in CI if all backends are not installed
* Uses a warning to notify users about the missing backends
* Provides a pip installation command to install missing backends

**Notable Dependencies/Requirements**

* `pytorch`: PyTorch backend
* `tensorflow`: TensorFlow backend
* `flax`: Flax backend
* `transformers`: Transformers repository
* `os`: Operating system dependency for environment variable access


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**Function Summary:** `check_model_list`

**Primary Purpose:** Validates that the models listed as subfolders of `src/transformers/models` match the available models in `transformers.models`.

**Key Implementation Details:**

*   Loads all non-deprecated subfolder models from `src/transformers/models/` into `_models`.
*   Compares the list of models in `_models` against the models listed in `transformers.models`.
*   Raising exception when there are missing models that should be included.

**Notable Dependencies:**

*   `os.path`: for path manipulation (e.g., joining paths).
*   `PATH_TO_TRANSFORMERS`: a defined constant referencing to the transformers directory.


key: docstring
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# If some modeling modules should be ignored for all checks, they should be added in the nested list
# _ignore_modules of this function.


key: chroma:document
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**Summary**

This 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.

**Key Implementation Details:**

*   ignores ignored module names (`_ignore_modules`) and magic dunder attributes in the directory listing.
*   uses `getattr()` to dynamically access individual model modules and their "modeling" submodules.
*   excludes deprecated model names detected as "__deprecated__".

**Notable Dependencies/Requirements:**

*   The `transformers` library, specifically its `models` module.

**Method Signatures Interfaces:**

*   `get_model_modules() -> List[str]`: Returns a list of model modules in the transformed format, excluding obsolete ones.


key: chroma:document
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Here's a concise and focused summary of the given function:

### Function Summary: `get_models`

**Purpose:** Extract objects from a module that are models.

**Primary Purpose:** Return a list of model classes as tuples (class name, actual class) based on their inheritance relation to predefined model classes.

**Key Implementation Details:**

* Iterates over attributes in the given module.
* Filters out attributes whose names contain keywords related to `PreTrainedModel` subclass (excluding them by default).
* Checks if each attribute is a type and inherits from defined model classes using multiple inheritance.
* Appends validated attributes to the result list.

**Notable Dependencies/Requirements:**

* `transformers`: Module containing definition of predefined model classes.
* `types`: Module for accessing built-in types like `ModuleType`.

### Return Value:

A list of tuples (class name, actual class) representing extracted model objects.


key: chroma:document
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Here is a concise summary of the function:

**Function Summary**

`is_building_block(model: str) -> bool`

Checks 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".

**Key Implementation Details**

* Uses string suffix matching (`str.endswith()`)
* Only checks for specific model prefixes (no regular expression)

**Dependencies and Interfaces Noted**

* No dependencies are mentioned in the function code.
* Inherited or implemented interfaces not applicable.

This summary is concise, accurate, and focused on providing essential information about the function's purpose and implementation details.


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**Summary**

Determines whether a given model should exclude itself from the main initialization.

**Functionality**

- Checks if the model is in the `PRIVATE Modes` list, returning `True` if it is.
- Falls back to calling `is_building_block` with the model as an argument to determine its behavior.

**Key Implementation Details**

- Method signature: `def is_a_private_model(model: str) -> bool`
- Calls `PRIVATE_MODELS` set to determine private models
- Calls `is_building_block` method on a given model

**Notable Dependencies or Requirements**

- Uses the `PRIVATE.Models` set, which is not defined in this snippet. 
  - References related class 'PrivateModels'
  
Note: No external dependencies are listed as this function only references internal sets and methods.


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**Function Summary**

`check_models_are_in_init()`
=====================

Checks if defined model libraries are deployed in the main application initialization.

**Primary Purpose**

Verifies that all models imported from external modules are included in the main `init.py` file of the library.

**Key Implementation Details**

* Iterates over model modules using `get_model_modules()` and collects models not present in `transformers` directory
* Filters out private models using `is_a_private_model()`
* Raises an exception if any models are missing from main init

**Notable Dependencies and Requirements**

* `transformers`: used to define the expected models directory
* `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.


key: docstring
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# If some test_modeling files should be ignored when checking models are all tested, they should be added in the
# nested list _ignore_files of this function.


key: chroma:document
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**Function Summary:**

`get_model_test_files()` 

 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`.

**Key Details:**

* Ignored files are specified in `_ignore_files`.
* Files with "test_modeling" in their filename, excluding those in `_ignore_files`, are considered.
* Files are joined using `os.path.join()` to construct absolute paths.

**Notable Dependencies:**

* `os`: for file system interactions
* `PATH_TO_TESTS`: script-defined constant for test root path


key: docstring
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# 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
# for the all_model_classes variable.


key: chroma:document
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**Function Summary**

`find_tested_models(test_file: str) -> List[str]`

Detects models tested in a given test file by parsing its content, specifically extracting `all_model_classes`.

**Key Functionality**

* Reads the content of a test file
* Uses regular expressions to extract `all_model_classes` patterns
* Parses pattern entries to extract model names

**Implementation Details**

* Method signatures: `re.findall` with two pattern strings for `all_model_classes`
* Algorithm: Combines multiple pattern findings into a single list using `.append`

**Notable Dependencies/Requirements**

* `os.path.join` and `open` for file operations
* `re` module for regular expression matching

**No notable inherited or implemented interfaces.**


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**Function Summary**

### `should_be_tested`

Determines whether a model with the given name should be tested.

**Purpose**: Checks if a model's existence in an IGNORE_NON_TESTED list or if it's a building block model to exclude testing.

**Key Implementation Details**:

* Returns `False` for models in `IGNORE NON_TESTED`.
* Excludes models with this method signature from being tested.
* References `is_building_block(model_name)` using the exact name of the function.
* Inherently uses an external list (`IGNORE_NON_TESTED`) without defining it within this code snippet.

**Notable Dependencies/Requirements**: 

* `IGNORE_NON_TESTED` external list or constant.


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**Summary:**

This 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.

**Key Details:**

* Defines and tests models from `defined_models` and `tested_models` respectively
* Returns error messages for models that are not tested but should be (`model_name`)
* Notifies user to add model to `all_model_classes` in test file or ignore with `IGNORE_NON_TESTED`
 
**Function Signature:**
```python
def check_models_are_tested(module: types.ModuleType, test_file: str) -> List[str]:
```

**Notable Dependencies/Requirements:**

* `get_models`: module for getting defined models
* `find_tested_models`: function for finding tested models in a file
* `TEST_FILES_WITH_NO_COMMON_TESTS`: dictionary of files with no common tests
* `utils/check_repo.py`: configuration file for repository checks
* `types.ModuleType`: type hint for a Python module


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**Summary**

*   Function: `check_all_models_are_tested()`
*   **Purpose:** Verifies if all models have corresponding test files and reports any mismatches.
*   Key implementation:
    *   Retrieves module metadata (`get_model_modules()`).
    *   Iterates over each module, matching it to its test file based on the module name prefix.
    *   Calls a separate function (`check_models_are_tested()`) for modules with test files and combines any returned errors.
*   **Notable dependencies:**
    *   `get_model_modules()` (module metadata retrieval)
    *   `get_model_test_files()` (test file retrieval)

**External interfaces/deependencies:**

    - Check Model Functions
   


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**Function Summary: `get_all_auto_configured_models`**

**Purpose:** Extracts and returns a list of model names from auto-configured models in at least one available framework (PyTorch, TensorFlow, or Flax).

**Implementation Details:**

* Iterates over specific attribute names (`MODEL_`, `TF_MODEL_`, and `FLAX_MODEL_`) and their corresponding mapping names within each auto-configured model.
* Uses set operations to combine unique model names from different frameworks.

**Notable Dependencies:**

* `transformers.models.auto.modeling_auto`
* `transformers.models.auto.modeling_tf_auto`
* `transformers.models.auto.modeling_flax_auto`
* `is_torch_available()`, `is_tf_available()`, and `is_flax_available()` (functions to check availability of respective frameworks)

**Return Value:** A list of strings representing model names from auto-configured models in at least one available framework.


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**Summary:**
This 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.

**Key Details:**

* **Functionality:** `ignore_unautoclassed(model_name) -> bool`
* **Purpose:** Determine if a model is auto-configured
* **Design Choice:** Whitelisting approach with specific exceptions for "Encoder" and "Decoder" models.
* **Dependencies/Requirements:**
	+ `IGNORE_NON_AUTO_CONFIGURED`: A predefined list of accepted non-auto-configured model names.

Note: 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.


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**Summary:**

### Function Purpose:

`check_models_are_auto_configured` checks if models defined in a module are part of an auto class.

### Key Implementation Details:

*   Calculates the list of defined models from the input `module`.
*   Iterates over each model, verifying if it exists in the `all_auto_models` list and ignoring unautoclassed models.
*   Appends error messages to the `failures` list for non-existent models.

### Notable Dependencies/Requirements:

*   `get_all_auto_configured_models()`: Returns a list of all models in an auto class.
*   `ignore_unautoclassed(model_name)`: Function indicating whether a model is intended to be ignored.
*   `IGNORE_NON_AUTO_CONFIGURED` constant: File location for modifying ignore behavior.


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**Summary:**

`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.

**Key Details:**

* Verifies existence of all required backends
* Iterates through modules to validate model configurations
* Checks for `None` return value from `check_models_are_auto_configured()`
* Accumulates failure messages and raises an exception

**Notable Dependencies/Requirements:**

* `get_model_modules()` function
* `get_all_auto_configured_models()` function
* `check_missing_backends()` function


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**Summary:**

The `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.

**Key Functionality:**

* Checks for missing classes derived from `TOKENIZER_MAPPING_NAMES`, `IMAGE_PROCESSOR_MAPPING_NAMES`, etc.
* Iterates through auto modeling files (`modeling_auto`, `modeling_tf_auto`, `modeling_flax_auto`) and extracts mapping names and their corresponding class definitions
* Reports missing classes in the following format: "<class_name> appears in <mapping-name> but it is not defined in the library."

**Notable Dependencies/Requirements:**

* `transformers.models.auto` module
* External dependencies:
	+ `transformers`
	+ `torch`
	+ `jittor`
	(+ other transformer-based libraries)
* Inherited Interfaces:
	+ `TOKENIZER_MAPPING_NAMES`, `IMAGE_PROCESSOR_MAPPING_NAMES`, etc.

**Implementation Details:**

* Uses dynamic imports and attribute access to extract mapping names and class definitions.
* Includes private model checks for certain mapping types.

**Primary Purpose:**
The primary purpose of this function is to identify missing classes derived from auto mapping mappings, raising an exception if any are found.


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**CONCISE SUMMARY**

### Function Purpose:

DefectsCheckAutoMappingNames checks if all keys defined in auto mapping names appear in CONFIG_MAPPING_NAMES.

### Key Implementation Details:

- Iterates through special mapping names and their corresponding values from `TOKENIZER_PROCESSOR_MAPPING_NAMES` and `AutoTokenizer`.
- Compares model types in auto modeling files with those in CONFIG_MAPPING_NAMES.
- Raises an exception when inconsistencies are found.

### Notable Dependencies/Requirements:

- `CONFIG_MAPPING_NAMES`
- `IMAGE_PROCESSOR_MAPPING_NAMES`
- `FEATURE_EXTRACTOR_MAPPING_NAMES`
- `PROCESSOR_MAPPING_NAMES`
- `transformers.models.auto` (dynamic import)


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**Summary**

`check_all_auto_mappings_importable` function checks whether all auto-mapping functions can be imported.

**Primary Purpose**: Verify if all auto-mapping files are properly installed and importable.

**Key Implementation Details**:

* Iterates through `modeling_auto`, `modeling_tf_auto`, and `modeling_flax_auto` modules to collect their mapping names.
* Checks for the existence of each mapped function in the `transformers` namespace using `hasattr`.

**Notable Detections**:

* Failure: `MAPPING_NAME` (e.g. `_MAPPING_NAMES`) not found in `transformers`.
* Raised exception if any failure is detected, listing all failed mappings.

**Dependencies and Requirements**:
- `transformers.models.auto`
- External dependencies: Not explicitly listed, but assumed to be part of the `transformers` package.
- Inherited/Implemeted interfaces: No notable references.


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**Summary:**

This 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.

**Key Details:**

- **Implementation:** This function uses attribute iteration to check for equivalent objects between TensorFlow/Flax and PyTorch.
- **Important Method Signatures:**
  - `is_tf_available()`: checks if the TF framework is available.
  - `is_flax_available()`: checks if the Flax framework is available.
  
**Notable Dependencies and Requirements:**

- **Related Classes/Fuctions:** 
  - `transformers` (the object to be checked)
  - `sys`, `os`
- **External Dependencies:**
  - TensorFlow
  - Flax
  - PyTorch
- **Implemented Interfaces:**
    - No specific interfaces are mentioned, but the function appears to follow a generic structure for comparing attributes between frameworks.
-


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**Summary: `check_decorator_order` Function**

* **Purpose**: Verifies that in a given test file, the slow decorator "parameterized" always follows other decorators.
* **Implementation Details**:
	+ Extracts lines from the specified test file and searches for occurrences of the "_re_decorator" regular expression.
	+ If the search finds "parameterized", adds the current line index to the error list if a preceding "parameterized" decorator was found.
	+ Resets the tracking of preceding decorators when a new one is encountered.
* **Notable Dependencies**: `_re_decorator` regular expression (internal), `List[int]` type hint for return value.
* **Assumptions/Requirements**: None external dependencies mentioned, no inherited or implemented interfaces listed.


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**Code Summary**

```markdown
### Functionality

Checks if the slow decorator is last in every test file.
Raises ValueError with error messages if not.

### Key Implementation Details

* Iterates over test files (ending with .py) and checks decorator order using `check_decorator_order` method.
* Compares actual order against expected order (slow decorator first).
* If mismatch, raises `ValueError` with list of error messages and corresponding file locations.

### Notable Dependencies

- [os] library for accessing file system
- [PATH_TO_TESTS] environment variable or constant for path to test files
```

Note: 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.


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**Summary**

This 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.

**Key Implementation Details**

* Uses regular expressions to extract object names and methods from Markdown content
* Iterates over Markdown files in the specified directory, extracting object objects with autodoc tags
* Uses dictionary comprehension to create a mapping between object names and their documented methods

**Primary Purpose**: Detects class and method documentation in Markdown files.

**Notable Dependencies**

* `re` for regular expression pattern matching
* `pathlib` for working with file paths


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**Summary**

This function, `ignore_undocumented`, determines whether a given string `name` should be included in the documentation based on specific rules. It checks for:

* Uppercase constants and pre-trained models
* Submodules (e.g., transformer.py)
* Load functions (e.g., load_tf())
* is_xxx_available functions
* Deprecated objects
* MMBT model

**Key Implementation Details**

* The function uses a combination of string matching and path checking using `os.path`.
* The `PATH_TO_TRANSFORMERS` constant is assumed to be defined elsewhere in the codebase.

**Notable Dependencies and Requirements**

* `os`: for file system operations
* `PATH_TO_TRANSFORMERS`: implied path to transformer submodules

**Primary Purpose**
The primary purpose of this function is to determine whether a given string should be excluded from documentation based on specific rules.


key: chroma:document
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**Summary**

`check_all_objects_are_documented()` function:

*   **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.

**Key implementation details**:

*   Checks objects in the `transformers` namespace using dir() to find non-module, non-internal private objects.
*   Verifies if objects are not already documented by calling `find_all_documented_objects()` and ignoring certain method names using `ignore_undocumented(c)`.
*   Raises an exception with a list of undocumented objects if any exist.

**Notable dependencies or requirements**:

*   Calls other functions: `find_all_documented_objects()`, `check_model_type_doc_match()`, and `check_public_method_exists(documented_methods_map)`. 
*   References classes/functions: `transformers._modules`, `dir(transformers)`.
*   External dependencies: None mentioned.
*   Implements interfaces: [Unspecified, as it appears to be a custom class or module within the `transformers` namespace.


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**Function Summary:**

`check_public_method_exists(documented_methods_map)`
=============================================

**Purpose:** Ensures that all explicitly documented public methods are defined in the corresponding class.

**Main Functionality:**
- Verifies consistency of documented public methods across the specified map.
- Resolves method definitions within nested object hierarchies using dynamic imports.

**Key Implementation Details:**

- Iterates through each object and its associated classes, ensuring unique documented method names.
- Handles class and method name resolutions via `getattr` with conditional error handling for unknown submodules or dependencies.

**External Dependencies:**
- `transformers`: A required submodule.

**Interfaces and Methods:** 

- The function iterates over a dictionary mapping objects to their listed public methods (`documented_methods_map`).


key: chroma:document
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**Summary**

This 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.

**Key Implementation Details**

* Iterates through documentation pages under `PATH_TO_DOC/model_doc`
* Uses `transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING` to get existing model types
* Compares each doc page's name with the model types, providing close matches for non-matching names
* Raises an error if any doc pages do not match an existing model type

**Note**

The function relies on external dependencies:

* `transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING`
* `get_close_matches` (not shown in this code snippet)
* `models/auto/configuration_auto`


key: chroma:document
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**Summary**

The `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:

* All models in the deprecated folder are included in the constant.
* None of the models listed in the constant are not present in the deprecated folder.

**Key Implementation Details**

* The function uses a list comprehension to extract model names from the deprecated folder.
* It compares the models in the `DEPRECATED_MODELS` constant with those in the deprecated folder using set differences.
* If any inconsistencies are found, it raises an exception with a descriptive message.

**Notable Dependencies and Requirements**

* `transformers`: A dependency for accessing the `models.auto.configuration_auto` module.
* The function assumes that the constants and folders referenced are located within this module.


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**Summary:**

The `check_repo_quality` function runs a sequence of checks on a repository, evaluating various aspects of model quality and configuration. Specifically, it:

*   Verifies models' inclusion
*   Inspects public visibility of models
*   Confirms test existence for models
*   Checks for documentation presence in objects
*   Validates auto-class configurations
*   Examines auto-mapping definitions and importability
*   Ensures object distribution in `__init__`
*   Verifies the `DEPRECATED_MODELS` constant's accuracy

**Key Functions/Methods:**

- `check_model_list()`: Verifies models' existence
- `check_models_are_in_init()`: Inspects public visibility of models
- `check_all_decorator_order()`: Confirms test existence for models
- `check_all_models_are_tested()`: Confirms documentation presence in objects
- `check_all_objects_are_documented()`: Validates auto-mapping definitions and importability

**Notable Dependencies/Requirements:**

- The function relies on the `CONFIG_MAPPING_NAMES` constant, indicating an external dependency.
- It also expects the existence of a `DEPRECATED_MODELS` constant, implying external configuration or setup.

Please note that further context is necessary to provide a more accurate analysis.


key: chroma:document
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**Code Summary**

### Function Purpose

`clean_doctest_list` cleans and organizes a doctest file by removing non-existent paths and sorting the remaining paths in alphabetical order.

### Key Implementation Details

* The function reads a doctest file line-by-line, stripping leading whitespace and splitting on spaces.
* It checks if each path exists and creates a list of non-existent paths.
* If any non-existent paths are found, they are reported as an error.
* 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.

### Notable Dependencies

* `os.path`: used for path manipulation (e.g., `join`, `resolve`)
* `REPO_PATH`: assumed to be a predefined constant containing the repository root path

### Method Signature and Interfaces

| Method/Interface | Description |
| --- | --- |
| `clean_doctest_list(doctest_file: str, overwrite: bool = False)` | Cleans and organizes a doctest file. |

### External Dependencies

None mentioned in this snippet, but `os` module is used implicitly.


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**Summary:**

This function, `_is_definition_header_ending_line`, determines whether a given line of code indicates the end of a class or function definition.

```markdown
### Main Purpose and Functionality

Determines if a line ends an opening parenthesis of a class or function definition.

### Key Implementation Details

- Uses regular expression (`re.search`) to search for the pattern `^\s*\)(\s*->.*:|:)\s*$` at the start of the line.
- Returns `True` when the pattern is found, indicating an ending definition line.
- The primary purpose is to identify lines that signal the completion of a class or function definition.

### Notable Dependencies and Requirements

None listed, but uses Python's built-in `re` module for regular expression matching.
```

**Additional Information:**

This function does not depend on any external libraries beyond the `re` module.


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**Summary:**

- `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.

**Implementation Details:**

- Checks for three conditions:
  - Starts with the specified indent (`line.startswith(indent)`)
  - Is an empty line after stripping whitespace (`len(line.strip()) == 0`)
  - Marks the end of a definition header using `_is_definition_header_ending_line(line)`


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**Function Summary: `_sanity_check_splits`**

### Purpose and Functionality

Verifies 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.

### Key Implementation Details

* Class block structure checking:
	+ Supports single block without name and consecutive sequence of blocks with name
	+ Number of blocks with name in consecutive sequence not considered
* Function/method block structure checking:
	+ Exact matching of block arrangement required

### Notable Dependencies and Requirements

* `is_class` flag indicating class definition or not
* `filename` parameter for logging purposes
* Internal constants and interface (`_block_without_name_`, `_empty_block_`)


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**Function Summary:**
`find_block_end`
---------------

Find the index of the block's ending line in a source code.

```python
def find_block_end(lines: List[str], start_index: int, indent: int) -> int:
    # ...
```

### Purpose and Functionality:

This function finds the end of a class/func block in a source code, starting from `start_index`.
It iterates through the lines until it encounters an invalid continuation line or reaches the end of the code.

### Key Implementation Details:

- Iterates until a blank or invalid line (`_should_continue`) is encountered.
- Cleans up empty lines at the end.
- Returns the index of the block's ending line (inclusive).

### Dependencies and Requirements:
 No external dependencies.


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**Function Summary: `split_code_into_blocks`**

### Purpose and Functionality
Splits the code block in the specified range into inner blocks, including contiguous regions without empty lines.

### Key Implementation Details

* Takes source code `_lines`, start index `_start_index`, end index `_end_index`, and `indent_level`.
* Uses a regular expression to find the definition header.
* Iterates through the code to find inner blocks using a regular expression.
* Handles backtrace by including lines before the inner block's definition header.

### Method Signatures and Interfaces

* Takes a list of strings (`lines`) as input.
* Returns a list of tuples containing string, start index, and end index (`List[Tuple[str, int, int]]`).

### Notable Dependencies or Requirements

* Uses the `re` module for regular expressions.

### Output Format
Returns a list of tuples containing block names, start indices, and end indices.

### Summary

Splits code into inner blocks using a regular expression-based approach. It handles backtrace and returns a list of tuples containing block information.


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**Summary: `find_code_in_transformers` Function**

The `find_code_in_transformers` function finds and returns the source code of an object in the transformers library.

### Purpose and Functionality

* Takes an object name, base path (optional), and return indices as input arguments.
* Returns either the object's source code or a tuple containing the source code, file lines, start index, and end index.

### Key Implementation Details

* Finds the module where the object lives by splitting the object name and checking existing files in the base path.
* Parses the Python code to find the class or function specified by the object name, returning the source code if `return_indices` is False.

### Notable Dependencies and Requirements

* Uses external dependencies:
	+ `os` for file operations
	+ `re` for regular expression matching
	+ `transformers` library (not explicitly imported)
* Inherited or implemented interfaces:
	+ None, but uses Python's built-in functions such as `open`, `readlines`, and `find_block_end`.

### Method Signatures

The function takes the following input arguments:

* `object_name`: str
* `base_path`: str (optional)
* `return_indices`: bool (optional)

The function returns a Union type containing either str or a tuple with the following elements:
	+ str: source code of the object, if `return_indices` is False.
	+ Tuple[List[str], int, int]: file lines, start index, and end index, if `return_indices` is True.


key: chroma:document
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**Summary**

`replace_code` function:

* Replaces occurrences of `obj1` with `obj2` in `code`, optionally applying casing conversions (`all-casing` option).
* Takes two inputs: `code` (string) and `replace_pattern` (string, formatted as "with X1->X2,Y1->Y2,Z1->Z2").
* Utilizes regular expressions to perform replacements, leveraging `_re_replace_pattern.search()` and `re.sub()` functions.

**Key Implementation Details**

* Uses regular expression substitutions (`re.sub()`) to replace patterns in the input code.
* Applies casing conversions for the "all-casing" option using additional `re.sub()` calls.
* Utilizes `_re_replace_pattern.search()` to extract pattern groups (e.g., `obj1`, `obj2`, and `option`).


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**Code Summary: `find_code_and_splits` Function**

**Purpose and Functionality:**
The `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.

**Key Implementation Details:**

*   Uses `find_code_in_transformers` to search for the code in the transformers library.
*   Utilizes `split_code_into_blocks` to split the retrieved code into blocks based on indentation levels.
*   Expects the `buffer` parameter to store previous results and retrieve them if available.

**Notable Dependencies and Requirements:**

*   `find_code_in_transformers`: Function from transformers library or custom implementation.
*   `split_code_into_blocks`: Custom or third-party function for block splitting.
*   `get_indent`: Custom or external function to calculate indentation levels.


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**Function Summary**

GetIndent Function: Finds the indent in the first non-empty line of code.

**Primary Purpose:** Extracts the leading whitespace from the first non-empty line of code.

**Key Implementation Details:**

* Splits code into individual lines
* Iterates through lines until a non-empty one is found
* Uses regex to match and extract the leading whitespace

**Notable Dependencies:**

* `re` module for regular expression matching ( referencing `search()` method)
* No external dependencies listed, but assumes Python environment with `str` and `list` data types.

**Method Signature:** `get_indent(code: str) -> str`

This 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.


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**Summary:**

`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.

**Key Implementation Details:**

- Utilizes `subprocess.Popen` for executing the Ruff command
- Supports both `check` (linting) and `format` (code reformattting)
- Uses `pyproject.toml` as default configuration file

**Dependences/Requirements:**

- Ruff code formatter (`ruff`)
- `pyproject.toml`

Note: The function returns the formatted output from the standard output of the Ruff command.


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Here is a concise summary of the `stylify` function:

**Summary:**
Converts input Python code to be compatible with RUFF's formatting style.

**Key Details:**

* Formats input code using the `ruff format` command
* Appends an empty class wrapper to preserve indentation if present
* Returns the formatted code without the wrapped class prefix

**Notable Dependencies:**

* `run_ruff`: External function from external library (not shown)
* `get_indent`: External function from external library (not shown)


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**Function Summary**

`check_codes_match`

Checks if two versions of a code match with the exception of class/function name.

**Purpose and Functionality**

Compares 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.

**Key Implementation Details**

* Uses regular expressions to extract class/function names from header lines.
* Compares each line in the code separately, ignoring the extracted names.

**Notable Dependencies or Requirements**

* Python's `re` module for regex pattern matching
* Python's `Optional` type hinting

**Return Value**

`Optional[int]`: The index of the first difference or `None` (no difference)

**Method Signatures and Interfaces**

* `_re_class_match` and `_re_func_match` regular expression patterns for class/function name extraction.

Note: This summary focuses on the most important information about the function, omitting minor details.


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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:

* Overwriting existing files
* Handling empty lines and whitespace differences
* Respecting the original block order when ignoring certain blocks
* Stylifying codes for comparison

Here's a brief overview of what the function does:

1. It reads two versions of the code file, comparing them line by line.
2. If there are changes between the two versions, it reports these changes and tracks their corresponding indices in both versions.
3. Depending on whether to overwrite existing files or not, it either makes the necessary modifications or simply returns a list of reported differences.
4. It also provides an option for modifying the code file in-place if there are any detected changes.

The 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.


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**Summary**

The `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.

**Key Takeaways**

* Function: `check_copies`
* Purpose: Validate file copies and report inconsistencies
* Parameters:
	+ `overwrite`: Boolean flag to overwrite discrepancies (default: False)
	+ `file`: Optional path to a single file for manual inspection (defaults to None)

**Implementation Details**

* Iterates through a list of files in specified directories (`all_files`)
* Uses `is_copy_consistent` function to check copy consistency for each file
* Reports inconsistencies via an exception if overwrite is False and discrepancies are found

**Notable Dependencies/Interfaces**

* `os.path.join`
* `glob`
* ` TRANSFORMERS_PATH`
* `MODEL_TEST_PATH`
* `is_copy_consistent` (function, dependencies not specified)


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**Function Summary: check_full_copies**

 Checks files indicated as full copies (`FULL_COPIES`) for consistency and raises an exception if the copies differ.

**Functionality**

* Compares source code with target copy
* If the target copy differs, considers overwriting the target (if `overwrite` is True) or logs a difference (if `overwrite` is False)
* Rises an exception if no overwrites is made and differences are found

**Key Implementation Details:**

* Uses dictionary `FULL_COPIES` to map source files to their corresponding target files
* Reads file contents using open() with encoding="utf-8"
* Compares source code (`source_code`) with target code (`target_code`)
* If the copies differ, uses a list to track differences (`diffs`)
* Uses exception handling to raise an error if no overwrites is made and differences are found

**Notable Dependencies/Requirements:**

* Python 3.7+ (encoding support)
* Makefile or Python script (utils/check_copies.py)

**Important Method Signatures and Interfaces:**

* `check_full_copies()`: Function to check files for consistency
	+ `overwrite**: Boolean flag to control overwrite behavior
	+ `FULL_COPIES`: Dictionary mapping source files to target files


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**Function Summary:**

`get_model_list`: Extracts and returns the model list from a README file.

**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.

**Key Implementation Details:**

* Reads the entire file into memory.
* Uses two prompts (`start_prompt` and `end_prompt`) to determine the start and end indices of the model list.
* 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.
* Handles cases where the current line has more than one character after processing.

**Dependencies and Interfaces:**

* `os`: for file path manipulation
* `REPO_PATH` (not shown in this code snippet): likely an environment variable or global constant containing the repository root directory.


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**Function Summary**

`convert_to_localized_md`

Compares model list from main README to localized README and updates the latter with synchronized metadata.

**Main Purpose and Functionality**

This 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.

**Key Implementation Details**

* Uses regular expressions to capture metadata from English model descriptions.
* Checks for matching models in both READMEs and updates the localized one if necessary.
* Synchronizes title link, paper title, and its link (if found) using regex patterns.

**Notable Dependencies or Requirements**

* `re` module for regular expression operations.
* `Tuple` type hint to indicate return value format.

Note: 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.


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**Function Summary:**

`_find_text_in_file` function finds text in a file between two prompts.
```python
import typing as t
from typing import Tuple, List

def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> Tuple[str, int, int, List[str]]:
    """
    Args:
        filename (str): File to search in.
        start_prompt (str): String introducing the content searched for.
        end_prompt (str): String marking the end of the content searched for.

    Returns:
        A tuple containing:
            - The extracted text.
            - The index of the start line in the original file.
            - The index of the end line in the original file.
            - The list of all lines in the file.
    """
```

**Key Implementation Details:**

* Finds start and end indices by searching for prompt strings.
* Handling cases where prompts may be on empty lines or follow multiple empty lines.

**Notable Dependencies and Requirements:**

* `open` function from Python's standard library for reading files.
* `re` module is not explicitly imported, but it might be implicitly used through string methods.


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**Code Summary**

### Function Purpose

`get_runner_status` retrieves the status of GitHub Actions runners from the GitHub API.

### Key Implementation Details

* Fetches runner status using `curl` and Parses the response with JSON
* Compares runner names against `target_runners` list to retrieve offline runnersonly
* Saves result to `offline_runners.txt`

### Notable Dependencies/Requirements

* `github-api`: implicit, accessed via `https://api.github.com/repos/huggingface/transformers/actions/runners`
* `json` and `subprocess` modules for standard I/O and execution
* `Authorization` token for authentication (Bearer `token`)


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**Function Summary: `check_flash_support_list`**

Purpose:
This function checks if FlashAttention-2 (FA2) support is included for various architectures based on documentation and code patterns.

Main Functionality:

1. Reads documentation text to extract supported architectures.
2. Searches for model files with FA2 support in `modeling*` pattern and initializes a list of supported architectures (`archs_supporting_fa2`).
3. Compares the extracted supported architectures against the ones listed in FA2 documentation and raises a ValueError if any mismatch.

Key Implementation Details:

- Uses glob to search for files under `src/transformers/models` with models containing "_supports_flash_attn_2 = True".
- Utilizes string splitting to extract relevant text from documentation files.
- Compares lists of supported architectures using set operations.

Notable Dependencies and Requirements:
- `os.path.join` and `open` functions for file system interactions
- `glob` function for pattern matching
- `REPO_PATH` constant referencing the repository's root path
- External dependency: FlashAttention documentation (`perf_infer_gpu_one.md`) and model files containing "_supports_flash_attn_2 = True".


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**Functionality Summary:**

The `check_sdpa_support_list` function checks if various Transformer models support SDPA (Software Distributed Performance Architecture) inference and training.

### Main Purpose:

1. Retrieves documentation text from a file (`docs/source/en/perf_infer_gpu_one.md`) to determine which architectures are supported by SDPA.
2. Iterates through model files in the `src/transformers/models` directory and extracts model names that contain the line `_supports_sdpa = True`.
3. Verifies if each detected model is listed in the retrieved documentation text, raising a ValueError if any are not found.

### Key Implementation Details:**

* The function uses regular expressions to extract specific lines from the documentation text.
* It utilizes glob to find model files matching certain naming patterns (`modeling_*.py`) and excludes files using `FlashAttention`.
* Model names are appended to a list (`archs_supporting_sdpa`) based on the presence of the `_supports_sdpa = True` line.

### Notable Dependencies and Requirements:**

* The function relies on the following external dependencies:
	+ `os.path.join()` for path manipulation
	+ `fnmatch` for glob pattern matching (not explicitly required, as Python's built-in `glob` module is used)
	+ `re` for regular expression operations (not explicitly required, but implied by string splitting and searching)

### Contextual Considerations:

This function appears to be part of a automated documentation checking system, verifying the support list of various Transformer models against actual implementation.


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**Summary:**

`handle_test_results` function processes test results by:

* Splitting into individual expressions containing "failed" and "passed"
* Calculating total failed and success tests count
* Extracting time spent in the output string, assuming it's surrounded by "= =" for short outputs

**Implementation Details:**

* Iterates through expressions using `enumerate`
* Uses exact method signaturs to access values: `int(expression[i - 1])` 

**Dependencies/Requirements:**

* Presumes test results are comma-separated strings
* Expects output string to contain a specific format (`"{value} = {expression}"`)
* No external dependencies listed, but assumes `test_results` input is valid.


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**Function Summary**

`handle_stacktraces`: Processes test results to extract and format error messages from stacktraces.

### Functionality

* Takes `test_results` as input
* Splits input into lines, excluding the first and last (header/footer)
* Iterates through each line, extracting:
	+ Line number from path element before first space character
	+ Error message from remaining path elements 
* Returns a list of formatted error messages

### Key Implementation Details

* `total_stacktraces` is extracted using `split("\n")`
* Uses indexing to extract line and error parts of each stacktrace
* Catches exceptions during processing, appending "Cannot retrieve error message." if an error occurs

### Notable Dependencies

* No external dependencies referenced
* Assumes input test results are structured according to specified architecture


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**Function Summary**

`dicts_to_sum` function:

* Takes a single `objects` parameter with two acceptable inputs:
	+ A dictionary of dictionaries (`Dict[str, Dict]`)
	+ A list of dictionaries (`List[dict]`)
* If input is a dictionary, it extracts the list of inner dictionaries.
* 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.

**Key Implementation Details:**

* Uses `collections.Counter` for efficient counting
* Applies a cumulative reduction using `functools.reduce` and `operator.add`

**Notable Dependencies/R Requirements:**

* `unico` (not 'Union', assumed typo, corrected to `Union`, actual is not present in this code snippet)
    * No explicit dependencies, but it utilizes:
        + `collections.Counter`
        + `functools.reduce`
        + `operator.add`
        + Assume a dictionary `Dict[str, Dict]` for conversion from other list format or use `List[dict]` as stated in the documentation


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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.

Key Features:

*   **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.
*   **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.
*   **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.

Improvement Suggestions:

1.  Add Exception Handling:
    *   Consider raising exceptions instead of silently failing on certain operations like fetching results where `ci_results_{job_name}` does not exist.
2.  Optimize Post Operations:
    *   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.

Here's an example implementation of error handling with exceptions:

```python
try:
    # your operations here...
except Exception as e:
    print(f"An error occurred: {e}")
```

Remember 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.


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**Function Summary**

`retrieve_artifact`: Retrieves artifacts from a specified path.

**Purpose and Functionality**
Extracts artifact files from the given `artifact_path`, including file contents in decoded format. Handles invalid GPU configurations and non-existent paths.

**Key Implementation Details**

* Checks valid GPU configurations (`single`, `multi`, or `None`).
* Uses `os.listdir` to get a list of files in the specified path.
* Iterates through each file, attempts to open and read its contents using `with` statement.
* Raises error on non-existent paths and fails to decode Unicode characters.

**Notable Dependencies and Requirements**

* `[os]` module for interacting with the file system.
* `Optional[str]` type hint for indicating that `gpu` can be `None`.
* `Unicode Decode Error` handling raised by Python.


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**Summary: `retrieve_available_artifacts` Function**

 retrieves and manages available artifacts, which are stored in the `_available_artifacts` dictionary.


*   **Main purpose**: This function iterates through directories on disk and populates an internal data structure with artifact information.
*   **Key implementation details**:
    *   The `_available_artifacts` dictionary is populated with `Artifact` objects, where each `Artifact` represents a directory-based asset.
    *   Directory names are used to determine the type of GPU required for the asset (single or multi).
    *   Assets are associated with paths and gpu types using the `add_path` method.
*   **Notable dependencies**:
    *   `os` module for interacting with file systems
    *   `filter` function from `itertools` for filtering directories
    *   `Dict` ( dictionary ) type hint


(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.)


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**Function Summary: prepare_reports**

**Purpose:** Generate a formatted report string with truncated content if necessary.

**Primary Functionality:**
Prepares 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.

**Key Implementation Details:**

* Iterates through each report in descending order using `_report = header + "\n".join(reports[: idx + 1])`.
* Checks for truncation conditions inside an `if` block (`if len(new_report) > MAX_ERROR_TEXT:`).
	+ Truncates the output with "[Truncated]" if necessary.

**Notable Dependencies/Requirements:**

* No external dependencies listed.
* Uses specific constants:
	+ `MAX_ERROR_TEXT` defined as a fixed value (3000 - 1).

Note this summary excludes unnecessary or generic details, focusing on the primary purpose and key implementation mechanics of the provided function.


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**Summary**

`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.

**Main Purpose and Functionality**
Inspect source code for attribute usage (`config.xxx`, `getattr()` constructs)

**Key Implementation Details**

* Check for direct attribute mentions (`f"config.{attribute}" in modeling_source`)
* Detect multi-line `getattr()` patterns
* Handle specific use cases like `TextConfig` and `SequenceSummary`

**Notable Dependencies or Requirements**
- `re` (regular expression library)
- `config_class` with its specific class methods (`__init__`, `get_text_config()`)


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**Summary: `check_config_attributes_being_used` Function**

This function checks if the configuration class's initialization arguments are used in the same directory's modeling files. It does this by:

1. Analyzing the init signature of the config class, identifying parameter names and default values.
2. Checking for existence and usage of attribute maps, which can map variant names to different attribute names.
3. Scanning model source files to detect attribute appearances that match the calculated attributes.

**Key Implementation Details:**

* Checks `attribute_map` for variants, allowing one usage to pass even if multiple variants exist.
* Utilizes `inspect.signature` and `os.path.join` for file path manipulation.
* Calls `check_attribute_being_used` internally to verify attribute usage against modeling sources.

**Notable Dependencies/Requirements:**

* `inspect`: For accessing signature information and getting source files.
* `os`: For handling file paths and directories.
* `config_class`, which is expected to have an `attribute_map` dictionary.

This function returns a list of unused attributes, sorted for consistency.


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**Function Summary:**

`check_config_attributes`: Checks if configuration class arguments in `__init__` are used in corresponding Python files and raises an error for unused attributes.

**Key Implementation Details:**

* Iterates through all configuration classes and their submodules to identify potential issue sources
* Uses `check_config_attributes_being_used` function to detect unused attributes in each configuration class

**Notable Dependencies/Requirements:**

* `CONFIG_MAPPING`: a dictionary mapping configuration classes (not defined in provided code snippet)
* `PretrainedConfig` class (assumed to be defined elsewhere)

**Primary Purpose:** To ensure that configuration class arguments are consistently used in corresponding Python files.


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**Function Summary:**

`get_jobs` extracts GitHub Actions workflow run jobs.

**Purpose:**
Extracts job data from a GitHub Actions workflow run API.

**Key Implementation Details:**

*   Uses the GitHub API to fetch job data with pagination.
*   Utilizes the `requests` library to send GET requests and parse JSON responses.

**Method Signatures:**

*   `requests.get(url, headers=headers)`: Sends a GET request to the specified URL with provided headers.
*   `.json()`: Parses the response as JSON.

**Notable Dependencies:**

*   `requests`
*   `math` (for calculating total pages)

**Interfaces Implemented:**

*   GitHub API (`https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs`)

This 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.


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**Function Summary: get_job_links**

**Main Purpose and Functionality:**
The `get_job_links` function extracts job names and their corresponding GitHub Actions workflow run links from the Hugging Face Transformers repository.

**Key Implementation Details:**

* Uses GitHub API to fetch job data in paginated chunks (up to 100 jobs per page).
* Supports token-based authentication for request headers.
* Iterates over multiple pages of results to collect all job links.

**Notable Dependencies and Requirements:**

* Python 3.6+
* `requests` library for HTTP requests
* `math` library for calculating pagination parameters
* Hugging Face Transformers repository GitHub API permissions ( token for authentication)


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**Summary**

This 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.

**Key Functionality:**

*   Fetches GitHub API for archive download URLs of artifacts within a workflow run.
*   Iterates over multiple pages to collect all required links until exhausted.
*   Handles pagination, total count, and error cases.

**Implementation Details:**

*   Uses `requests` library with optional authentication token via `Authorization` header.
*   Utilizes JSON parsing for receiving API response data.
*   Performs dictionary updates for efficient collection of artifact URLs.

**Notable Dependencies/Requirements:**

*   `math` for calculating pages to iterate.
*   `requests` and `json` for interacting with the GitHub API, handling responses.
*   `traceback` for error reporting in case of an issue.

**Primary Purpose:**
To fetch archive download URLs for artifacts from a specified GitHub Actions workflow run.

**Key Method Signatures:**

*   `requests.get()`: To fetch pages from the API.
*   `dict.update()`: To add new entries to the dictionary of artifact links.
*   `math.ceil()`: For calculating the number of pages to iterate over.


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**Function Summary: `download_artifact`**

Downloads a GitHub Action artifact from a URL.

**Primary Functionality:**
This 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.

**Key Implementation Details:**

* Uses the `requests` library to send HTTP requests
* Sets headers with `Authorization` and `Accept` fields based on whether a token is provided
* Sets `allow_redirects=False` initially, but switches to `True` when downloading the redirect URL
* Retrieves the file path from the URL's `Location` header

**Notable Dependencies:**

* `requests`
* `os`

**Method Signatures and Interfaces:**
- `requests.get()`: sends a GET request to a specified URL
- `Authorization` flow for token authentication

Note 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.


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**Code Summary: `get_errors_from_single_artifact`**

The primary purpose of this function is to extract error messages and related test information from a ZIP archive downloaded as input.

**Key Implementation Details:**

* Uses the `zipfile` module to iterate over files in the ZIP archive.
* Checks specific file contents (e.g., `failures_line.txt`, `summary_short.txt`, `job_name.txt`) for relevant data extraction.
* Processes errors and failed test information using try-except blocks.
* Raises an error if there's a mismatch between extracted error messages and failed tests.

**Method Signatures and Interfaces:**

* `get_errors_from_single_artifact(artifact_zip_path, job_links=None)`: The main function with two input parameters.
* `zip.open(filename) as f`: Used to read file contents within the ZIP archive.
* `job_links.get(job_name, None)`: Retrieves a job link based on a matching job name (if provided).

**Notable Dependencies and Requirements:**

* Python 3.x
* `zipfile` library for working with ZIP archives.
* External dependencies:
	+ `os` module for path operations.

**Primary Purpose Statement:**

Extracts errors from a downloaded artifact in XML format, mapping error lines to corresponding failed tests.


key: chroma:document
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**Summary:**

This 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.

**Key Implementation Details:**

* Searches for `.zip` files recursively
* Calls `get_errors_from_single_artifact` on each file with optional `job_links`
* Extends errors list with results from all extracted artifacts

**Notable Dependencies:**

* `os.path.join`
* `os.listdir`
* `get_errors_from_single_artifact` ( referenced function, not shown in original snippet)

**Primary Purpose:**
Extract and return a list of error messages from artifact file zip archives.


key: chroma:document
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**Summary:**

The `reduce_by_error` function clusters test log errors into a dictionary, grouped by frequency and containing failed tests. It takes in:

*   Logs with error codes as values
*   Optional error filter list
*   Counts each unique error using `Counter` and returns the results sorted by count in descending order.

**Implementation Details:**

*   Updates a `Counter` object to count unique errors from logs.
*   Creates a dictionary (`r`) to store clusters of errors, where each cluster contains:
    *   Error code
    *   Count
    *   Failed test(s) with original log index and error code
*   Sorts the final dictionary by error count in descending order.

**Dependencies/Requirements:**

*   `Counter` from collections module for counting unique errors.
*   List of expected error filter values (if provided).


key: chroma:document
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**Summary:**

* Function `get_model`: retrieves the model name from a test method or path.
 
**Key Implementation Details:**
 
* Splits input string by '::' and takes first part as model name
* Checks if input starts with 'tests/models/' prefix, and splits by '/' to extract model name
* Returns extracted model name, else returns None

**Notable Dependencies/Requirements:**

* None


key: chroma:document
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**Function Summary:** `reduce_by_model`

**Purpose:** Group and count errors by model, filtering out those excluded by the input `error_filter`.

**Implementation Details:**

* Uses list comprehension to enrich log data with the corresponding model type
* Applies error filtering using `error_filter` if provided
* Counts occurrences of each error in the `test` group using a `Counter`
* Calculates the total number of errors for each test and stores them in a dictionary

**Key Method Signatures:**

* `most_common()`: Returns the most common values and their counts via `Counter`

**Notable Dependencies/Requirements:**

* Standard library: `collections.Counter`, function `get_model`
* External dependency: None

**Context:** This code appears to be part of a logging analysis workflow, grouping errors by model type for further analysis.


key: chroma:document
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**Function Summary: generateGitHubTable**

**Purpose:** Generate a formatted table of GitHub errors with status and count.

**Implementation Details:**

* Uses a fixed header line with four columns.
* Utilizes a separator line to split the data into rows.
* Loops through each error in `reduced_by_error`, creating a single row per error with the error name truncated to 100 characters.
* Joints all lines together using newline characters.

**Key Features:**

* `make_github_table` function takes a dictionary of errors as input (`reduced_by_error`).
* Returns a string representing the formatted table.

**Dependencies and Requirements:** 

* None explicitly defined, but assumes a Python environment with standard library access.


key: chroma:document
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**Summary**

**Functionality**
Create a GitHub table per model with errors, displaying:
- Model name
- No. of errors
- Major error description (truncated to 60 characters)
- Error count

**Implementation Details**
- Iterates through `reduced_by_model` dictionary
- Extracts key model data: error count and major error details
- Builds table format string with dynamic values
- Returns a formatted string with the table lines joined by newline (`\n`)

**Key Components**

* `model`: reduced_by_model dictionary keys (model names)
* `errors`: values for each model, containing error and count data
	+ error: first key-value pair item (assuming single major error per model)
	+ count: corresponding value

**Notable Dependencies/Requirements**
* None


key: chroma:document
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**Summary:**

Updates the `version` parameter within a specified file of type `file_type`.

**Key Implementation Details:**

* Reads the contents of the file and replaces the `VERSION` placeholder with the provided `version`.
* Uses regular expression substitution (`REPLACE_PATTERNS[file_type]`) to update the version in the code.
* Writes the updated code back to the original file.

**Notable Dependencies:**

* Expects `REPLACE_PATTERNS` dictionary containing replacement patterns for different file types.


key: chroma:document
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**Summary**

`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.

**Key Implementation Details**

* Removes outdated project folders ("research_projects" and "legacy")
* Iterates over file extensions (.py) to detect Python files
* Calls `update_version_in_file` function for each Python file to update its version with the specified `version`

**Notable Dependencies or Requirements**

* Uses `os` module for walking directories and accessing paths
* Utilizes `PATH_TO_EXAMPLES` environment variable, not defined in this code snippet.

* `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.


key: chroma:document
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**Function Summary:**

`global_version_update`

Updates the specified version in all required files.

* Parameters:
	+ `version`: New version string
	+ `patch`: Boolean indicating patch release (default: False)

Key Implementation Details:

* Iterates through `REPLACE_FILES` dictionary to update files with pattern matches.
* Calls `update_version_in_file` method for each matched file.
* Excludes updating examples if not a patch release.

Notable Dependencies/Requirements:

* `REPLACE_FILES` dictionary
* `update_version_in_file` method
* `update_version_in_examples` function


key: chroma:document
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**Summary:**
`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.

**Key Details:**

* Removes Python scripts (`convert*.py`) from the `PATH_TO_MODELS` directory using `globbing`.
* Deletes each script found using `unlink()` after listing them for removal.

**Notable Interface/Class:**

* `Path`: provides file system path manipulation.
* `glob()`: performs Unix shell-style string matching on paths.

**External Dependencies:**

None noted.


key: chroma:document
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**Code Summary**

Retrieves the current version from the `__init__.py` file and parses it into a `packaging.version.Version` object.

**Key Details**

* Reads content from `REPLACE_FILES["init"]`
* Searches for default version in `code` using regex pattern `REPLACE_PATTERNS["init"][0]`
* Returns parsed version using `packaging.version.parse()`

**Dependencies**

* Expects `packaging` library with `version` module
* Uses regular expressions (`re`) with `search()` method


key: chroma:document
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**Function Summary: `pre_release_work`**

### Main Purpose and Functionality
The `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.

### Key Implementation Details

* Returns a default minor release version if patching, otherwise increments the minor version.
* Asks user confirmation to choose the release version.
* Updates global `version` using `global_version_update`.
* Removes conversion scripts using `remove_conversion_scripts`.

### Notable Dependencies/Requirements

* `get_version()` function
* Global variables: `base_version`, `major`, `minor`, and `micro`

### Interfaces Implemented/Inherited

* None mentioned, but may be inherited from parent class or interface.


key: chroma:document
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**Summary: post_release_work Function**

**Purpose and Functionality:**
The `post_release_work` function performs the necessary steps after a release, including:

* Obtaining the current version
* Confirming with the user that the next development version is correct
* Updating all versions accordingly

**Key Implementation Details:**

* Calls `get_version()` to retrieve the current version.
* Calculates the next development version by incrementing the minor version of the current version.
* Queries the user to confirm the chosen version using `input()`.
* Updates global versions using `global_version_update(version)`.

**Notable Dependencies and Requirements:**
No external dependencies mentioned. However, it is assumed that:

* `get_version()` function is available and returns a Version object with attributes such as `major`, `minor`, `base_version`.
* `global_version_update()` function is available and accepts a version string as an argument.

**Interface and Inheritance:**

* Implements no explicit interfaces. However, it assumes the existence of custom attributes for the `Version` class returned by `get_version()`.


key: chroma:document
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**Summary:**

`count_lines` function counts the number of lines in a given file.
 
**Key Implementation Details:**
- Reads a file line by line using `open` and stores content in a variable (`f.read()`).
- Splits content into lines using newline character (`"\n"`).
- Returns the count of lines.

**Notable Dependencies:**
- [Filesystem module](https://docs.python.org/3/library/os.html#module-os) for file operations.
- This function does not use any external Python libraries, it is a fundamental file handling task.


key: chroma:document
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**Summary:**

* **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.

* **Key Implementation Details:**
  - Calculates `num_nodes` based on `line_count` and average tests per nodes.
  - Applies a soft cap at 1 node for small line counts (<4) to prevent division by zero errors.

* **Notable Dependencies/Requirements:**
  - `math.ceil()`: used to calculate ceiling of the division result
  - `AVERAGE_TESTS_PER_NODES`, `MAX_PARALLEL_NODES`: constants imported from parent module, representing average tests per nodes and maximum parallel nodes allowed, respectively.


key: chroma:document
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**Function Summary: `process_artifacts`**

### Main Purpose and Functionality

The `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.

**Transformation Rules:**

* If an item's path contains a "test_list" key, extract the key by removing the file extension using `os.path.basename`.
* Compute parallelism levels based on count of nodes in corresponding text files using `compute_parallel_nodes`.
* Store transformed data in a new JSON structure with `transformed_data`.

**Key Implementation Details:**

* Uses `json.load` to read input JSON file.
* Utilizes `count_lines` function to compute parallelism levels.

**Notable Dependencies or Requirements:**

* External dependency on the `os`, `json`, and `file_utils` (assuming `compute_parallel_nodes` is an external function).
* Assumed implementation of `compute_parallel_nodes` function is not shown in this code snippet.

### Additional Information:

The 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.


key: chroma:document
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**Summary:**

### Purpose:

This function retrieves the merge commit in a GitHub repository (using `git` commands) for a specific pull request (PR), given a tag range (`since_tag`).

### Functionality:

1. **Searches for merge commit**: Uses `repo.iter_commits` to find the first commit that matches the PR number within the specified tag range.
2. **Catches errors**:
	* `StopIteration`: No matching commit found; returns `None`.
	* `GitCommandError`: Error during command execution (e.g., invalid syntax); prints error message and returns `None`.

### Key Details:

* Method: `repo.iter_commits`
* Git log filter: `f"v{since_tag}...origin/main and grep=f#{pr_number}"`
* Returns a single merge commit object on success or `None` on failure.

**Notable Dependencies:** None

**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.


key: chroma:document
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**Summary:**

### Main Functionality

The `main` function takes a list of PR numbers and generates Git cherry-pick commands to apply merge commits across tag ranges.

**Key Implementation Details:**

* **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.
* **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.

**Notable Dependencies or Requirements:**

* `Repo()` object (assumed to be a GitHub API client)
* External dependency on `version.parse()` for tag validation
* Inherited interface from `get_merge_commit()` function, which is not provided in this code snippet

### Code Summary:

```
def main(pr_numbers):
    # Initialize Repo and manage tags
    repo = Repo(".")  # ...
    tags = {}  # ...

    # Find merge commits across PR tag ranges
    for pr in pr_numbers:
        commit = get_merge_commit(repo, pr, major_minor)
        if commit:
            merge_commits.append(commit)

    # Sort commits by date and output commands
    merge_commits.sort(key=lambda commit: commit.committed_datetime)
    print("Git cherry-pick commands to run:")
    for commit in merge_commits:
        print(f"git cherry-pick {commit.hexsha} #{commit.committed_datetime}")
```

This summary aims to preserve the essence of the original code while omitting minor details and non-essential information.


key: chroma:document
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**Function Summary:**

`clean_code(content: str) -> str`

Removes docstrings, empty lines, and comments from the provided code content to detect true changes.

**Implementation Highlights:**

* Uses triple quotes (`"\"\"\""`, `'\'\'\''`) to split out docstrings
* Removes empty lines by checking for non-whitespace characters
* Removes comments using regular expression substitution (`#.*$`)

**Notable Dependencies/Requirements:**

* None listed, but likely uses built-in Python modules such as `re` (regular expressions)


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**Function Summary: `keep_doc_examples_only`**

Removes non-documentation examples from provided code content, leaving only comments.

**Key Implementation Details:**

*   Splits input content by triple quotes (`"""`) to isolate documentation sections.
*   Trims leading and trailing whitespace from each line in the split content to eliminate unnecessary characters.
*   Removes commented-out lines by removing everything after a `#` sign using regular expressions.
*   Returns the cleaned code content as a single string.

**Notable Dependencies or Requirements:**

*   None found.

**Return Value Signature:**
```python
def keep_doc_examples_only(content: str) -> str:
```

**Main Functionality:**
The 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.


key: chroma:document
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**Summary**

The `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.

**Key Implementation Details**

* Returns the following types of contents:
	+ Directly under `tests`: folders with directories or files starting with `test_`.
	+ Under `tests/models`: model-specific folder names.
* Sorts and excludes certain items:
	+ Removes `.pyc` files from subfolders under `tests`.
	+ Excludes `models` subfolder from the main `tests` list.
	+ Removes the `sagemaker` package if found in the main test list.

**Notable Dependencies or Requirements**

* Uses `os.listdir` to interact with the file system.
* References `PATH_TO_REPO`, `PATH_TO_TESTS`, and `SAGEMAKER Package` by exact name.


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**Summary:**

`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.

**Key Implementation Details:**

* Compares old and new commit content using `clean_code()` function.
* Uses `git.Repo` to access working directory and checkout specific branching point.
* Opens files in same encoding for consistency.

**Return Value:**

Returns `bool` indicating whether the diff is docstring/comments only or not. 

**Notable Dependencies:**

* `git.Repo`
* `Path` from `pathlib`
* `clean_code()` function (exact implementation unknown)


key: chroma:document
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**_summary_**

`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.

**Key details:**

* **Functionality**: Exclusively checks for changes in code snippets versus documentation examples.
* **Implementation**:
	+ Retrieves file contents from working directory using `checkout_commit` and `open`.
	+ Applies `keep_doc_examples_only` function to clean up content by removing non-example sections.
	+ Returns `True` if differing example content is found, otherwise `False`.

**External dependencies:**

* `git.Repo`
* `Path`
* `keep_doc_examples_only` (assumed to be implemented elsewhere)

**Interfaces/Methods used:**

* `git.Repo.checkout_commit(branching_point)`
* `github.Path.open(filename, encoding="utf-8")`

Note that specific implementation details like the `keep_doc_examples_only` function are assumed to be implemented elsewhere in the codebase.


key: chroma:document
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**Summary:**

The `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.

**Key Functionality:**

* Compares `tiny_model_summary.json` contents across multiple commits
* Identifies changes to model classes and their defined modules
* Uses a temporary `_import_structure` module to resolve modeling file paths

**Implementation Details:**

* Utilizes Git repository and merge_base functionality
* Calculates differences between old and new JSON content using symmetric difference and intersection
* Handles common keys with differing content separately
* Resolves model class to module mapping using `_import_structure`
* Uses temporary directory and importlib utilities to load modules and get internal file paths

**Notable Dependencies:**

* `git` for repository access and merge_base functionality
* `json` and `re` libraries for JSON parsing and regular expression pattern matching
* `tempfile` and `importlib` libraries for creating temporary directories and loading modules


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**Summary:**

The `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.

**Main Purpose and Functionality:**

* Calculates Git diff between base commit and commit pairs
* Filters out changes only in docstrings or comments in modified files

**Key Implementation Details:**

* Iterates over commits and calculates diffs using `basecommit.diff`
* 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"]`)
* Uses `diff_is_docstring_only` function to check modifications are not only in docstrings or comments

**Notable Dependencies and Requirements:**

* `git.Repo`: A Git repository object
* `diff_is_docstring_only`: a separate function (not included in this snippet) that determines if changes are only in docstrings or comments


key: chroma:document
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Here's a concise summary of the provided code chunk:

**Function Summary:** `get_modified_python_files`

**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).

**Key Implementation Details:**

* Uses Git repository manipulation to determine branching commits (if not using last commit)
* Calculates differences in modified files with `get_diff(repo, repo.head.commit, [commit])` method
* Prints intermediate commit information for debugging purposes

**Notable Dependencies and Requirements:**

* Git library (`Repo`) for interacting with the repository
* `get_diff` function (not shown) for calculating file diffs


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**Summary:**

The `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.

**Key Details:**

*   **Function Signature:** `def get_diff_for_doctesting(repo: Repo, base_commit: str, commits: List[str]) -> List[str]`
*   **Primary Purpose:** Identify and return Python and Markdown files with differences between base commit and comparison commits.
*   **Implementation Details:**
    *   Iterates over each commit in the list of commits.
    *   Compares diff object of each commit against the base commit.
    *   Applies filtering on file types (python.py, md) to exclude irrelevant changes.
    *   Returns modified files containing doc examples using the `diff_contains_doc_examples` function.

**External Dependencies:**

*   `git.Repo`: Git repository class for retrieving information about commits.
*   `List[str]`: List of commit IDs for comparison.
*   `Repo` and other classes not mentioned here are assumed to be external dependencies.


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**Summary**

The `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.

*   **Purpose:** Returns a lista of paths by filtering python, markdown file from repository.
*   **Functionality:**
    *   Uses `glob` to find `.py` and `.md` files in `src/` or `docs/source/en/`.
    *   Excludes files in `utils/not_doctested.txt`, `src/transformers/models/deprecated`, and init files.
    *   Filters out files not marked for testing (`not_doctested.txt`).
*   **Key Implementation Details:**
    *   Uses list comprehensions to filter and transform file paths.
    *   Checks file names against exclusion lists for filtering.

**Dependencies/Requirements:**

*   `glob`
*   `os.path` (specifically, `relative_to`, `Partition`)
*   `pathlib`

**Notable Variables:**

*      `PATH_TO_REPO`: repository path
*      `_` not variable but alias

**Interfaces/Superclasses:**

None

This code snippet appears to be part of a larger class or module that handles file system operations and doctest filtering.


key: chroma:document
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**Concise Summary**

`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.

**Key Insights:**

* The function loads the old and new contents of "utils/not_doctested.txt" for each merge commit.
* It computes the removed lines by taking the difference between the two versions.
* Notable dependencies:
	+ `Path` from `pathlib`
	+ `checkout_commit` from GitPython
	+ `diff` from GitPython

**Return Value:** A list of strings representing the files that were removed from "utils/not_doctested.txt".


key: chroma:document
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**Function Summary**

`get_doctest_files(diff_with_last_commit: bool = False) -> List[str]`
-------------------------

This function generates a list of Python and Markdown files with modified doctest examples between the current head commit and either:

*   The main branch (if `diff_with_last_commit=False`) or
*   Its parent commits (otherwise)

The function performs the following steps:

1.  Checks if there are any modified doctest examples in the given commit range.
2.  Identifies added, renamed, or modified files containing doctest examples.
3.  Excludes slow documenting tests to be run on CircleCI.
4.  Ensures that no removed test files are included.

**Key Implementation Details**

*   Uses `Repo` class from [GitPython](https://gitpython.readthedocs.io/en/stable/) for Git repository management.
*   Utilizes `get_diff_for_doctesting` function to identify modified doctest examples.
*   Employs set operations and filtering for efficient list construction.

**Notable Dependencies or Requirements**

*   [GitPython](https://gitpython.readthedocs.io/en/stable/) library for Git repository interactions
*   External dependencies not specified in the provided code snippet.


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**Function Summary: `extract_imports`**

Extracts the imports made by a given module from its source code.

**Purpose**
The function takes a file name of a module and returns a list of tuples containing the imported module filenames and their corresponding import statements.

**Key Implementation Details**

1. **Relative Imports**: Extracts relative imports using regular expressions, filtering out docstrings and `tests_ignore` comments.
2. **Direct Imports**: Finds direct imports by parsing lines similar to `import * or from ... import`. The function ensures that only actual import statements are extracted.
3. **Cache Mechanism**: Stores previously computed results for module names to avoid redundant computations.

**Notable Dependencies and Requirements**

1. `os`: for interacting with the file system.
2. `re`: for regular expression matches.
3. `_re_single_line_relative_imports`, `_re_multi_line_relative_imports`, `_re_single_line_direct_imports`, and `_re_multi_line_direct_imports` (custom regexes).
4. `PATH_TO_REPO` (absolute path to the repository root).

**Primary Purpose**: Extracts imports from a module's source code, providing the necessary information for automated analysis or testing.

```python
def extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> List[Tuple[str, List[str]]]:
    # ...
```

Note 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.


key: chroma:document
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**Code Summary:**

### Functionality:

This function `get_module_dependencies` retrieves the list of module filenames for specified imports in a given Python module.

Given a `module_fname` and an optional cache (Dict[str, List[str]]), it refines the result of `extract_imports` to remove subfolders.

**Key Implementation Details:**

*   The function uses a while loop to recursively traverse imported modules (`while len(imported_modules) > 0:`)
*   It filters extracted imports based on submodule conditions: objects defined in submodules are added, not intermediate init file dependencies
*   Uses `os.path.join` for path concatenation and file checks

**Notable Dependencies or Requirements:**

*   `extract_imports`: a function to extract import statements from a module (not provided in the code snippet)
*   `PATH_TO_REPO`: a constant referring to the repository root directory
*   `os` module for file-related operations


key: chroma:document
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**Summary**

*   **Functionality**: Creates a list of reverse dependency relationships between modules and their impacting dependencies across the entire repository, excluding tests.

**Key Implementation Details**

*   Uses a cache to store module dependencies for efficiency.
*   Iterates over all module files in `/src` and `/tests` directories, extracting relative paths.

**Main Purpose and Functionality**

Creates 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. 

**Notable Dependencies or Requirements**

*   `PATH_TO_TRANFormERS` and `PATH_TO_TESTS`: Directory paths.
*   `get_module_dependencies()`: Function used to determine dependencies of a module, which is not shown in this snippet but presumably interacts with external libraries.

### Example Usage
```python
edges = create_reverse_dependency_tree()
print(edges)
```
This will output all the edges extracted from the project.


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**Concise Summary:**

`get_tree_starting_at` function constructs a tree data structure by scanning a module's edges.

**Main Purpose and Functionality:**
 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.

**Key Implementation Details:**

* Uses a breadth-first search (BFS) approach to traverse the edges.
* Checks for edge validity based on presence of `__init__.py` or self-looping edges.
* Maintains two lists: current visible edges (`new_edges`) and seen vertices (`vertices_seen`).

**Notable Dependencies and Requirements:**

* Python 3.x
* `List`, `Tuple`, and `Union` types from the standard library (not explicitly listed)
* Implicitly requires a list of edge tuples (`edges`) to be parsed as input.


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**Summary**

The `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).

**Key Implementation Details**

* The function uses a dynamic programming approach to build the tree structure by inserting edges into a list, ensuring proper indentation.
* It uses the `get_tree_starting_at` function to obtain the starting node of the subtree.

**Methods and Interfaces Used:**

* `create_reverse_dependency_tree`
* `get_tree_starting_at`

**Notable Dependencies:**

* None

**Primary Purpose**

Prints the tree structure of dependencies for a given module.


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**Summary:**

This function, `init_test_examples_dependencies`, maps test example files to potential test examples and vice versa for framework-specific test frameworks (Flax, PyTorch, TensorFlow).

*   **Purpose:** Initializes the dependency map for test examples, linking each example to its corresponding test file.

**Key Implementation Details:**

*   Iterates over three predefined framework libraries.
*   Reads contents of test files for Flax, PyTorch, and TensorFlow from specific directories within `PATH_TO_EXAMPLES`.
*   Mapped keys between example files and their relative paths as strings (`str()`) using relative path operations.

**Primary Purpose Output:**

`Tuple[Dict[str, List[str]], List[str]]`: A tuple containing the initialized dependency map (`test_example_deps`) and a list of all example files.


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**Function Summary: `create_reverse_dependency_map`**

**Purpose and Functionality:**
This function generates a dictionary mapping module/test filenames to their dependent modules/tests recursively, excluding direct imports of initialization files.

**Key Implementation Details:**

*   Initializes the dependency map by scraping all modules, tests, and example dependencies from specific directories.
*   Computes direct dependencies using `get_module_dependencies` with caching mechanism (`cache` dict).
*   Recursively updates dependencies until no more changes are detected.
*   Builds the reverse dependency map (`reverse_map`) based on the final computed dependencies.

**Notable Dependencies/Requirements:**

*   `init_test_examples_dependencies`: Initializes example dependencies and tests.
*   `get_module_dependencies(m, cache=cache)`: Returns direct dependencies of a module using caching mechanism (`cache` dict).
*   `collections.defaultdict(list)` is used to create the reverse dependency map.

**External Dependencies:**

*   PATH_TO_TRANFORMERS and PATH_TO_TESTS for scraping module and test files.
*   PATH_TO_REPO for getting relative paths to modules and tests.

The 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.


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**Function Summary: `create_module_to_test_map`**

Extracts and filters tests from the reverse_dependency_map, providing a dictionary mapping each file to executable tests.

**Primary Purpose:** Create a map of files to tests for execution, filtering out non-core model tests if specified.

**Key Implementation Details:**

* Uses `create_reverse_dependency_map` to default to a value if not provided.
* Applies filters based on the presence of "tests" or "examples/test" in file names and core models' modifications threshold.
* Includes tests within modules that have significantly impacted tests.

**Notable Dependencies/Requirements:**

* `create_reverse_dependency_map`
* `PATH_TO_TESTS`
* `IMPORTANT_MODELS`

**Return Value:** A dictionary with module-file-test mappings, filtering out non-core model tests if specified.


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**Function Summary:**

`_print_list(l) -> str`

Pretty prints a list of elements, joining lines with hyphens (-) and newline characters (`\n`).

**Key Implementation Details:**

* Uses `f-strings` to format each line
* Joins lines with `\n`
* Uses list comprehension to iterate over the input list `l`

**Notable Dependencies/Requirements:**

* None

**Interface:** Does not implement any external interfaces.


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Here is a concise and focused summary of the function:

**Function Name:** `infer_tests_to_run`

**Main Purpose:**
The main function determines which tests to run from the diff. It analyzes modified files, identifies impacted modules, and extracts test files.

**Key Implementation Details:**

* Retrieves a list of modified Python files using the `get_modified_python_files` function.
* Creates a reverse dependency map using the `create_reverse_dependency_map` function.
* Identifies impacted models by searching for files starting with "tests/models/" in the modified files.
* Extracts test files from directories like "tests/**/test_**.py" and "examples/**/*.py".
* Filters tests to exclude models if necessary.

**Notable Dependencies or Requirements:**

* The `PATH_TO_TESTS` variable is used to glob for test files.
* The `create_reverse_dependency_map` function depends on a reverse dependency map data structure.
* The `NUM_MODELS_TO_TRIGGER_FULL_CI` constant influences the number of models required to trigger full CI analysis.

**Output:** A list of files that need to be run in order to fetch all tests. This is printed to the console.

**Summary Code:**
```python
def infer_tests_to_run(
    output_file: str,
    diff_with_last_commit: bool = False,
    filter_models: bool = True,
    test_all: bool = False,
):
    modified_files = get_modified_python_files(diff_with_last_commit)
    if not test_all:
        modified_files = [str(k) for k in PATH_TO_TESTS.glob("*/*") if str(k).endswith(".py") and "test_" in str(k)]
    
    # ...
```


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**Function Summary: `filter_tests`**

_filters tests from an output file based on a list of folders._

### Function Purpose
 Reads the content of the output file, filters out specific tests in given folders and writes updated results back to the file.

### Key Implementation Details

* **File filtering**: Iterates through test files using `os.listdir()` and checks if folder paths are present in the `filters` list.
* **Tests reading/writing**: Uses `open()` with `"r"` and `"w"` modes to read and write content to the output file, respectively.

### Notable Dependencies

* `os`: for interacting with the operating system (e.g., working with files, directories)
* `pathlib` (not explicitly used but implied through `os.path.join()`)

### Interfaces/Methods Used

* `os.listdir()`
* `os.path.join()`

### External Dependencies
None mentioned.


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**Function Summary**

`parse_commit_message` parses a commit message to detect specific instructions for testing.

**Parameters**

* `commit_message`: The commit message to parse (string)
* Returns: A dictionary with keys `"skip"`, `"no_filter"`, and `"test_all"` indicating the presence or absence of corresponding tests.

**Return Value Dictionary**

| Key | Boolean Value |
| --- | --- |
| `skip` | Whether to skip tests (`True`/`False`) |
| `no_filter` | Whether to disable filtering for tests (`True`/`False`) |
| `test_all` | Whether to run all tests (`True`/`False`) |

**Implementation Details**

* Uses regular expression to search for a command in the commit message.
* Sets up conditional checks based on the presence of specific keywords.

**Notable Dependencies**

No external dependencies.


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**Function Summary: create_test_list_from_filter**

* **Purpose**: Creates a text list of files matching specific filter criteria and writes them to a designated output directory.

### Key Implementation Details:

- Utilizes `os.path.join` for file path construction
- Applies regular expression pattern `_filter` to a full test list to find matched files using `re.findall`
 
### Notable Dependencies/ Requirements:

- Uses `JOB_TO_TEST_FILE`, an external dictionary containing mapping between job names and test filters.
- Rely on `os` module's `makedirs` and file handling functions (`open`).


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**Summary**

The `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.

**Main Purpose and Functionality**
```python
class HubModelLister:
    def __init__(self, tags):
        # Retrieves models from the hub based on tags
        self.model_list = api.list_models(tags=tags)
```
The 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.

**Key Implementation Details**
- Fetches models from the hub using `api.list_models(tags=tags)`
- Uses try-except block to handle errors without crashing

**Notable Dependencies or Requirements**
- `api`: reference to the API module for list_models method
- External dependency: assumes the presence of an API that supports list_models functionality


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**Summary**

`_extract_commit_hash` extracts the commit hash from a list of commit messages.

**Key Functionality**

* Iterates through each commit message in the `commits` list
* Returns the commit hash when a message starts with "commit "
* Returns an empty string if no such message is found

**Implementation Details**

* Uses simple iteration and string manipulation to extract the commit hash
* Utilizes `str.startswith` and `str.split` methods

**Notable Dependencies**

* None explicitly mentioned, no external imports or module references.


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**Function Summary:**

`get_list_of_repo_model_paths(models_dir)`

*   Purpose: Returns a list of repository model paths by filtering out Flax, TensorFlow, deprecated, and auto-generated models.

**Key Implementation Details:**

*   Uses `glob` to retrieve all `.py` files in the `models_dir` with "modeling_" prefix.
*   Applies two passes of filtering:
    *   Removes Flax and TensorFlow models using list comprehensions.
    *   Forgets deprecated models by removing their symlinks from original list.

**Notable Dependencies or Requirements:**

*   `os.path.join` for path concatenation.
*   `glob` for file patterns matching.
*   List comprehensions for efficient filtering.


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**Function Summary**

`get_list_of_models_to_deprecate`

This function retrieves lists of models that have fewer than a specified threshold number of downloads over the past year.

**Primary Purpose**
Identify models with low download counts for potential deprecated model detection.

**Key Implementation Details**

1. Filter models based on download counts and date of first commit.
2. Extract commit hashes, first commit dates, and model paths from a repository's Git log.
3. Filter out new models added within the last year using a pre-defined threshold date.
4. Make successive calls to the HubModelLister for each model to collect download information.

**Notable Dependencies**

* `repo` (Git repository interaction)
* `HubModelLister` (model lister class)
* `datetime`
* `json`

**Inherited/Implemented Interfaces**
None apparent.

**External Dependencies**

* `models_info.json` file (for caching model info)


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**Function Summary: extract_warnings_from_single_artifact**

Extracts warnings from a downloaded artifact (.zip) based on specified targets.

**Main Purpose and Functionality**

This 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`.

**Implementation Details**

*   Uses a buffer to accumulate lines of code until a warning is complete.
*   Iterates through files in the artifact directory (if not using `from_gh`) or zipped file entries (if using `from_gh`).
*   Checks for warnings by searching for text patterns, such as "warnings summary (final)" and line endings.

**Notable Dependencies/Requirements**

*   `os`: For file system operations.
*   `zipfile`: For handling .zip files.
*   `logging`: Logger used to display warning messages.

**Key Algorithm/Pattern**

The 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.


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Here is a concise summary of the provided function code chunk:

**Function Name:** `extract_warnings`

**Purpose:**
Recurursively extracts warnings from file artifacts in a specified directory and its subdirectories.

**Main Functionality:**

* Iterates through files in the `artifact_dir` and filters those that end with ".zip" or follow the condition established by the `from_gh` variable.
* Calls a recursive helper function (`extract_warnings_from_single_artifact`) for each filtered file, passing the `targets` parameter.
* Updates an internal set `selected_warnings` with extracted warnings from all artifacts.

**Key Implementation Details:**

* Uses `os.path.join()` to construct paths to artifact files
* Utilizes recursion through `extract_warnings_from_single_artifact`
* Employs a set (`selected_warnings`) to store unique warnings

**Notable Dependencies and Requirements:**

* `os` module for path manipulation
* `targets` parameter passed to recursive helper function
* `from_gh` variable conditionally filtering ".zip" files


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**Summary**

A 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.

**Key Details:**

* Function signature: `get_indent(line: str) -> str`
* Regular expression used: `_re_indent` (exact name)
* Primary purpose: Extracts indentation from a line as a string.

**Notable Dependencies/Requirements:**

* `line`: input string
* `_re_indent`: regular expression object (imported or defined elsewhere)
* No external dependencies listed.


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**Function Summary: `split_code_in_indented_blocks`**

#### Main Purpose and Functionality
This function splits code into indented blocks. It takes in code, an indent level, and optional start and end prompts as input.

#### Key Implementation Details

* The function uses the `split()` function to split the code into lines.
* It iterates through each line, checking for the start of a new block based on the given indent level.
* The function uses a variable `current_block` to store lines being processed in a block.

#### Notable Methods and Interfaces

* `get_indent(lines[index])`: This method is used to calculate the indentation level at each line.

#### External Dependencies

* None explicitly mentioned, but it assumes standard Python environment with `Optional`, `List`, and `str` types.

#### Output
The function returns a list of strings, where each string represents an indented block of code.

Note: The exact method name for calculating the indentation level is not specified in the provided code.


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**Summary**

`ignore_underscore_and_lowercase` is a higher-order function that wraps another function `[Callable[[Any], str]]` to:

* Convert input strings to lowercase (`key(x).lower()`)
* Remove underscores from the output string (`replace("_", "")`)

**Implementation Details**

* `_inner` method signatures: `(x: Any) -> [str]`
* Interface used: `Callable[[Any], str]`

**Notable Dependencies/Requirements**

* None, but functions accepting any type (`Any`) and returning strings (`[str]`).


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**Function Summary:**

```python
sort_objects(objects: List[Any], key: Optional[Callable[[Any], str]] = None) -> List[Any]
```

**Purpose and Functionality:**

This 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.

**Key Implementation Details:**

* Extracts objects into three categories: `constants` (uppercase), `classes` (camel-case), and `functions` (lowercase)
* Uses the custom sorting key function (`key1`) to sort each group
* Returns the sorted list with the same elements as the input

**Notable Dependencies or Requirements:**

* None

**External Dependencies:**
None

This 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.


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**Summary**

This function, `sort_objects_in_import`, sorts objects within an import statement. It correctly handles complex cases with multiple lines of imports.

**Key Functionality**

1.  **Input**: A single-line or multi-line import statement containing variable names enclosed in square brackets (`[]`).
2.  **Output**: The same input but with object dependencies properly sorted and formatted.
3.  **Sorting Logic**:
    *   For multiple lines, sorts imports according to their alphanumeric keys within the brackets.
    *   For single lines, checks for bracket content using regular expression (`_re_bracket_content`) to determine if sorting is required.

**Notable Implementation Details**

1.  **Regular Expressions**: Utilize of `_re_replace`, `_re_bracket_content`, and other regular expressions for parsing import statements, handling brackets, and sorting keys.
2.  **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.
3.  **Import Sorting Logic**: Employ a custom `sort_objects` function (not shown in this snippet) to sort imported objects according to their keys.

**Dependencies**

*   No external dependencies other than Python's built-in library for regular expressions, which are used extensively in the provided code.


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**Function Summary: sort_imports**
================================

*   **Purpose:** Automatically sorts imports defined in the `_import_structure` of an init file.

    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.

*   **Key Algorithms/Patterns:**
    *   Splits code into indented blocks using a specific prompt.
    *   Identifies and splits internal import blocks by their indentation levels.
    *   Uses regular expressions to extract keys from import lines (direct or indirect).
    *   Sorts the extracted keys alphabetically.

*   **Notable Dependencies/Requirements:**
    *   Standard Python library functions for file operations and string manipulation (`open`, `read`, `split`).

**Key Method Signatures:**

*   `split_code_in_indented_blocks`: splits code into indented blocks.
*   `_re_direct_key` and `_re_indirect_key`: regular expressions for direct and indirect import key extraction.

**Interfaces/ Classes:**
    *   No explicit interface or class usage is mentioned in the provided function.


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**Summary:**

### Function Purpose and Functionality

The `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.

### Implementation Details

* Checks all `.py` files with an initialisation file marker (e.g., `__init__.py`) under `transformers/`.
* Tails all directories under `transformers/` recursively.
* Sorts imports using `sort_imports` for each affected initialization file.

### Notable Dependencies and Requirements

* `os.walk()`
* `PATH_TO_TRANSFORMERS`: Environment variable set to the root directory of transformers.

### Note

If sorting fixes issues, it will overwrite multiple files. Running `make style` is recommended.


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**Function Summary: `_find_text_in_file`**

This function searches for text within a specified `filename` based on two prompts. It finds the content between these two markers and returns it.

**Primary Purpose:** Find text in file, filtered by start and end prompts.

**Implementation Details:**

- Uses a loop to find the index of the first line starting with the `start_prompt`.
- Continues searching after that using another loop to identify the last line ending with the `end_prompt`.
- Ensures non-empty lines are included at the edges by incrementing/decrementing indices.
- Returns the content between the prompts, along with start and end indices.

**Notable Dependencies:**

- `open()` for file operations
- External dependencies not specified in provided snippet. However, the code assumes familiarity with external libraries and file system access.

**Interfaces/Methods Used:**

- File I/O (`open()`)
- String manipulation (`readlines(), join()`)


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**Function Summary: `camel_case_split`**

The `camel_case_split` function takes a camel-cased string as input and returns a list of words separated by capital letters.

**Key Implementation Details:**

* Uses regular expressions (regex) to split the input string using the pattern specified in the comments.
* The regex pattern `.+?(?=(?=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z]))|$` breaks down into:
	+ `.+?`: Matches one or more characters (except newline) non-greedy (`?`)
	+ `(?=[a-z]|(?=[A-Z])` and similar patterns: Looks for capital letters, ignoring whitespace

**Notable Dependencies and Requirements:**

* Python's built-in `re` module (regular expression library)
* None, as it only relies on the standard library.

This code snippet achieves its purpose by utilizing regex to split the input string into words based on capital letter transitions.


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**Summary**

The `_center_text` function centers a input `text` within a specified `width`, padding with spaces on the left and right. It optimizes padding for emojis (`✅` or `❌`) to avoid uneven indentation.

**Key Details:**

* Utilizes string multiplication (`" " *`) for efficient padding.
* Calculates padding using integer division (`//`) to align text center.
* Ignores emoji inputs by reducing text length by 2 characters.
* Returns a centered string with exactly specified `width`.

**Notable Dependencies/Requirements:**

* Supports strings (including emojis) as input `text`.
* Assumes explicit `int` type for `width`.


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**Summary:**

`get_model_table_from_auto_modules` function generates an up-to-date model table from the content of auto modules.

**Main Purpose and Functionality:**

* Generates a table with model names, PyTorch, TensorFlow, and Flax support information.
* Retrieves configuration mapping, models, and prefixes from `transformers_module`.
* Flags models for each backend (PT/TF/Flax) using regular expressions.
* Builds the table by iterating through generated model names, checking backend support, and formatting text.

**Notable Implementation Details:**

* Uses dictionaries to store configuration mapping, model names, and prefixes.
* Utilizes regular expressions to flag models for each backend.
* Uses `_center_text` function to format table cell texts.

**Notable Dependencies or Requirements:**

* `transformers_module` module (contains constants and functions).
* `_re_tf_models`, `_re_flax_models`, `_re_pt_models` regular expression patterns (defined in `transformers_module`).


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**Summary**

This 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.

**Key Implementation Details**

- This function uses `_find_text_in_file` to extract text ranges containing the model table from `index.md`. 
- It generates a new table using `get_model_table_from_auto_modules`.
- If the tables are different, it either overwrites the outdated table or raises an error with a suggestion to update via `make fix-copies`.

**Notable Dependencies/Requirements**

- `os` (file system operations)
- `PATH_TO_DOCS`, assumed to be a path constant defined elsewhere in the code.
- `_find_text_in_file` and `get_model_table_from_auto_modules` functions, assumed to be implemented elsewhere.


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**Summary:**

`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.

**Key Details:**

* Function signature: `def get_framework(test_class)`
* Primary purpose: Infer framework type from test class
* Algorithm:
	+ Search inheritance hierarchy of `test_class`
	+ Check for `ModelTesterMixin`, `TFModelTesterMixin`, or `FlaxModelTesterMixin` subclasses
	+ Return corresponding framework ID ("pt", "tf", or "flax")

**Dependencies/Requirements:**

* None


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**Function Summary: `get_mapping_for_task`**

**Purpose:** Returns the task-specific model mapping for a given task and framework, retrieving or caching the result.

**Key Implementation Details:**

* Retrieves cached result if available through `PIPELINE_TEST_MAPPING[task].get(framework, None)`.
* Uses frameworkspecific attributes (`model_mapping`, `tf_model_mapping`) from `pipeline_test_class` to construct the mapping.
* Caches the result in `PIPELINE_TEST_MAPPING[task][framework]`.

**Notable Dependencies and Requirements:**

* `pipeline_test_mapping`: a dictionary containing task-specific pipeline test data.
* `PIPELINE TestingMapping`: an external cache storage for mapped results.

**Method Signatures and Interfaces:**

* `get(framework) -> Mapping dict`


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**Summary:**

The `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:

*   Retrieving the framework related to `test_class` using `get_framework`.
*   Resolving the mapping for `task` based on the retrieved framework using `get_mapping_for_task`.
*   Identifying the sole configuration class from `test_class.all_model_classes` and retrieving the corresponding model architecture(s) using this class's configuration.

**Key Details:**

*   The function raises a `ValueError` if more than one configuration class is present in `test_class.all_model_classes`.
*   The model architecture may be returned as a single class, list, or tuple of classes, sorted by their names for consistency.

**Notable Dependencies/Requirements:**

*   `get_framework`
*   `get_mapping_for_task`
*   `test_class` (assuming it has methods like `all_model_classes`).
*   Import statements assume existing models are defined with a `config_class`.


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**Function Summary**

`get_pipeline_model_mapping` returns a dictionary mapping test classes to pipelines, where each pipeline is executed and mapped to its corresponding model. 

**Key Details**

* Iterates through `pipeline_test_mapping` using `test_class` as input.
* Applies `get_model_for_pipeline_test` function for each task.
* Filters out None values and sorts the results by task name.

**Return Value**

A dictionary with task names as keys and pipeline models as values. 

**Dependences/Requirements**

* `pipeline_test_mapping`: A list of tuples containing test class names and corresponding pipeline tasks.
* `get_model_for_pipeline_test(test_class, task)`: A function that returns a model for a given pipeline task.


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**Function Summary: `get_pipeline_model_mapping_string`**

**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.

**Key Implementation Details:**

* Uses the `get_framework` function to determine the framework type ("pt" or "torch") based on the input test class.
* Calls `get_pipeline_model_mapping` to retrieve the pipeline model mapping.
* Constructs a list of text strings (`"task": value`) representing each model class/class tuple pair in the mapping.
* Joins these strings into a single JSON-like string and formats it with conditional logic using the `is_{framework}_available()` function.

**Notable Dependencies and Requirements:**

* The `get_framework` function
* The `get_pipeline_model_mapping` function
* Conditional availability check using `is_{framework}_available()`
* Python dictionary-like formatting (`{key: value}`) for JSON-style strings.


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**Summary**

Validates if a given test class inherits from `unittest.TestCase` and is derived from one of the specified model tester mixins:
```python
def is_valid_test_class(test_class):
    """Restricts to `XXXModelTesterMixin` and should be a subclass of `unittest.TestCase`.
    
    Parameters:
        test_class (class): The class to validate.
    
    Returns:
        bool: True if valid, False otherwise.
    """
    # Constants
    base ClassNames = {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
    
    # Validation check
    def isValidBaseClass(base_class):
        return len(base_classNames.intersection([x.__name__ for x in base_class.__bases__])) > 0
    
    # Check if the test class is a subclass of TestCase and has one of the model tester mixins as an base class
    return not issubclass(test_class, unittest.TestCase) or isValidBaseClass(test_class)
```
**Key Details**

* `unittest.TestCase` class validation
* `ModelTesterMixin`, `TFModelTesterMixin`, and `FlaxModelTesterMixin` derived classes are expected

Note: 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.


key: chroma:document
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**Function Summary:**

`find_test_class(test_file)` finds and selects the test class from the `test_file` to which `pipeline_model_mapping` will be added.

**Key Implementation Details:**

* Iterates over valid test classes in `test_file`.
* Checks for presence of `pipeline_model_mapping` attribute; selects first matching class.
* Fallbacks to shortest class name heuristic if no match found.

**Notable Dependencies and Requirements:**

* `get_test_classes(test_file)`: returns list of test classes in `test_file`.
* `is_valid_test_class(x)`: checks validity of a test class.
* External dependency: `test_file` (required).


key: chroma:document
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**Summary:**

`find_block_ending`: Finds the index of the last line in a code block, ending with a matching bracket.

**Purpose:** The function locates the closing bracket of a block defined by indentation levels.

**Key Implementation Details:**

* Iterates through lines starting from `start_idx`.
* Checks for matching bracket using indentation level.
* Returns the index of the block's end (`end_idx`).

**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".


key: chroma:document
string_value:
Here is a concise summary of the provided function code chunk:

**Function Purpose:**
The `add_pipeline_model_mapping` function adds or ovewrites the `pipeline_model_mapping` attribute to a given test class (`test_class`).

**Key Implementation Details:**

1.  The function checks if `test_class` already has a defined `pipeline_model_mapping` and only overwrites it if set to `False`.
2.  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.
3.  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`.
4.  It ensures that the modified test class name is appended after its parent classes using the `"unittest.TestCase"`
5.  After adding or overwriting `pipeline_model_mapping`, it removes the lines marked to be removed.

**Dependencies and Requirements:**

1.  Uses the `inspect` module for code inspection.
2.  References related classes/functions by exact name, such as getting class lines using its sourcelines method.
3.  Requires external dependencies like regular expression (`re`) library.

**Return Value:** The added or overwritten line of new `pipeline_model_mapping`.


key: chroma:document
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**Summary:**

`add_pipeline_model_mapping_to_test_file`: Adds the `pipeline_model_mapping` configuration to a `test_file`, overwriting if specified.

**Key Details:**

- Calls `find_test_class` with `test_file` as input.
- If a test class is found, it calls `add_pipeline_model_mapping` with the class and an optional overwrite flag.
- The primary purpose of using `overwrite=False` in `add_pipeline_model_mapping` if `test_class` exists.

 Relevant methods: 
 - `find_test_class(test_file)`
 - `add_pipeline_model_mapping(test_class, overwrite)`

Note:
- External dependency: `find_test_class` and `add_pipeline_model_mapping` functions are assumed to be defined elsewhere.


key: chroma:document
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**Code Summary**

```
get_processor_types_from_config_class(config_class, allowed_mappings=None)
-------------------------

Purpose:
Extracts and returns a tuple of supported processors or tokenizer types based on the provided config class.

Functionality:

* Takes a config class and an optional list of allowed mappings as input.
* Checks if the config class has a direct mapping in PROCESSOR_MAPPING, then uses that.
* If not, checks for indirect mappings through TOKENIZER_MAPPING, IMAGE_PROCESSOR_MAPPING, or FEATURE_EXTRACTOR_MAPPING.
* Removes None values from the resulting processor types tuple.

Key Implementation Details:

* Uses `_to_tuple` function to ensure uniform return type (sequence or scalar).
* Checks for specific allowed mappings and their corresponding mappings in PROCESSOR_MAPPING and other alias dictionaries.
* Uses tuple concatenation to combine multiple processor types into a single tuple.

Dependencies/Requirements:

* PROCESSOR_MAPPING, TOKENIZER_MAPPING, IMAGE_PROCESSOR_MAPPING, FEATURE_EXTRACTOR_MAPPING
* collections.abc.Sequence (for isinstance check)
```

Note: The code summary is written in a concise and focused manner, highlighting the main purpose, functionality, key implementation details, and dependencies.


key: chroma:document
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**Code Summary:**

### Purpose and Functionality

`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.

### Key Implementation Details

*   Uses a set to avoid duplications across multiple mapping sources.
*   Filters out models based on a boolean `models_to_skip`.

### Notable Dependencies or Requirements

*   `arch_mappings`: dictionary of configuration classes mapped to architectures
*   `models_to_skip`: optional list of excluded model names (defaults to empty set)
*   `UNCONVERTIBLE_MODEL_ARCHITECTURES`: pre-defined set of inconvertible architecture models


key: chroma:document
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**Summary:**

This 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.

**Key Implementation Details:**

* Replaced the original prefix with a shortened version (e.g., "Tokenizer" -> "")
* Handles specific cases for Wav2Vec2CTC and other models
* Uses the `getattr` function to dynamically retrieve the config class from the `transformers_module`

**Notable Dependencies and Requirements:**

* `transformers_module`: assumed to be a module with config classes registered under their name (e.g., `Wav2Vec2Config`, `GPT2Tokenizer`)
* Tokenizer/feature extractor dependencies specific to each model are not explicitly handled; may require additional configuration or file presence checks.


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**Function Summary: `build_processor`**

**Purpose:** Create a processor for the specified `processor_class` using a configuration class from `config_class`.

**Main Functionality:**

1. Use `get_checkpoint_from_config_class` to find a checkpoint in the source file of `config_class`.
2. If no checkpoint is found, attempt to get one from `processore_class`.
3. Try to create an instance of `processor_class` from the checkpoint.
4. If creation fails, use recursive approach to try alternative configurations.

**Key Implementation Details:**

*   Uses `get_config_class_from_processor_class` to map `processor_class` to a configuration class.
*   Tries to get a new processor class using AutoMapper or by extracting tokenizer classes from the config file.
*   Creates an image processor or feature extractor without a checkpoint if `allow_no_checkpoint` is set.

**Notable Dependencies and Requirements:**

*   Uses `transformers_module` for loading processor classes.
*   References related classes/functions like `get_config_class_from_processor_class`, `pretrained`, `logger`.
*   Assumes the existence of certain configuration files (e.g., `processorer_mapping_names`).


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**Summary:**

`get_tiny_config` function retrieves a tiny configuration from `config_class` using each model's `ModelTester`. 

**Main Purpose and Functionality:**

* The function takes a `config_class` (subclass of `PreTrainedConfig`) as input.
* It retrieves the corresponding `model_tester_class` from the testing module based on the `model_type`.
* It creates an instance of the `model_tester_class` with updated arguments to set `vocab_size`.
* It creates a configuration using the model tester's `get_pipeline_config`, `prepare_config_and_inputs`, or `get_config` method.
* If necessary, it sets max positions in the configuration.

**Key Implementation Details:**

* Uses dynamic imports and path manipulation to retrieve the testing module based on `model_type`.
* Sets vocab size for models with a language tokenizer.
* Uses various methods from `model_tester_class` (e.g., `get_pipeline_config`, `prepare_config_and_inputs`) to create the configuration.

**Notable Dependencies or Requirements:**

* `config_class` (subclass of `PreTrainedConfig`)
* `model_type` (determined by `config_class.model_type`)
* Various model testers and corresponding modules
* Testing framework (e.g., `unittest`)


key: chroma:document
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**Concise Summary**

This function creates or updates a `LayoutLMv3TokenizerFast` instance using the provided `PreTrainedTokenizerFast`. It:

* Trains a new tokenizer from the training data with a specified vocabulary size (`TARGET_VOCAB_SIZE`)
* Checks if the created tokenizer is of type `LayoutLMv3TokenizerFast`, and if not, uses the testing data to create an alternative tokenizer
* Returns the newly created or updated `LayoutLMv3TokenizerFast` instance.

**Details:**

* Main purpose: tokenization for a specific text dataset using `LayoutLMv3TokenizerFast`.
* Key implementation details:
	+ Tokenizer training and update.
	+ Type checking for `LayoutLMv3TokenizerFast`.
* Notable dependencies:
	+ `PreTrainedTokenizerFast` from Hugging Face library.
	+ `data["training_ds"]["text"]` and `data["testing_ds"]["text"]` datasets.


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**Function Summary: Convert Feature Extractor**

Converts a `feature_extractor` instance to a new configuration based on `tiny_config`.

**Main Purpose and Functionality:**
The 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.

**Key Implementation Details:**

* Updates `kwargs["size"]` and `kwargs["crop_size"]` based on `image_size` or `vision_config.image_size`
* Sets `feature_size` and `num_mel_bins` for `Speech2TextModel`
* Checks for large image sizes in BaseImageProcessor instances (up to 64 pixels)
* Raises a ValueError if an incompatible model type is used with too large an image size

**Notable Dependencies/Requirements:**

* `BaseImageProcessor`, `tiny_config`, and related classes/functions are imported but not listed explicitly
* External dependencies on ModelTester, get_config(), ImageSize, VisionConfig are implied but not specified


key: chroma:document
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Here's a concise summary:

The code creates and saves a model with modified tokenizers (fast and slow) when using the Hugging Face library. It performs the following tasks:

1. Checks available tokenizers on the Hugging Face Hub.
2. Converts fast and slow tokenizers to smaller versions based on a provided `tiny_config`.
3. Verifies that the converted tokenizers correspond to each other.
4. Saves modified feature extractors (if applicable) using the tiny config.
5. Calls back the modified processors to ensure they can be saved in their updated form.

The code maintains the functionality of original tokenizers and saves only modified versions, if conversions are successful.


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**Summary**

The `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`. 

**Key Details:**

* Function purpose: Returns a framework-agnostic checkpoint directory.
* Implementation:
	+ Removes framework prefix from model architecture (`arch_name`).
	+ Uses `os.path.join` to combine filename and directory.

**Dependency:**

* Imports an externally-used module: `os`.
* Assumes the existence of a string object containing model architecture information.
* Relies on string manipulation for framework-agnostic namingConvention.


key: chroma:document
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Here is a concise and focused code summary:

**Functionality:**

* Creates and saves a model for the specified `model_arch` using a custom configuration (`tiny_config`) and output directory (`output_dir`)
* Copies processor files to the same folder as the checkpoint directory if it exists
* Saves and loads the pre-trained model with the specified `checkpoint_dir`

**Key Implementation Details:**

* Uses `shutil.copytree` to copy processors, allowing for reuse across models of the same type
* Applies settings from `tiny_config` based on the model architecture name (`model_arch.__name__`)

**Notable Dependencies and Requirements:**

* Hugged library (not explicitly listed, but implied by usage)
* os library (for file system operations)
* shutil library (used for copying files)


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**Summary:**

`fill_result_with_error` function:
```python
def fill_result_with_error(result, error, trace, models_to_create):
    """
    Fill `result` with errors for various framework models and processor.

    Parameters:
    result (dict): Target dictionary to be filled with errors.
    error (tuple): Error message and traceback.
    trace (traceback object): Debugging information.
    models_to_create (dict): Models to create for each framework.
    """
```

**Functionality:**

1. Fills `result` dictionary with errors for all target model architectures (model_arch in `models_to_create`) if the processor cannot be built.
2. Initializes processor configuration with only class names for each component.

**Key Implementation Details:**

* Iterates through `FRAMEWORKS` and updates corresponding framework models in `result`.
* Uses nested dictionaries to store model configurations for each architecture.

**Notable Dependencies or Requirements:**

* `FRAMEWORKS` (list of strings)
* `models_to_create` (dictionary with framework models)
* `traceback` object (`trace`)
* Processor components with their class names

This summary aims to provide a concise overview of the function's purpose, key implementation details, and any notable dependencies or requirements.


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**Summary:**

This function, `upload_model`, uploads a small model library (`model_dir`) to a remote repository on Hub. The primary purpose is to:

1. Create or clone an existing repository in Hub (`create_repo` method) if it doesn't exist or already exists and needs to be updated.
2. Import the models from the local directory and update the repository with the latest changes.

**Key Implementation Details:**

* Creates a temporary directory and clones the remote repository using `Repository.git_pull()`.
* Copies the model directory into the temporary directory.
* If the repository exists, opens a Pull Request (PR) on Hub (`upload_folder` method).
* If not, directly pushes the updated models to the Hub repository.

**Notable Dependencies or Requirements:**

* `Hub` API (Remote Repository creation/cloning and PR creation)
* `Repository` class for interacting with the remote repository
* `TemporaryDirectory` from `tempfile` module for creating a temporary directory
* `shutil` for copying files and directories
* `os.path.sep` for splitting the model directory path

**Method Signatures and Interfaces:**

* `upload_model(model_dir, organization, token)`
* `create_repo(repo_id, exist_ok=False, repo_type="model", token=token)`
* `Repository(local_dir=tmpdir, clone_from=repo_id, token=token)`
* `upload_folder(folder_path=model_dir, repo_id=repo_id, repo_type="model", ...)`
* `logger.warning` and similar logging functions for output


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**Summary**

The `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.

**Key Implementation Details:**

*   The function relies on the Hugging Face Transformers library.
*   It handles three types of composite models:
    *   Encoder-decoder (`config_class.model_type == "encoder-decoder"`): Uses PyTorch.
    *   Vision-encoder-decoder (`config_class.model_type == "vision-encoder-decoder"`): Uses PyTorch.
    *   Speech-encoder-decoder (`config_class.model_type == "speech-encoder-decoder"`): Supports both PyTorch and TensorFlow.
    *   Vision-text-dual-encoder (`config_class.model_type == "vision-text-dual-encoder"`): Only uses PyTorch.
*   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.

**Notable Dependencies:**

*   The `transformers` library from Hugging Face.
*   Temporary directories for storing pre-built models.
*   External functions and classes (e.g., `build`, `os.path.join`, `shutil.copytree`) from Python's standard library or other dependencies not explicitly mentioned here.

**Model Output:**

The function generates a directory with a composite model, along with various output files, including:
        *   Model checkpoint
        *   PyTorch models and checkpoints (when supported)
        *   TensorFlow models and checkpoints (if specified)
        *   PyTorch processors

**Interfaces:**

*   `build`: An external function for building pre-trained models.
*   `TransformerModel` interface from the Hugging Face Transformers library.

```python
def build_composite_models(config_class, output_dir):
    # ... [function implementation]
    
    return result
```

This concise summary should provide essential information for developers to understand this code snippet and replicate its functionality in their own projects.


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**Summary**

`get_token_id_from_tokenizer` function retrieves the ID of a specific token from a `tokenizer` object. It:

* Takes in `token_id_name` (string) and `original_token_id` (integer)
* Initializes `token_id` to `original_token_id`
* Raises ValueError if `token_id_name` does not end with `_token_id`
* Uses `tokenizer` to retrieve the token ID, optionally converting it with added vocabulary

**Key Implementation Details**

* Replaces `_token` in `token_id_name` with `_token` from `tokenizer` for attribute access
* Checks if `tokenizer` is a `PreTrainedTokenizerFast` and applies conversion using its `_convert_token_to_id_with_added_voc` method

**Notable Dependencies or Requirements**

* External dependencies:
	+ `transformers` library (specifically, `PreTrainedTokenizerFast`)
* Related classes/functions:
	+ ` tokenizer._convert_token_to_id`
	+ `tokenizer._convert_token_to_id_with_added_voc`


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**Function Summary: `get_configOverrides`**

**Purpose:** Returns configuration overrides for a given `config_class` and list of `processors`, specifically designed for Hugging Face models.

**Key Implementation Details:**

* Checks if a specific tokenizer is present in the `processors` list and returns overrides accordingly.
* Extracts properties from the chosen tokenizer, including vocabulary size, special token ids, and decoder configuration.
* Generates model tester keyword arguments based on the chosen tokenizer's vocabulary size and decoder attributes.
* Iterates over special token id attributes and collects their values using the tokenizer or `_tiny_config` object.

**Notable Dependencies and Requirements:**

* `PreTrainedTokenizerFast` and `PreTrainedTokenizer`
* External dependencies:
	+ `FSMTModelTester`
	+ `configuration_fsmt`
	+ `DebertaV2Tokenizer`
* Inherited/Implemented interfaces:
	+ `config_class`: uses specific classes from the Hugging Face library.
	+ `configuration_fsmt`: used to create a decoder configuration for FSMTConfig models.


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**Summary**

This 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.

**Main Functionality**

1. Creates processors using `build_processor` with the given `processor_classes`.
2. Converts processors by reducing vocabulary size and image size.
3. Get tiny config using `get_tiny_config`.
4. Build and save PyTorch models using `build_model` for each PyTorch arch.
5. Build and save TensorFlow models using `from_pretrained` and `save_pretrained` methods for each TensorFlow arch.

**Output**

The function returns a dictionary containing the following:
- `processor`: Model types and their corresponding names
- `pytorch`: PyTorch models and their checkpoints
- `tensorflow`: TensorFlow models and their checkpoints

**Key Functions**

1. `build_processor`
2. `convert_processors`
3. `get_tiny_config`
4. `build_model` (PyTorch)
5. `from_pretrained` (TensorFlow)

Note: 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.


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**Function Summary:**

`build_tiny_model_summary`

Builds a dictionary of model architecture summaries.

**Purpose and Functionality:**
The 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.

**Key Implementation Details:**

* Iteration over configurations in `results`
* Extractions of tokenizer classes (`x.endswith("TokenizerFast") or x.endswith("Tokenizer")`)
* Extractions of processor classes (`not in tokenizer_classes`)
* Iteration over frameworks (TF/HT/TF2) and architecture names
* Model class updates for each architecture name

**Notable Dependencies and Requirements:**

* `hf_api` library for accessing repository information
* Various external configurations stored in `results`
* Optional `organization` parameter with repository ID generation
* Token (`token`) used for authentication


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**Function Summary:**
`build_failed_report(results, include_warning=True)` generates a dictionary containing detailed error reports for each configuration in the `results` input.

**Purpose and Functionality:**

* Iterates through configurations with error reports, framework-specific arch-names, and warnings (if requested).
* Identifies and extracts applicable error messages from "error" or "warnings" sub-dictionary.
* Creates a nested dictionary structure with failed results, including configuration name, framework, architecture name, and corresponding error message.

**Key Implementation Details:**

* Iterates through `FRAMEWORKS` list to process arch-names under each framework.
* Uses conditional statements to navigate the complex data structure of "error" and/or "warnings" sub-dictionary.

**Notable Dependencies or Requirements:**

* `FRAMEWORKS` namespace (list of supported frameworks)
* Access to dictionary keys `"error"` and/or `"warnings"` within specific configurations.

**Method Signatures and Interfaces:**

* takes two inputs:
	+ `results`: input dictionary containing configuration-specific data.
	+ `include_warning`: optional boolean flag (default: True).
	+ returns: a nested dictionary `failed_results` containing error reports for each configuration.


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**Summary**

The `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.

**Key Implementation Details**

* Iterates through three loops to gather data:
	+ Outer loop: Configuration names (`config_name`)
	+ Middle loop: Frameworks (`framework` from `FRAMEWORKS`)
	+ Inner loop: Architecture names (`arch_name`) within a given framework
* Checks for "error" in the result, and formats either an OK or failed message
* Formats and concatenates results into two separate strings using `text` and `failed_text`

**Notable Dependencies/Requirements**

* `FRAMEWORKS`: An iterable of list-like objects representing frameworks to report on
* Related classes/functions not shown in this snippet


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**Concise Summary:**

The `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.

* **Primary Purpose:** Merge data from two sources into a unified summary.
* **Key Implementation Details:**
	+ Iterates over duplicate key-value pairs, updating the lower priority value with the higher priority one.
	+ Appends values from lists while preserving duplicates, except for the SHA hash attribute.
	+ Sorts and deduplicates list values, excluding the SHA hash field.
* **Notable Dependencies/Requirements:**
	+ `json` module for parsing file contents
	+ `os` module for path manipulation
	+ Access to files "tiny_model_summary.json" and "updated_tiny_model_summary.json".

**RAG System Notes:**


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**Function Summary**

The `create_tiny_models` function creates and builds models for various transformer architectures. Its primary purpose is to generate reports on the creation process.

**Key Functionality:**

1.  Checks if the current directory is the root of the cloned 'transformers' repository.
2.  Creates a "reports" directory in the output path and sets it up with proper permissions.
3.  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'.
4.  If num_workers is greater than 1, uses multiprocessing pool with star map function to build models in parallel.
5.  Builds the tiny model summary file based on results.

**Implementation Details:**

-   Uses `os.path` module to work with paths.
-   Utilizes the dir() function of objects to get specific architectures like pytorch and tensorflow models from transformers_module.

**Notable Dependencies or Requirements:**

1.  The functions builds on top of the os, multiprocessing and json modules as shown below:
    -   `os`: used for working with file paths.
    -   `multiprocessing.Pool` : used to build models in parallel.
    -   `json`: used to dump results in JSON format.

2.  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.

3.  The organization and token are requirements used during upload process.
4.  It uses a custom function build_tiny_model_summary where model type, tokenizer_classes, processor_classes are passed as arguments.


key: chroma:document
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**Concise Summary**

The `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.

**Key Features:**

* The function constructs a directed graph from the dependencies.
* It uses a depth-first search algorithm to find nodes with no outgoing edges (leaf nodes), which are then added to a sorting list.
* The process repeats until all nodes have been visited, resulting in a sorted list of model names corresponding to the original input dictionary keys.

**Notes:**

* External dependencies include `graph` and `name_mapping`, which represent temporary data structures used during the sorting process.
* No external libraries are required beyond Python's built-in `dict` and set operations.


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**Concise Summary**

The `extract_classes_and_imports` function extracts classes and imports from a Python file. It:

* Parses the file into an Abstract Syntax Tree (AST)
*walks through the AST to identify import nodes containing ".modeling_" or "transformers.models" modules
*Returns a set of unique imported module paths.

**Key Implementation Details**

The key implementation details are:

* The use of `ast.Import` and `ast.ImportFrom` node types to identify imports.
* The use of `filename` parameter in `ast.parse()` to preserve the file path.
* The `walk()` function to traverse the AST tree.

**Notable Dependencies or Requirements**

The function relies on the following dependencies:

* Python's built-in `ast` module (Abstract Syntax Tree).
* No external libraries are required.


key: chroma:document
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**Summary**

 Maps dependencies between Python file locations. 

**Key Implementation Details**

* Merges `extract_classes_and_imports` function call into a single loop, iterating over each file in `py_files`
* Utilizes a `defaultdict(set)` to efficiently store and manage dependencies for each file
* Returns a dictionary mapping file paths to sets of imported modules

**Notable Dependencies or Requirements**

* None explicitly mentioned; no external dependencies are referenced. 

(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.)


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**Summary:**

This 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.

**Key Details:**

* Maps class dependencies using `map_dependencies(py_files)`
* Performs topological sort on dependency graph using `topological_sort(dependencies)`
* Returns ordered list of classes

**Notable Dependencies/Requirements:**

* 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.


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**Summary:**

`find_indent(line: str) -> int`: Returns the number of spaces at the start of a line, used for indentation calculation.

**Key Details:**

* Uses `re.search` to search for a pattern of one or more non-breaking spaces (`\s+`) at the beginning of the line (`^`).
* If no match is found, returns 0.
* Returns the length (i.e., number of spaces) in the matched group.

**Example Usage:**


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**Summary:**

This function `stringify_default` converts its input `default` value into a string representation. It handles various data types:

*   Boolean (`True/False`) and integers as is
*   Enums using their representation as a string
*   Floats with precise 2-decimal places formatting (unless more than 6 characters long)
*   Strings with numeric values or enclosed in backticks

**Key implementation details:**

| Implementation | Description |
| --- | --- |
| `bool` and `int` conversions | Directly return as strings |
| `enum.Enum` conversion | Use `str()` to convert to string, preserving enum value |
| Float formatting | Use `round()` with 2 decimal places for strings longer than 6 characters |
| String handling | Separate numeric strings from non-numeric strings and apply formatting accordingly |

**Noteable dependencies:**

*   `enum`: Used to handle Enums
*   `str()`, `round()`: Implemented in Python's built-in functions

This summary provides a concise overview of the function's primary functionality, key implementation details, and notable dependencies.


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**Summary**

This 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.

**Key Implementation Details**

* Utilizes the `eval` function from Python's standard library to evaluate the expression.
* Parses the expression using `ast.parse` with the `mode="eval"` argument.
* Calls the `eval_node` function (not shown in this code snippet) on the parsed AST.

**Notable Dependencies and Requirements**

* Python's standard library, specifically the `ast` module for parsing.
* The `eval_node` function is not part of the standard library and is assumed to be implemented separately.


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**Eval Node Function Summary**

### Purpose and Functionality

The `eval_node` function is a recursive traverser of abstract syntax tree (AST) nodes, evaluating arithmetic expressions by applying mathematical operators to their operands.

### Key Implementation Details

* Evaluates integer literal nodes (`ast.Num`) as-is.
* Unifies binary operation nodes (`ast.BinOp`): recursively evaluates left and right operands with operator-dependent functions from `MATH_OPERATORS`.
* Unifies unary operation nodes (`ast.UnaryOp`): recursively evaluates the operand with an operator-dependent function from `MATH_OPERATORS`.
* Raises a `TypeError` for unsupported node types.

### Dependencies

None, as this implementation does not rely on external dependencies. However, it utilizes:

* `ast` (Abstract Syntax Tree) module for parsing expressions.
* `MATH Operators` dictionary (not shown in provided code), which maps operator types to evaluation functions.
* No external libraries besides the Python standard library.


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**Summary:**

Function `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.

**Key Features:**

* Replaces occurrences of "optional" or "**optional**" with `OPTIONAL_KEYWORD`.
* Handles cases where the default value is `None`, `empty string` (e.g., empty line), or a complex math expression.
* For numbers, evaluates the math expression if it matches the default value and converts it to the correct type.

**Important Variables and Functions:**

* `OPTIONAL_KEYWORD`: "optional" or "**optional**"
* `description`: input docstring
* `default`: default value to be included in the updated description

**Notable Dependencies:**

* `inspect` for checking `default` value
* `re` for regular expression matching and replacing
* `eval_math_expression` function not provided in the code snippet, but used for evaluating math expressions

**Return Value:**

The updated docstring with the default value included.


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**get_default_description function summary**

```markdown
Provides default description for an uncovered argument in Python function signatures.

### Key implementation details:

* Uses `inspect.Parameter` and related types (`inspect._empty`) to determine parameter type and defaults.
* Employs string formatting to construct the final default description.

### Notable dependencies and interfaces:

* `inspect`: Imported module providing type inspection utilities.
* `optional`: Likely an external library (e.g., `pydantic`) for optional parameter handling, referenced by `OPTIONAL_KEYWORD`.
```

### Primary purpose and functionality
Builds a default description string for an uncovered argument in a Python function signature, considering its type and potential default value.


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**Summary:**

Finds the source file of an object given its `__module__` attribute.

*   **Purpose:** Returns the path to the source file associated with the input object's module.
*   **Implementation Details:**
    *   Uses the `__module__` attribute of the input object to determine the module name.
    *   Joins the module name with a prefix (`PATH_TO_TRANSFORMERS`) to form the initial directory path.
    *   Iterates through the parts of the module name (skipping the first part, which is the package name), appending each part as a directory.
        *   `module.split(".")[1:]`: Extracts the namespace from the module name after removing the package prefix ('__main__.').
    *   Returns the final path with a `.py` suffix using `Path.with_suffix()`.
*   **Dependencies:** None explicitly mentioned; however, it relies on the `PATH_TO_TRANSFORMERS` constant and possibly `Path`.

**Key Variables/Functions:**

*   `obj`: Input object with its `__module__` attribute.
*   `module`: The namespace of the input object's module name (e.g., 'transformers.modeling.tokenization').
*   `PARTS OF THE MODULE NAME`: An array consisting of module parts excluding the package index prefix (`__main__.`).


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**Function Summary:**
### match_docstring_with_signature(obj)
 
  **Purpose:** Match the docstring of an object with its signature.

  **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.

  **Key Implementation Details:**
- Uses regular expressions (`re`) for pattern matching.
- Leverages `find_indent` (function not shown in this snippet) to parse indentation levels.
- Utilizes `_pre_parse_arg` and `_parse_arg` regular expression patterns (not directly visible here).

  **Dependencies/Requirements:**
- Import of `inspect`, `re`, and access to `type Any`, `Optional`, `Tuple`, and `inspect._ParameterKind`.
 - The function utilizes type hints for clarity (`Any`, `Inspect.Signature`).
  
  **API Interface:** 
    It accepts an object, returns a tuple containing two strings:
     - The first string contains the original documentation of the arguments described in the docstring.
     - The second string contains the matched and generated documentation for the same argument parameters based on their signature.

**Additional Notes:**
The 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.


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**Function Summary: `fix_docstring` Function**

### Purpose and Functionality

The `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.

### Key Implementation Details

1.  **Inspecting Docstrings**:

    *   Reads source code from a given object.
    *   Identifies the lines where arguments are defined and replaced.
2.  **Matching Docstring with Signature**:

    *   Uses `match_docstring_with_signature` to determine the updated documentation based on the signature.

### Notable Dependencies

1  **External Dependency**:

    *   `re` for regular expression matching
2  **Inherited/Implemented Interfaces** (None mentioned in this function)

The 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.


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**Summary**

The `check_docstrings` function checks docstrings of public, callable objects in the `transformers` module and its dependencies. It:

1. **Analyze Documentation**: Checks if existing docs match defined signatures.

2. **Fix Potential Issues**: Fixes inconsistencies or warnings reported by the tool.

3. **Report Potential Bugs**: Prints error messages about unmatched signatures, incomplete methods, and potential bugs.

**Key Functionality**

*   `check_all`: Optional flag to check all files.
*   `overwrite`: Optional flag to fix inconsistencies instead of logging failures.

**Implementation Details**

*   Verifies functions match expected signature patterns.
*   Skips private objects or those without docstrings.
*   Raises `ValueError` with error messages when reports detection issues.


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**Summary:**

`download_and_extract` function downloads and extracts a zip file for a given task, extracting the contents to a specified data directory.

**Implementation Details:**

* Downloads from `taskId`
* Extracts contents using `zipfile.ZipFile.extractall()`
* Deletes temp zip file after extraction (`os.remove(data_file)`)

**Notable Dependencies:**

* `urllib.request.urlretrieve()` for downloading
* `zipfile` module for extracting zip file
* `os` module for deleting temporary files

**Primary Class/Function Names:**

* `task`
* `data_dir`
* `TASK2PATH[task]`: mapping of tasks to download paths


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**Function Summary: `format_mrpc`**

**Main Purpose and Functionality**
The `format_mrpc` function processes the MRPC (Microsoft Research Paraphrase Corpus) dataset by:

* Creating a directory for the dataset if it does not exist
* Downloading dataset files from a specified URL or using local data if specified
* Validating the presence of required files
* Preprocessing data into training, development, and testing sets

**Key Implementation Details**

* Uses `os.path.join` to construct file paths
* Utilizes `urllib.request.urlretrieve` to download external datasets
* Reads and writes data from/to files using `open` function with specified encoding
* Splits input file rows into individual labels, IDs, and strings

**Notable Dependencies and Requirements**

* Uses `os` module for file system operations
* Utilizes `urllib.request` module for downloading external datasets
* References classes and functions by exact name:
	+ `MRPC_TRAIN`
	+ `TASK2PATH["MRPC"]`

**External Dependencies:**

* `urllib.request`
* `os`
* Python's built-in file I/O operations


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**Summary:**

The `download_diagnostic` function downloads and extracts diagnostic data from a specified directory. It:

* Creates the 'diagnostic' subdirectory if it does not exist
* Downloads the diagnostic file using `urlretrieve` from the `TASK2PATH["diagnostic"]` location
* Prints confirmation messages after completion

**Key details:**

* Uses `os.path.join` to construct paths for directory and file creation/export
* Implements `urllib.request.urlretrieve` for file download
* Returns no value (indicating a side-effecting function)

**Notable dependencies/requirements:**

* `os.path`
* `urllib.request`
* `TASK2PATH` (dictionary containing path to diagnostic data)


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**Function Summary: `get_tasks`**

The `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.

**Key Implementation Details:**

* Uses string splitting to parse input task names.
* Checks if "all" is present in the input task names.

**Notable Dependencies or Requirements:**

* The `TASKS` list of predefined task names (assuming it's defined elsewhere).
* None explicitly listed, but implicit dependencies on Python's built-in data structures and error handling mechanisms.


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Here is a concise summary of the provided code chunk:

**Main Functionality:**
The `main` function orchestrates data preparation for multiple natural language processing (NLP) tasks. It:

1. Parses command-line arguments for task specifics (`--data_dir`, `--tasks`, and `--path_to_mrpc`) to download and process data.
2. Creates a directory to store the downloaded data if it does not exist.
3. Determines the tasks to be processed based on the comma-separated string provided in `--tasks`.
4. For each task, performs specific actions:
   - MRPC task: Formats MRPC data using `format_mrpc`.
   - Diagnostic task: Downloads diagnostic data using `download_diagnostic`.
   - Other tasks: Downloads and extracts data using `download_and_extract`.

**Key Implementation Details:**

* Utilizes `argparse` for command-line argument parsing.
* Uses the `get_tasks` function to determine the specific tasks to be processed.
* Implements a conditional statement to perform task-specific actions based on the parsed arguments.

**Notable Dependencies or Requirements:**

* `os` module for directory manipulation (e.g., creating directories).
* `argparse` library for command-line argument parsing.
* External dependencies:
	+ `get_tasks` function (not shown in this snippet).
	+ `download_diagnostic`, `format_mrpc`, and `download_and_extract` functions (not shown in this snippet).


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** camel_case_split() Function Summary **


*   **Purpose and Functionality:** 
    Splits a camel-cased string into individual words based on capitalization.

        *   Type Hint: `identifier (str) -> List[str]`
        *   Returns: A list of words separated by capital letters.

*   **Implementation Details:** 
    - Uses regular expressions with look-behind and look-ahead assertions to match word boundaries.
    - Utilizes the `re.finditer` function to extract matches from the input string.


*   **Notable Dependencies or Requirements:**
    - `re` module for regex functionality
    - No external dependencies


        Example usage:

            ```py
>>> camel_case_split("CamelCasedClass")
['Camel', 'Cased', 'Class']
```


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**Function Summary: `get_frameworks_table`**

**Primary Purpose:** Generates a pandas DataFrame containing the supported auto classes for each model type, using the content of the auto modules.

**Key Implementation Details:**

* Uses two dictionaries to map model prefixes to model types (`model_prefix_to_model_type`)
* Populates three separate dictionaries (`pt_models`, `tf_models`, and `flax_models`) to track backend support for each model prefix
* Iterates through all transformers object attributes using regular expressions to identify models supported by PT, TF, or Flax backends

**Notable Dependencies and Requirements:**

* `pd` (pandas library) for data manipulation
* `transformers_module` (external dependencies) containing configuration mapping names and processing class mappings
* `_re_tf_models`, `_re_flax_models`, and `_re_pt_models` (regex patterns used to identify models supported by PT, TF, or Flax backends)
* `collections` (utility module) for using defaultdicts


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**Function Summary**

### `update_pipeline_and_auto_class_table`

Updates the table mapping models to pipelines and auto classes without removing old keys.

#### Parameters
- `table`: The existing table mapping model names to a tuple containing the pipeline tag and the auto-class name (Dict[str, Tuple[str, str]])

#### Returns
- `Dict[str, Tuple[str, str]]`: The updated table in the same format

#### Implementation Details

- 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)
- Handles cases where pipeline type may not exist in a given framework
- Uses model mappings and pipepline tags to populate the table

#### Notable Dependencies
- `PIPELINE_TAGS_AND_AUTO_MODELS`
- `transformers_module.models.auto.modeling_auto`, `transformers_module.models.auto.modeling_tf_auto`, `transformers_module.models.auto.modeling_flax_auto`

#### External Dependencies
- `transformers_module`

#### Notes

This function iterates over a list of frameworks, extracts model names, and updates the table with pipeline tags and auto-model classes.


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**Function Summary:**

`update_metadata`

**Purpose:** Updates the metadata for the Transformers repository by processing and uploading dataset files.

**Key Implementation Details:**

* Downloads pipeline_tags.json and frameworks.json dataset files from huggingface/transformers-metadata
* Processes framework and tag data from downloaded files
* Checks if updates need to be pushed to GitHub and commits message is generated accordingly

**Noteable Dependencies/Requirements:**

* `get_frameworks_table` function (not shown in this code snippet)
* `Dataset.from_json` and `Dataset.from_pandas` functions (from the Hugging Face datasets library)
* `hf_hub_download` and `upload_folder` functions (from the Hugging Face Hub client library)

**Interfaces/ Method Signatures:**

* `update_pipeline_and_auto_class_table(table)`: Updates table with processed framework data
* `get_frameworks_table()`: Returns a pandas dataframe containing frameworks data

**External Dependencies:**

* `huggingface/transformers-metadata`
* `huggingfacedatasets` library
* `huggingface_hub` library


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**Summary:**

This function, `check_pipeline_tags()`, detects and raises an error if any pipeline tasks are missing from the predefined `PIPELINE_TAGS_AND_AUTO_MODELS` constant.

**Key Details:**

- Verifies that all task pipelines match the predefined auto-models.
- Checks against `transformers_module.pipelines.SUPPORTED_TASKS`.
- Raises a `ValueError` with missing task pipelines as input when not found in the defined constants.

**Implementation Notes:**

* Utilizes dictionary comprehension to create an in-table mapping of pipeline tags and models.
* Iterates through supported tasks, checking their existence in the predefined constant map (`in_table`).
* Raises an error if the `missing` list is populated during iteration. 

This function ensures that all defined pipelines have corresponding auto-models.


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r"""
The argument `test_file` in this file refers to a model test file. This should be a string of the from
`tests/models/*/test_modeling_*.py`.
"""


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**Function Summary**

The `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.

**Key Implementation Details**

- Splits the input `test_file` into components using the OS-specific path separator.
- Verifies that the first two components are "tests" and "models," and the last component ends with ".py."
- Reconstructs the module path from the validated components.

**Notable Dependencies and Requirements**

- Uses the `os` module for handling file paths.
- References `python files` directly in validation checks.


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**Summary:**

This 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.

**Key Implementation Details:**

* Imports a module using `importlib.import_module` with a specified path.
* Raises a ValueError with an informative message if the import fails due to a common issue.

**Notable Dependencies:**

* `importlib`
* None

**Interfaces/Classes:**

* No external interface or class is used in this function.


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**Code Summary**

### Purpose and Functionality

Get all classes in a model test file whose names end with "ModelTester".

### Implementation Details

* Uses the `dir()` function to list attributes of the `test_module` object.
* Filters attributes based on name ending with "ModelTester".
* Retrieves attribute objects using `getattr()`.
* Sorts retrieved classes by their names using the `sorted()` function and a lambda function.

### Notable Dependencies or Requirements

* `get_test_module`: Returns the test module for the given test file (not shown in this code snippet).
 
This summary preserves detailed information from the original code while minimizing unnecessary details.


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Here is a concise summary of the code chunk:

**Function Purpose**
The `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`.

**Key Implementation Details**

* Iterates through the module's attributes to find classes with an empty `all_model_classes` attribute.
* Excludes exceptions like `RagTestMixin` and its subclasses.
* Sorts the test classes by their names using `__name__` attribute.

**Notable Dependencies and Requirements**

* The `get_test_module` function is called from within this function, suggesting a dependency on another module (`test_file`).
* This function relies on `unittest.TestCase`, indicating a requirement for testing framework.


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**Function Summary**

`get_model_classes`

Returns a sorted list of unique model classes from `all_model_classes` attributes in a model test file.

**Key Implementation Details**

* Uses `set` to eliminate duplicate class instances
* Iterates over `test_classes`, updating `model_classes` with `all_model_classes`
* Sorts classes by name using a case-insensitive comparison

**Notable Dependencies and Requirements**

* `get_test_classes` function is called with `test_file` as an argument
* Uses `__name__` attribute of class names for sorting comparison
* No external dependencies are listed due to lack of information


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**Summary**

The `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.

**Key Implementation Details**

* Tries to call `setUp` on the test instance if present
* Ignores cases where `model_tester` is set to `None`, likely due to default values in `ModelTesterMixin`

**Notable Dependencies/Requirements**

* `test_class`: The input class being analyzed
* `TF/Flax` (implied): The framework used for the `ModelTesterMixin`
* No external dependencies noted

**Notes**: This code appears to be part of a testing harness, likely for machine learning models.


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**Function Summary**

`get_test_classes_for_model`: Returns a list of test classes from `test_file` that include `model_class`. 

**Key Implementation Details**

* Uses `sorted` function for alphabetical ordering by class name (`x.__name__`)
* Iterates through all test classes using `get_test_classes(test_file)`
* Filters test classes based on presence of `model_class` in `all_model_classes`

**Input/Output**

* `test_file`: input file containing test classes
* `model_class`: model class to search for in test classes
* Returns a sorted list of matching test classes

**Dependencies/Requirements**

* `get_test_classes` function from same library or module


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**Summary**

Returns a list of model tester classes from a test file (`test_file`) associated with the specified `model_class`, sorted alphabetically by class name.

**Key Details**

* **Functionality**: Retrieves and filters model tester classes based on the provided `test_file` and `model_class`.
* **Algorithm/Design Choice**: Sorts filtered tester classes using the `__name__` attribute.
* **Method Signatures**:
	+ `get_test_classes_for_model()`: returns list of test classes for a given model
	+ `get_model_tester_from_test_class(test_class)`: returns associated model tester class from a test class

**Dependencies**

* `get_test_classes_for_model`
* `get_model_tester_from_test_class`


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**Summary**

`get_test_to_tester_mapping` function:
 
* Maps test classes to model tester classes based on inheritance from `unittest.TestCase`.
* Uses `get_test_classes` to retrieve test class list and `get_model_tester_from_test_class` to map each test class.

**Key Details:**

* Reusable in a unit testing framework.
* May return classes that are not subclasses of `unittest.TestCase`.

**Implementation Notes:**

* Uses dictionary comprehension for efficient creation of mapping.
* Relies on `get_test_classes` and `get_model_tester_from_test_class` external functions.


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**Summary:**

`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.

**Key Details:**

- Uses `get_model_classes(test_file)` to retrieve model class names
- Populates a dictionary (`model_test_mapping`) with models as keys and test classes as values using map comprehension

**Notable Dependencies/Requirements:**

- `get_model_classes(test_file)`
- `get_test_classes_for_model(test_file, model_class)`

It can be summarized as:
```python
def get_model_to_test_mapping(test_file):
    # Maps model classes to their corresponding test classes
    model_classes = get_model_classes(test_file)
    model_test_mapping = {model_class: get_test_classes_for_model(test_file, model_class) for model_class in model_classes}
    return model_test_mapping
```


key: chroma:document
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**Summary**

`get_model_to_tester_mapping` function maps model classes to their corresponding tester classes in a given test file.

**Key Details:**

* Uses `model_classes` from `get_model_classes` for iteration
* Iterates using dictionary comprehension to create mappings of model classes to tester classes
* Returns a dictionary `model_to_tester_mapping`

**Notable Dependencies and Interfaces:**

* References `get_model_classes` and `get_tester_classes_for_model` functions (not shown)


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**Concise Summary**

### Function Purpose and Main Functionality
The `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.

### Key Implementation Details

* Handles string, type alias, list, tuple, and dictionary conversions using recursive calls.
* Uses `__name__` attribute to display only class names instead of full class representations.

### Notable Dependencies or Requirements
None mentioned, but it assumes objects are usable in a context that supports nested data processing (e.g., lists, dictionaries).

### Key Method Signatures and Interfaces

* Implicit: No explicit interfaces defined.
* Explicit: None.


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**Summary:**

`get_checkpoint_from_config_class` function retrieves the corresponding checkpoint name from a provided config class's source code.

*   **Purpose:** To extract the checkpoint name from a config class based on its implementation details.
*   **Key Details:**
    *   Uses regular expression to find checkpoints in the config class source code.
    *   Verifies each checkpoint by comparing its name with the corresponding link.
*   **Notable Dependencies/Requirements:**
    *   `_re_checkpoint` regular expression pattern
    *   `inspect.getsource(config_class)` function
*   **Interfaces/Methods:** None (uses built-in Python functions and modules)
*   **External Dependencies:**

    *   `_re_checkpoint`
    *   `config_class` (input parameter)

**Summary Focus Criteria Met:**

1. Precise technical terms used: "config class", "checkpoint name", "regular expression pattern"
2. Class/function/variable names preserved exactly.
3. Primary purpose stated: "To extract the checkpoint name from a config class based on its implementation details."
4. Key implementation details narrowed down to:
    *   Regular expression usage
    *   Comparison verification for checkpoints
5. Notable dependencies listed with exact class names and functions: `_re_checkpoint`, `config_class`.
6. No external modules or libraries required besides built-in Python functions.


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**Function Summary**

`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.

**Key Implementation Details**

* Skips deprecated models by checking for "models.deprecated" in the module name.
* Iterates over config classes in `CONFIG_MAPPING`, appending names of missing checkpoint info to `configs_without_checkpoint` list.
* If no checkpoints found, raises a ValueError with an error message and sorted list of configuration class names.

**Notable Dependencies and Requirements**

* `CONFIG_MAPPING`: mapping of configuration classes
* `CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK`: list of ignored configuration class names for docstring checkpoint check
* `get_checkpoint_from_config_class(config_class)`: function to retrieve checkpoint from a config class
* `sorted()` function for sorting configuration class names.


key: chroma:document
string_value:
**Summary**

The `handle_test_results` function parses test results to extract failure and success counts, as well as the timestamp. Key aspects include:

*   **Purpose**: Parse test output to calculate failure and success metrics.
*   **Implementation Details**:
    *   Splits test output into expressions using spaces as separators.
    *   Calculates `time_spent` by detecting presence of `== OUTPUT ==` signs in the last expression.
    *   Iterates through expressions, incrementing `failed` and `success` counts based on specific checks (e.g., "failed" or "passed").
*   **Notable Dependencies/Requirements**:
    *   None

**Detailed Information**

*   Method Signatures: Not applicable
*   Interfaces Implemented: None
*   External Dependencies: None


key: chroma:document
string_value:
**Summary:**

This 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.

**Key Details:**

- **Algorithm:** Uses regular expressions to scan short lines for a pattern (_ [doctest]).
- **Method Signatures/Interfaces:** Takes `failures_short_lines` as input and returns a dictionary of failures (`failures`) with file names.
- **Dependencies:**
  - Utilizes external `re` (regular expression) module.
  - No imported classes/functions besides built-in Python.

**Context:**
This function appears to be part of a code analysis or testing framework, likely used for data extraction from test output logs.


key: chroma:document
string_value:
**Class Summary:**

The `Message` class is designed to represent test results and report them in a Slack channel. It has the following primary purposes:

*   Calculate and store metadata about the test results (e.g., number of successes/failures, time spent on tests)
*   Generate reports with various information types (header, failures by category, payload)

**Notable Properties and Methods:**

*   `n_failures`, `n_tests`: Number of test failures and total tests
*   `payload`: A Slack-compatible report payload in JSON format

Key methods:

*   `post()`: Posts the test result report to the specified Slack channel.
*   `error_out()`: Sends a notification with an error message to the same Slack channel.
*   `get_reply_blocks()`: Generates reply blocks for individual test failures, including links to GitHub Action jobs.


key: chroma:document
string_value:
**Summary**

`retrieve_artifact` function retrieves files from a specified directory and returns an artifact dictionary containing file names as keys and their corresponding contents.

**Main Purpose and Functionality**
The 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.

```python
def retrieve_artifact(name: str) -> dict:
    # ... (function implementation remains the same)
```

**Key Implementation Details**

* Uses `os.path.exists` to check if the specified directory path exists.
* Lists files using `os.listdir`.
* Tries to open and read each file with UTF-8 encoding, raising an error for decoding failures.

```python
files = os.listdir(name)
for file in files:
    try:
        with open(os.path.join(name, file), encoding="utf-8") as f:
            _artifact[file.split(".")[0]] = f.read()
```

**Notable Dependencies and Requirements**

* `os` module for path manipulation.
* External dependency: `file extensions (e.g., `.txt`, `.pdf`).
* No apparent inheritance or interface implementation.

```python
import os
```

**RAG System Representation**

| Key | Value |
| --- | --- |
| Purpose | Retrieve files from a specified directory and return an artifact dictionary. |
| Functionality | Iterates over each file, attempts to decode its contents, and stores them in a dictionary. |
| Implementation Details | Uses `os.path.exists`, `os.listdir`, and UTF-8 encoding for file reading. |
| Dependencies | `os` module. |


key: chroma:document
string_value:
**Concise Summary:**

This 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.

**Key Implementation Details:**

* Creates an `Artifact` class with `name` attribute, which stores directory names as names and their corresponding paths.
* Utilizes `filter()` and `os.path.isdir()` to retrieve only directories from the system.
* Popsulates `_available_artifacts` dictionary with the extracted directory information.

**Notable Dependencies:**

* `os`: for interacting with the operating system (e.g., `os.path.isdir()`)
* `filter()`: a built-in Python function for filtering elements from iterable collections based on condition.

**Primary Purpose and Functionality:**
The main purpose of this code is to retrieve available artifacts stored in directories, which can be utilized for subsequent operations or processing. 

**Additional Informed Insights:**
- The code iterates over all files and folders in the system using `os.listdir()`.
- It populates an internal data structure `_available_artifacts` dictionary, containing only directories as keys.
- It assumes that the directory names match the artifact names within these instances, allowing for consistent categorization.


key: chroma:document
string_value:
**Concise Summary**

The `clean_model_doc_toc` function cleans the table of content in model documentation by removing duplicates and sorting models alphabetically.

**Main Purpose and Functionality**

* Cleans a list of dictionaries (`model_doc`) extracted from `_toctree.yml` by:
	+ Counting occurrences of each local key
	+ Identifying duplicate keys with multiple titles
	+ Removing all but one version of each duplicate title
* Returns the cleaned list sorted alphabetically by title.

**Key Implementation Details**

* Uses `defaultdict` to count occurrences of each local key.
* Utilizes a lambda function for sorting by title (case-insensitive).
* Raises a ValueError if multiple versions of a title are found for a duplicate key.

**Notable Dependencies or Requirements**

* Uses Python's built-in `List`, `dict`, and `defaultdict` types.
* Assumes the presence of an `_toctree.yml` file with documentation data.


key: chroma:document
string_value:
**Summary: check_model_doc function**

The `check_model_doc` function validates and potentially auto-cleans the table of contents (`_toctree.yml`) for the model API doc. It checks for:

* Duplicates: A duplicate title is found, indicating an invalid TOC structure.
* Sorting: The TOC contains unsorted sections.

**Key Implementation Details**

1. Reads `PATH_TO_TOC` file using `yaml.safe_load()`.
2. Iterates to find the "API" and "Models" section indices in the content tree.
3. Extracts modalities documents from the "Models" section.
4. Cleans each modality document using `clean_model_doc_toc()`.

**Notable Dependencies/Requirements**

* `yaml`: for parsing the `_toctree.yml` file.
* `PATH_TO_TOC`: a constant representing the file path to `._toctree.yml`.

**Interfaces and Method Signatures**

1. `yaml.safe_load()`: loads YAML data from a string.
2. `clean_model_doc_toc()`: cleans a model doc using unknown implementation details.

This function appears to be part of a larger project for generating and maintaining documentation, possibly in a MarkDown or Sphinx-based environment.


key: chroma:document
string_value:
**Summary:**

The `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.

**Key Implementation Details:**

* Runs `python3 -m pytest -v <test_name>` with specified `target_test`
* Checks if error or failure message is present in standard output/error
* Exits with success (0) or failure (-1/2) depending on validation

**Notable Dependencies/Requirements:**

* `os` and `subprocess` modules for executing external commands
* `pytest` module for running tests


key: chroma:document
string_value:
**Function Summary: find_bad_commit**

**Purpose:** Find the earliest commit between two given commits at which a specified test fails.

### Functionality:

1.  **Initial Check**: Verifies if `start_commit` and `end_commit` are equal, returning `start_commit` in this case.
2.  Creates a bash script to reset git bisect mode and start it with the provided `target_test`.
3.  Runs the `run_git_bisect.sh` script using `subprocess.run`, capturing its output and error messages.
4.  Checks for errors, specifically "bisect run failed". If found, extracts the error message and continues.
5.  Seeks the first occurrence of "first bad commit" in the standard output to identify the earliest failing commit.

### Key Implementation Details:

*   `git bisect` mode is managed using bash scripts and subprocesses.
*   Standard output and error messages are analyzed for errors or test failures.
*   Regular expressions (`re`) are used to extract specific information from the output.

**Key Method Signatures:**

*   `create_script(target_test)`
*   `subprocess.run(["bash", "run_git_bisect.sh"])`

**Dependencies and Interfaces:**

*   External dependencies:
    *   `git` command
    *   `python3` for running script
    *   `re` (regular expression library) for pattern matching

**Interfaces:**

| Interface | Description                |
| :-------- | -------------------------- |
| `target_test` | Target test to check          |

### Notable Variability:

*   Initial commit check logic.
*   Bash script and subprocess execution.

This 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.


key: chroma:document
string_value:
**Summary**

The `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.

* Purpose: Fetch commit metadata via GitHub API.
* Implementation Details:
	+ Retrieves PR info for a given commit and extracts the PR number.
	+ Fetches details about the associated PR, including author and merged author, if available.
	+ Fallbacks to fetching original commit information using `commit` URL if no PR author is found.
	+ Returns a dictionary with commit-related data.

**Key Method Signsatures/Interfaces**

* `requests.get()`: Used for API requests (`url = f..."`)

**Notable Dependencies**

* `requests`
* `json` ( implicit, handling JSON responses from GitHub API)
* `user`, `login` (accessed through PR and commit responses)

**Interface Inheritance/Implementation**

None.

This 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.


key: chroma:document
string_value:
**Function Summary: `onnx_compliancy`**

### Purpose and Functionality

The `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.

**Key Implementation Details**

* Extracts ONNX opsets from `onnx.json` file
* Parses saved model from file using `SavedModel.ParseFromString`
* Iterates over multiple graphs in saved model (if present)
* Compares extracted operation names from saved model to list of compatible operations (`onnx_ops`) and internal operations (`INTERNAL_OPS`)
* Raises exception if strict mode is enabled and incompatible ops are found

**Notable Dependencies and Requirements**

* `SavedModel` class
* `json` module for parsing `onnx.json` file
* `os` module for getting repository path (`REPO_PATH`)
* External dependencies:
	+ `onnx_ops` (list of compatible ONNX opsets)
	+ `INTERNAL_OPS` (list of internal operations)


key: chroma:document
string_value:
**Summary: QuantizationMessage Class**

This class, `QuantizationMessage`, inherits from the `Message` class and represents a message containing quantization testing results.

**Main Purpose and Functionality**
The 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.

**Implementation Details**

*   **Initialization**: The constructor takes two parameters: `title` (a string) and `results` (a dictionary).
    *   `results`: A dictionary containing test results, including failure information.
        *   `n_success`
        *   `single_gpu_failures`
        *   `multi_gpu_failures`
        *   `n_failures`
        *   `n_tests`
        *   `thread_ts` (initially None)
*   **Properties**:
    *   `payload`: Returns the formatted JSON payload as a string, including optional sections for failure overview and detailed reports.
        *   `blocks`: A list of JSON blocks containing the message content.
        *   Includes failuer section
    *   `time`: Calculates and returns the total time spent on all tests in hours, minutes, and seconds.
*   **Methods**:
    *   `post()`: Sends the message payload to a Slack channel using the client's chat post message method.
    *   `post_reply()`: Posts replies for individual jobs with failures.


key: chroma:document
string_value:
**Function Summary:**

`get_new_python_files_between_commits`

Returns 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.

**Key Implementation Details:**

* Uses `git.Repo.diff()` to compare each commit's diff against the base commit.
* Iterates through a list of commits (`commits`) using the `diff()` method.
* Identifies new Python files added in each commit by checking for file creation type ("A") and file extension (`.py`).
* Returns a list of paths to newly created Python files.

**Notable Dependencies/Requirements:**

* `git.Repo`: Git repository access
* `List[str]`: List of commit references

**Primary Purpose:**
Identify new Python files added between a base commit and one or several commits in a Git repository.


key: chroma:document
string_value:
**Function Summary**

`get_new_python_files()` returns a list of Python files added between the current Git head and the `main` branch.

**Key Implementation Details**

* Uses `Repo` class to interact with the GitHub repository
* Checks if `main` branch exists locally, falling back to remote `origin` in GitHub Actions runners
* Retrieves merge base commits using `repo.merge_base`
* Returns list of new Python files added using `get_new_python_files_between_commits`

**Notable Dependencies and Requirements**

* `Repo` class from `pathlib` library ( implied but not explicitly stated)
* External dependency on GitHub repository at `PATH_TO_REPO`


key: chroma:document
string_value:
**Function Summary: `get_new_model`**

Retrieves the names of newly created Python model files in a Git repository.

**Primary Functionality:**
Parses file contents to extract and return the name of the new model file, assuming only one new modeling file exists in each pull request.

**Key Implementation Details:**

* Uses regular expression `reg` to extract pattern "src/transformers/models/(.*)/modeling_.*\.py"
* Iterates over list of new Python files (`new_files`) and uses `re.findall` to find the matching model file name
* Returns the extracted model file name as a string

**Notable Dependencies or Requirements:**

* `re` (regular expression) module for pattern matching
* `get_new_python_files()` external function or call, returning list of new Python files


key: chroma:document
string_value:
**Summary**

### Functionality

The `parse_message` function parses a GitHub pull request comment to extract models specified in the message to run slow CI.

### Implementation Details

* **Key Algorithm:** Trims and normalizes user input message.
* **Method Signature:**
	+ `parse_message(message: str) -> str`
* **Design Choice:** Uses multiple prefixes ("run-slow", "run_slow", "run slow") to account for varying notation in comments.

### Dependencies and Requirements

* None explicitly listed, but assumes `str` type for input message.


key: chroma:document
string_value:
**Summary**

### Function Overview

`get_models(message: str)`

Extracts and preprocesses a list of models from a given message.

### Primary Purpose

Resets and splits the `models` string to separate individual model names, replacing commas with spaces.

### Key Implementation Details

* Uses `parse_message(message)` (exact class/function not provided)
* Applies whitespace manipulation to output string
* Utilizes string splitting (`split()`) on `models`

### Notable Dependencies or Requirements

* `parse_message(message)` (related function not provided)
 
### External Dependencies

List of external dependencies is not specified in the given code chunk


key: chroma:document
string_value:
**Summary**

Checks if a `model_name` is valid. A valid model name consists of letters, digits, and underscores only.

* **Primary Purpose**: Validate model names to ensure they comply with the specified format.
* **Key Implementation Details**:
	+ Uses regular expression for string validation (`string.ascii_letters + string.digits + "_"`).
	+ Checks for leading/trailing underscore prefixes using `startswith()` and `endswith()`.
* **Notable Dependencies**:
	+ `string` module for character classification.
* **Interfaces/Inherited Class**: None.

In RAG system notation: 
### model_name_validation
- Description: Validates a model name to ensure it contains only letters, digits, or underscores.
- Primary Purpose: Ensure model names meet the specified format requirement.


key: chroma:document
string_value:
**Function Summary**

`process_file(modular_file_path, generated_modeling_content, file_type="modeling_", fix_and_overwrite=False)`

**Purpose and Functionality**
Process and compare the content of two modeling files located in `modular_file_path`.

**Main Algorithms/P Patterns/Design Choices**

* Reading and comparing file contents using `difflib.unified_diff` to identify differences.
* Overwriting or printing differences based on `fix_and_overwrite` flag.

**Key Method Signatures and Interfaces**

- `difflib.unified_diff(fromfile, tofile, lineterm="")`
- `console.print()`

**Notable Dependencies and Requirements**

- `StringIO`
- `difflib`
- `console` (assuming a custom console object or module)
- `generated_modeling_content[file_type]` referencing an external data structure.


key: chroma:document
string_value:
**Summary:**

`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`). 

**Key Implementation Details:**

* Generates expected modeling content using `convert_modular_file(modular_file_path)`
	+ Input parameter: `modular_file_path`
	+ Output parameter: `generated_modeling_content` dictionary containing different file types
* 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)`
	+ Input parameters: `modular_file_path`, `generated_modeling_content`, `file_type`
	+ Output parameter: cumulative difference (`diff`)
* Returns the cumulative difference (`diff`) after processing all files

**Notable Dependencies/Requirements:**

* `convert_modular_file` function
* `process_file` function with parameters `modular_file_path`, `generated_modeling_content`, `file_type`, and `fix_and_overwrite`
 
This summary aims to preserve the original method's flow, focus on key implementation details, and reference related functions by exact name.


key: chroma:document
string_value:
**Summary**

`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)`.

**Implementation Details**

* Uses two regular expressions: `_re_test_backend` to search for the backend pattern and `_re_backend` to extract the backend names.
* Extracts backend names from the line using a list comprehension (`[b[0] for b in _re_backend.findall(line)]`) and sorts them alphabetically.

**Return Value**

Returns all extracted backend names joined on `_and_`, or `None` if no backend is found (`if _re_test_backend.search(line) is None`).


key: chroma:document
string_value:
**Function Summary: `parse_init`**

Parsing an init file to extract import and TYPE_CHECKING definitions.

**Purpose:** Read an init file, identifying and parsing the `_import_structure` and `TYPE_CHECKING` objects defined in it.

**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.

**Key Implementation Details:**

*   Checks for `_import_structure = { ... }` definitions to parse backend-specific imports.
*   Iterates through lines to identify and extract:
    *   Import structures without a specific backend (`_re_one_line_import_struct.search`)
    *   Backend-specific imports within the try-except-else block (`_re_backend_find`)
    *   TYPE_CHECKING objects without a specific backend (`_re_import.search`)
*   Parses import definitions using regular expressions (e.g., `_import_struct_add_many`, `_re_between_brackets`)

**Notable Dependencies and Requirements:**

*   The function uses regular expression classes (`_re_one_line_import_struct`, `_re_backend_find`, etc.) to parse the init file.
*   It relies on `find_backend()` function, but its implementation is not provided in this code snippet.

**Return Value:** A tuple of two dictionaries:
    *   `import_dict_objects`: Mapping backends to lists of import structures without a specific backend (`"none"`).
    *   `type_hint_objects`: Mapping backends (if applicable) to lists of TYPE_CHECKING objects without a specific backend (`"none"`).


key: chroma:document
string_value:
**Function Summary**

`analyze_results` analyzes the discrepancies between `_import_structure` objects and `TYPE_CHECKING` objects found in an init.

**Main Purpose**
Identifies duplicate imports, missing imports, and incorrect backend names across both structures.

**Key Implementation Details**

* Verifies that both input dictionaries have identical keys (backends).
* Uses a custom function `find_duplicates` to detect duplicate values in lists.
* Iterates through each key-value pair, comparing the two dictionaries for:

    * Duplicate imports
    * Missing imports

**Notable Dependencies/Requirements**

* Python built-in libraries: `collections`
* External dependencies:
	+ None (self-contained)
* Implemented interfaces:
	+ No explicit interface implementation; relies on dictionary comparisons.


key: chroma:document
string_value:
**Function Summary:**

`check_all_inits()`

Checks 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.

**Key Implementation Details:**

* Iterates through `__init__.py` files in the transformers repository using `os.walk`.
* Parses initialization file contents using `parse_init(fname)`.
* Analyzes results using `analyze_results(*objects)` and accumulates errors.
* Raises a `ValueError` with error messages if any discrepancies are found.

**Notable Dependencies:**

* `os`, `PATH_TO_TRANSFORMERS`
* `parse_init`, `analyze_results` (external functions not shown in this code snippet)


key: chroma:document
string_value:
**Summary**

`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.

**Key Implementation Details**

* Uses `os.walk()` to traverse directory tree
* Checks for file patterns in folders (`*.py`, `__init__.py`) and files (excluding `__init__.py`)
* Normalizes folder and file paths to standardize submodule names

**Notable Dependencies or Requirements**

* `PATH_TO_TRANSFORMERS`
* `os` module for walking directories
* `Path` class from `pathlib` library for path manipulation


key: chroma:document
string_value:
**Summary: `check_submodules()` Function**

* **Purpose:** Validate that all submodules in `Transformers` are properly initialized with their respective import structures.
* **Key Implementation Details:**
	+ Verifies `transformers._import_structure` keys against the expected structure and user-defined imports from `_init.py`.
	+ Compares submodule lists against imported `import_structure_keys`.
	+ Raises a `ValueError` if any submodules are not properly registered.
* **Notable Dependencies & Requirements:**
	+ `direct_transformers_import`: imports transformers module ensuring it's the one in the repo.
	+ `get_transformers_submodules`: recursively finds all submodules of Transformers.
	+ `_init.py`: provides user-defined import structure additions for optional dependencies.


key: chroma:document
string_value:
**Function Summary: `get_daily_ci_runs`**

**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.

**Key Implementation Details:**

* Employs the API endpoint `https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs` with query parameters:
	+ Branch: `main`
	+ Event: `schedule`
	+ Exclude pull requests: `true`
	+ Page size: `num_runs`
* Utilizes the `requests.get()` method to send a GET request to the API endpoint, authenticated via Bearer token.

**Notable Dependencies and Requirements:**

* `token`: GitHub API authentication token
* `requests` library for API calls
* External dependencies:
	+ github.com ( GitHub repository)
	+ transformrsactions API


key: chroma:document
string_value:
**Summary:**

```markdown
### Function:
#### `get_last_daily_ci_runs(token)`

#### Purpose:
 Retrieves the ID of the most recent completed daily Continuous Integration (CI) workflow.

#### Implementation Details:

* Calls `get_daily_ci_runs(token)` to retrieve daily CI runs
* Iterates through runs, returning the ID of the first complete run (`if "status" in workflow_run:`)
* Returns the most recent completed daily CI run's ID

#### Notable Dependencies:
None

#### Interfaces:
- `get_daily_ci_runs(token)`
```

**RAG Code Tag:**
`func: get_last_daily_ci_runs(token)`:


key: chroma:document
string_value:
**Summary**

The `get_last_daily_ci_run_commit` function retrieves the commit SHA of the last completed scheduled daily CI workflow run.

**Key Points:**

* **Purpose:** Returns the SHA of the latest completed CI run.
* **Functionality:** Iterates through completed daily CI runs to find the head SHA associated with the completion.
* **Implementation Details:**
	+ Calls `get_daily_ci_runs` function to retrieve successful workflow runs.
	+ Loops through runs, identifying completed ones and saving their corresponding head SHA.
	+ Returns the head SHA of the last completed run.

**Notable Dependencies/Requirements:**

* `get_daily_ci_runs(token)` - Function called to retrieve daily CI runs.


key: chroma:document
string_value:
**Function Summary:**

`get_last_daily_ci_artifacts`

 Retrieves and downloads artifacts from the last completed daily CI workflow run.

**Key Implementation Details:**

* Fetches `workflow_run_id` using `get_last_daily_ci_runs`.
* Retrieves artifact links for the specified `workflow_run_id` using `get_artifacts_links`.
* Iterates over `artifact_names`, downloading artifacts matching those names using `download_artifact`.

**Notable Dependencies or Requirements:**

* Utilizes `token` in multiple calls.
* Calls `get_artifacts_links` with `workflow_run_id` and `token`.
* Expects implementation of `download_artifact` function separate from this snippet.

This summary prescribes the main purpose, key algorithms/patterns, and dependencies used within `get_last_daily_ci_artifacts`.


key: chroma:document
string_value:
**Summary**

The `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`.

**Purpose and Functionality**

Retrieves artifacts from the last daily CI workflow run, saving their contents in a dictionary for each artifact.

**Key Implementation Details**

* Iterates over `.zip` files with artifact names matching in the provided directories.
* Reads and decodes file contents using UTF-8 encoding.
* Returns a nested dictionary containing artifact names as keys and their corresponding file contents as values.

**Notable Dependencies or Requirements**

* `os.path.isfile`
* `os.open`
* `zipped zipfile.ZipFile`

Note: 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.


key: chroma:document
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**Summary**

This function, `extract_time_from_single_job`, extracts time-related information from a single job in a GitHub Actions workflow run. It:

* Retrieves the start and end timestamps of the job
* Calculates the duration in minutes between these two timestamps
* Stores this duration along with the original timestamps in a dictionary, `job_info`
* Returns the populated `job_info` dictionary

**Key Implementation Details**

* Retrieves dates using date parsing (`date_parser.parse`)
* Calculates duration in minutes from the timestamp difference ( converted to seconds)

**Notable Dependencies or Requirements**

* `date_parser`: assumed dependency for parsing timestamps into datetimes
* Expected input: `job` dictionary with keys "started_at" and "completed_at"
* Returns a dictionary (`job_info`) with keys "started_at", "completed_at", and "duration".


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**Function Summary**

`get_job_time`: Extracts time information for all jobs in a GitHub Actions workflow run.

**Primary Purpose:** Retrieves and extracts time info from multiple GitHub Actions workflow jobs.

**Implementation Details:**

- Uses `requests` to fetch GitHub API data
- Iterates through 2 pages of jobs (due to pagination) to collect time info
- Utilizes `extract_time_from_single_job` function (not shown in this snippet)
- Handles exceptions and prints error messages if an issue occurs

**Design Choices:**

* Uses Bearer token authentication for API requests
* Sets a page size of 100 jobs per request (per GitHub's rate limiting guidelines)

**Notable Dependencies:**

* `requests`
* `math` (for calculating total pages to iterate over)
* `extract_time_from_single_job` (not shown in this snippet, assumed to be implemented elsewhere)


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**Summary:**

The `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.

**Key Implementation Details:**

* Fetches `transformers` Pypi JSON data using `requests`
* Compares current version to find last stable minor release
* Uses `version.parse()` to sort and compare release versions

**Notable Dependencies/Requirements:**

* `requests` library for HTTP client
* `pyperpapi` library (`version.parse()`) for parsing release versions


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**Function Summary: build_tip_message**

* **Purpose**: Generates a maintenance mode warning message for a machine learning model, indicating that new PRs changing the code are not accepted.
	+ Primary purpose: Warn users about model maintenance mode and provide instructions to reinstall previous version.

**Key Implementation Details:**
- Uses f-string formatting to insert `last_stable_release` into the message.
- Includes a Git installation command for reusing the last stable version supported by the model (Transformer vX.X).

**Notable Dependencies or Requirements:** None mentioned explicitly.


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**Summary:**

`insert_tip_to_model_doc` function appends a tip message below the title section of an existing Markdown document file.

**Key Details:**

* Reads an input Markdown document file (`model_doc_path`) and tip message (`tip_message`)
* Splits the document into lines, separating those with title markers (`# `) from others
* Inserts the tip message lines below the original title lines, preserving blank line separation

**Implementation Notes:**

* Uses `file read/write` operations to modify an external file resource.

**External Dependencies: None specified. No known inherited or implemented interfaces are assumed to be used in this code snippet.**


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**Summary:**

This 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.

**Implementation Details:**

* The function returns two optional strings, (`model_doc_path`, `model_name`) if a matching documentation file exists.
* The search considers three variant names:
	1. Original model name
	2. Model name with hyphens instead of underscores
	3. Model name without hyphens
* It utilizes the `os` module for path existence checks.

**Notable Dependencies:**

* `REPO_PATH`: assumed to be a constants or an external variable referencing the repository's root directory.
* `os.path.exists()`
* `zip()` function


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**Function Summary: extract_model_info**

**Purpose and Functionality:**
This function retrieves information about a given machine learning model, including its documentation path and file location.

**Key Implementation Details:**

* Retrieves `model` using the `get_model_doc_path` function to get its documentation path.
* Returns a dictionary containing the following keys:
	+ `model_doc_path`: Path to the model's documentation.
	+ `model_doc_name`: Name of the model's documentation.

**Notable Dependencies and Requirements:**

* `REPO_PATH`: A constant representing the repository path.
* `get_model_doc_path` function.
* External dependencies: This function relies on the existence of the `get_model_doc_path` function, which itself may depend on other external libraries.


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**Summary**

`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.

**Key Implementation Details**

* Replaces `from ..` with `from ...` across all lines to convert relative imports to absolute ones.
* Uses `split("\n")` to split the file into individual lines for processing.

**Notable Dependencies/Requirements**

* None explicit dependencies. However, it's assumed that this function runs in a Python environment with necessary modules and libraries available.


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**Summary**

This 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.

**Key Implementation Details:**

* Iterates over lines in each file in the model directory, skipping lines containing "# Copied from".
* Uses `os` and `re` modules implicitly for listing directory contents and file reading/writing.
* Writes modified line-by-line to new file, omitting lines with the specified comment.

**Notable Dependencies/Requirements:**

* `REPO_PATH`: reference path (relative to current working directory) containing model files.
* `os`: file system operations.
* `re`: regular expression matching used implicitly for pattern searching.


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**Summary: Migrate Model Files to Deprecation**

### Purpose and Functionality

Migrates model files from `src/transformers/models/{model}` to `src/transformers/models/deprecated/{model}`.

### Key Implementation Details

* Uses `repo.git.mv` to rename files, updating them at the new location.
* Calls `update_relative_imports` to update relative imports for deprecated files.

### Notable Dependencies or Requirements

* `REPO_PATH`: Base path of the repository
* `os.path.exists`, `os.makedirs`, `repo.git.mv`
* `update_relative_imports`

Note: The exact implementation details may vary based on the `update_relative_imports` function, which is not shown in this snippet.


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**Function Summary: `delete_model_tests`**

 Deletes model test files from the repository, removing related Git tracking.

**Main Purpose and Functionality:**
The 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.

**Key Implementation Details:**

* Uses `os.path.exists` to check for file existence.
* Utilizes `REPO_PATH / f"tests/models/{model}"` as the test path, referencing the `REPO_PATH` constant.
* Executes `repo.git.rm("-r", tests_path)` to remove the files and their tracking using Git.

**Notable Dependencies or Requirements:**

* References `repo`, an existing Git repository object.
* Utilizes external tool, `os`.
* `REPO_PATH`, a constant referencing the repository path.


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**Summary:**

Calculates the line indentation by subtracting the length of the stripped leading spaces from the total string length.

**Key Implementations:**

* `lstrip()` method is used to remove trailing whitespace characters
* Function takes a single argument `s` (string)

**Notable Dependencies/Requirements:**

* None mentioned

This 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.


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**Function Summary: `update_main_init_file`**

**Primary Purpose:** Replaces deprecated model names in `__init__.py` file with new, deprecated versions.

**Key Implementation Details:**

* Replaces instances of `model.model_name` with `model.deprecated.model_name` in the `__init__.py` file.
* Also updates import statements to use the deprecated module name.

**Notable Dependencies and Requirements:**

* `REPO_PATH`: The absolute path to the repository's source directory (`"src/transformers/__init__.py"`).
* Uses an external function `sort_imports_in_all_inits(check_only=False)` (not shown in the provided code snippet).


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**Summary:**

`remove_model_references_from_file` function:

* Purpose: Removes references to specified models from a given file by applying a filter condition.
* Key Implementation Details:
	+ Reads the entire file into memory using `init_file = f.read()`.
	+ Iterates over each line, skipping those that match any model based on the `condition` function.
	+ Writes the new filtered file contents back to disk.
* Notable Dependencies and Requirements:
	+ Uses Python's built-in `open()` and `read/write` functions for file input/output.
	+ Assumes a `REPO_PATH` constant or variable defining the repository root path.
	+ Accepts a `condition` function with signature `Callable[[str, str], bool]`.

**RAG (Reusable Anonymous Code) System:**

```markdown
# remove_model_references_from_file
### Purpose
Removes references to specified models from a given file.

### Key Implementation Details

* Reads entire file into memory.
* Applies filter condition to line-model pairs.
* Writes new filtered file contents back to disk.

### Dependencies and Requirements

* Python built-in `open` and `read/write`.
* `REPO_PATH` constant or variable.
* `condition` function signature: `Callable[[str, str], bool]`.
```


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**Function Summary:**

`remove_model_config_classes_from_config_check`

Removes deprecated model configuration classes from the `check_config_attributes.py` file.

**Purpose and Functionality:**

* Reads the contents of the specified Python file (`utils/check_config_attributes.py`)
* Iterates through each line, tracking indentation and special cases
* Removes comments above deprecated model config classes and rewrites the content in a new file

**Key Implementation Details:**

* Uses regex-free text processing to identify lines with model config classes
* Preserves indentation and formatting when rewriting the content
* Handles edge cases such as nested brackets and escaped newline characters

**Dependencies and Interfaces:**

* None, but may utilize external `REPO_PATH` constant in a larger context.
* Does not implement or inherit any specific interfaces.

**Method Signatures:**

* `remove_model_config_classes_from_config_check(model_config_classes)`: takes a list of string model config class names as input


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**Function Summary:**

`add_models_to_deprecated_models_in_config_auto(models)`

Adds the input `models` to the list of deprecated models in `configuration_auto.py`, updating its contents and sorting the list alphabetically.

**Key Implementation Details:**

- Reads `DEPRECATED_MODELS` from `configuration_auto.py`.
- Appends new models to the list in alphabetical order.
- Updates the file with the modified content.

**Notable Dependencies/Requirements:**

- `REPO_PATH`: reference to a repository-specific path ( exact class/function name required)
- External dependency on:
  - `os` module for file system operations
  - `pathlib` module for handling file paths
  - `shlex` module (not explicitly used but possibly implied by the `split()` and `strip()` functions)

**Interface/Import Notes:**

- No explicit import statements are provided, suggesting that necessary modules and classes will be imported within this code.


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**Function Summary: deprecate_models**

### Purpose and Functionality:

The `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.

### Key Implementation Details:

*   Iterates through a list of models using `models` as input.
*   Calls `extract_model_info(model)` for each model to gather information.
*   Checks if a configured class exists in `CONFIG_MAPPING` or by name within `CONFIG_MAPPING`.
*   Updates the last stable minor release version using `get_last_stable_minor_release()`.
*   Uses `build_tip_message(tip_message)`, `remove_model_config_classes_from_config_check(model_config_classes)`, and other functions to finalize model depreciation.
*   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.

### Dependencies and Requirements:

*   Uses `defaultdict` from Python's standard library for creating default dictionaries.
*   References `CONFIG_MAPPING` (a dictionary of model configurations) at least once during execution, which must be defined elsewhere in the codebase.
*   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.

### Notable Interfaces:

The function provides a few notable interfaces to other components of the system:
    
*   `CONFIG_MAPPING`: A dictionary mapping model names or documentation lines to their respective configuration classes.
*   External dependencies such as `update_main_init_file`, `remove_model_references_from_file` (referencing specific files or directories), and `build_tip_message`.


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**Function Summary: `sort_auto_mapping`**

The `sort_auto_mapping` function sorts the auto-mappings in a given file.

### Purpose and Functionality:

Automatically rearranges indented lines containing mappings in the specified file to adhere to a specific format. Compares and returns whether the original formatting is preserved upon sorting (when `overwrite=False`) or if changes were made for overwriting (`overwrite=True`).

### Key Implementation Details:

1.  **Regex-based Pattern Matching**:
    *   `_re_intro_mapping.search()`: Identifies the start of a new mapping in lines.
2.  **Blocks and Lines Processing**:
    *   `indentation calculation`: Calculates indentation length before processing each mapping block.
    *   `block decomposition` (`if lines[line_idx].strip() == "(":`): Tackles blocks divided across multiple lines with indentation matching the first line's formatting.
3.  **Sorting Algorithm**:
    *   `blocks sorted by ids` (`sorted(blocks, key=lambda x: _re_identifier.search(x).groups()[0])`): Orders and groups mappings according to their unique identifiers.

### Notable Dependencies or Requirements:

1.  `re` module (regular expressions) for pattern matching.
2.  External file operations via `open` function with UTF-8 encoding.

**API Reference:**

*   `_re_intro_mapping`, `_re_identifier`: Externally defined regular expression patterns used within the sorting logic, not part of this summary but crucial for implementation context.

### Example Usage:
```python
result = sort_auto_mapping('path/to/file.txt')
print(result)  # Returns 'None' if overwrite is False and file has been properly sorted, False otherwise.
```


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**Summary**

Function `sort_all_auto_mappings`: Sorts all auto mappings in the library, fixing inconsistencies if required. 

### Purpose
Synchronizes Python file auto mappings within a module.

### Implementation Details

- Fetches relevant files using list comprehension with path manipulation (`os.path.join`).
- Iterates through each file, calls `sort_auto_mapping` to sort its mappings.
- Checks for non-overwritten changes after sorting; raises an error if updates are detected.


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**Summary:**

This class `CustomTokenizerFast` inherits from `BertTokenizerFast`. It overrides the default tokenizer by specifying its own tokenizer class as `SlowTokenizer`. The primary purpose is to enable the use of a custom, slow tokenization mechanism in place of the FastTokenizer.

**Key Implementation Details:**

* Inherited Class: `BertTokenizerFast`
* Implemented Class: `CustomTokenizer` (specifically assigned as `slow_tokenizer_class`)
* No additional functionality or method implementations provided; primarily serves as an override for the default tokenizer configuration.


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**Summary**

Class: `CustomTokenizer`
Inherits from: `BertTokenizer`

**Purpose and Functionality**: Creates a custom version of the BERT tokenizer.

**Key Implementation Details**:

* No specific tokenization algorithms or patterns are defined, inheriting functionality from `BertTokenizer`.
* No method signatures or interfaces are overridden by default.
 

**Notable Dependencies/Requirements**:
* Inherits from: `BertTokenizer`
* External dependencies not specified.

No key implementation details or interface implementations to note.


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**Summary:**

The `CustomModel` class is a custom PyTorch model derived from `PreTrainedModel`. It uses the `CustomConfig` configuration.

**Main Purpose and Functionality:**
It defines a basic neural network architecture with one linear layer, where the input `x` passes through this layer to produce an output.

**Key Implementation Details:**

*   A linear layer (`self.linear`) with input/ouput size of `config.hidden_size`.
*   The `forward` method applies the linear transformation to the input `x`.

**Notable Dependencies and Requirements:**

*   Import from PyTorch (`torch.nn.Linear`).
*   Inheritance from `PreTrainedModel`.


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**Summary:**

The `NoSuperInitModel` class is a PyTorch-based pre-trained model that extends the `PreTrainedModel` class. It defines:

* A linear layer with a configuration attribute `config.attribute`.
* A forward pass through the linear layer, mapping input `x` to its output.
* An `_init_weights` method that sets no weights (implies super-init behavior).

**Key Details:**

* **Linear Layer**: `self.linear = torch.nn.Linear(config.attribute, config.attribute)`
* **Forward Pass**: `return self.linear(x)`
* **_init_weights Method**: Implicitly inherits from superclass's behavior

**Dependencies/Requirements:**

* PyTorch (`torch`)
* `PreTrainedModel` class (inherited)
* `NoSuperInitConfig` configuration class ( referenced)


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**Code Summary:**

*   Class Name: `CustomImageProcessor`
*   Inheritance: Extends `CLIPImageProcessor` to create a custom image processor.

**Additional Details:**

*   No specific implementation details are provided, as the class solely inherits from `CLIPImageProcessor`.


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**Concise Summary**

This function implements the softmax activation function. It takes an input array `outputs` of shape `(num_classes, batch_size)` and returns a normalized value for each class.

The primary purpose is to prevent output values from growing exponentially large by normalizing them over their maximum value across classes.

**Key Implementation Details:**

- Uses NumPy's vectorized operations to calculate the softmax output.
- Employs shift-invariant exponentiation (`outputs - maxes`) to mitigate numerical instability issues.

**Notable Dependencies and Requirements:**

* `numpy` (imported as `np` for array operations)
* `outputs` input should be an array of shape `(num_classes, batch_size)` containing output values.


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**PairClassificationPipeline Class Summary**

* **Purpose:** This class implements a pipeline for pair classification tasks using a pre-trained model. It sanitizes input parameters, preprocesses text inputs with optional second text, and postprocesses model outputs to extract confidence scores and predicted labels.

* **Key Implementation Details:**
	+ `preprocess` method takes in text inputs and an optional second text, and uses the `tokenizer` to generate input tensors in a specific framework.
	+ `_forward` method passes the preprocessed input to the trained model for inference.
	+ `postprocess` method extracts attention scores from the model output, converts them to probabilities using softmax, and determines the predicted label by finding the class with the highest probability.

* **Notable Dependencies:**
	+ `tokenizer`: a tokenizer object used in the `preprocess` method
	+ `model`: a pre-trained model instance that takes in input tensors in the same framework as the pipeline
	+ `self.framework`: the specific framework used for tensor generation (e.g. PyTorch, TensorFlow)
	+ `softmax`, `logits`, and related functions: commonly used mathematical functions for probability calculations


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**Summary**

`CustomConfig` is a custom configuration class for the `PretrainedConfig` base class. It defines a model type as "custom" and extends initialization from its parent with an additional `attribute` parameter, defaulting to 1.

**Key Implementation Details:**

* Customizes model type to `model_type = "custom"`
* Extends __init__ from `PretrainedConfig` with `attribute` parameter

**Notable Dependencies or Requirements:**
None explicitly listed. Inherited interfaces: `PretrainedConfig`.


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**Summary**

This `NoSuperInitConfig` class inherits from `PretrainedConfig`, defining a custom configuration for a specific model. The primary purpose is to initialize the configuration with an optional `attribute` parameter.

**Key Details**

* Inherited from: `PretrainedConfig`
* Model type: "custom"
* Initialization:
	+ Required attribute: `attribute=1` (default value)

**Notable Dependencies/Requirements**

* None explicitly listed, but assumes access to `PretrainedConfig` class and related dependencies.


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**Summary:**

This `CustomProcessor` class inherits from `ProcessorMixin` and defines two key attributes:

* `feature_extractor_class`: specifies the use of the `AutoFeatureExtractor` component.
* `tokenizer_class`: specifies the use of the `AutoTokenizer` component.

No additional implementation details or dependencies are defined for this class.


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**Summary**

A custom `CustomFeatureExtractor` class that inherits from the `Wav2Vec2FeatureExtractor` class, effectively extending the functionality of the parent class with no additional implementation.

**Key Implementation Details**

* Inherited from `Wav2Vec2FeatureExtractor`, implying an extension to the Wav2Vec2 model's feature extraction capabilities.
* No additional methods or attributes are defined, suggesting a focus on leveraging the existing `Wav2Vec2FeatureExtractor` functionality with no new additions.

**Notable Dependencies**

* `Wav2Vec2FeatureExtractor`: The parent class from which `CustomFeatureExtractor` inherits.

**Additional Requirements**

None.


