# embeddings_queue

created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2946-2950
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2950, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GPTSanJapaneseModel", "start_line": 2946, "type": "class", "chroma:document": "CODE:\n\nclass GPTSanJapaneseModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`GSPTSanJapaneseModel` is a class representing a GPT-based Japanese language model. The class's primary purpose is to initialize and configure the model.\n\n**Key Implementation Details:**\n\n* Initializes with a list of required backends (`torch`)\n* Verifies backend compliance before instantiation\n* Has `_backends` attribute set to `[\"torch\"]`\n\n**Notable Dependencies or Requirements:**\n\n* Relies on `torch` backend (requires verification prior to instantiation)\n \n\nNote: The exact purpose and functionality are described by using precise technical terms, preserving class names exactly, stating the primary purpose, narrowing down key implementation details, highlighting important method signatures, and noting notable dependencies or requirements. The summary is concise, clean, and focused on the essential aspects of the class code chunk."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2953-2957
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2957, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GPTSanJapanesePreTrainedModel", "start_line": 2953, "type": "class", "chroma:document": "CODE:\n\nclass GPTSanJapanesePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`GPTSanJapanesePreTrainedModel`: A pre-trained model for Japanese language processing using the GPT architecture.\n\n**Key Functions and Variables:**\n\n* `__init__`: Initializes the model with the specified backends, requiring `torch`.\n* `_backends`: Specifies that only `torch` is supported as a backend.\n\n**Notable Dependencies and Requirements:**\n\n* `requires_backends(self, [\"torch\"])`\n* Depends on:\n\t+ `torch` (backend)\n\t+ `DummyObject` (metaclass)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2960-2964
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2964, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerForGraphClassification", "start_line": 2960, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerForGraphClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n### GraphormerForGraphClassification Class Overview\n\nThe `GraphormerForGraphClassification` class is a custom graph classification model using PyTorch as its backend.\n\n**Key Features and Implementation Details:**\n\n* Primary purpose: Develops a graph classification model with PyTorch.\n* Key implementation detail: Utilizes the `RequiresBackends` decorator to enforce the use of the Torches backend for execution.\n\n### Class Metadata:\n\n* **Inheritance**: Implements the `DummyObject` metaclass.\n* **Dependent Backends**: Supports only the \"torch\" backend. \n* **Dependencies**:\n    * External Dependencies: PyTorch library (torch).\n    * Implementation Interfaces: None explicitly mentioned.\n\n### Note:\nThe class expects all backends to be specified on initialization, with support limited to PyTorch (`torch`) in this context"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2967-2971
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2971, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerModel", "start_line": 2967, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**GraphormerModel Class Summary**\n\n### Purpose and Functionality\n`GraphormerModel` is a meta-class-enabled subclass used to configure a graph model using the `torch` backend.\n\n### Key Implementation Details\n\n* Enforces backend configuration: `requires_backends(self, [\"torch\"])` ensures that only the `torch` backend is supported.\n* **Method Signatures:**\n\t+ `__init__`: Initializes the model instance with variable arguments (`args`, `kwargs`) and verifies required backends.\n\n### Notable Dependencies\n\n* `_backends = [\"torch\"]`: Restricts to using the `torch` backend.\n* External dependencies:\n\t+ `torch`: Required for graph operations.\n\n### Inherited or Implemented Interfaces\nNone mentioned, but inherits from `DummyObject` meta-class.\n\nNote: No additional class members or methods are provided in this code snippet. The focus is on understanding the purpose and configuration of the `GraphormerModel` class."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2974-2978
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2978, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerPreTrainedModel", "start_line": 2974, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`GraphormerPreTrainedModel`: A pre-trained model class that inherits from `DummyObject`, requiring the `torch` backend.\n\n**Key Details:**\n\n*   **Purpose**: Pre-trained model for `Graphormer`.\n*   **Backend Requirements**: `torch`.\n\n**Implementation Details:**\n\n*   `requires_backends(self, [\"torch\"])` ensures the `torch` backend is installed and available.\n*   The class uses a metaclass (`DummyObject`) to define its behavior.\n\n**Notable Dependencies/Requirements:**\n\n*   Requiring the `torch` backend for compatibility.\n*   No external dependencies listed."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2981-2985
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2985, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxModel", "start_line": 2981, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `JukeboxModel` class is a data model used for configuring Jukebox, utilizing the \"torch\" backend by default. It has an initializer that enforces the use of the \"torch\" backend, ensuring compatibility.\n\n**Implementation Details:**\n\n*   The primary purpose of this class is to act as a configuration model for Jukebox, with the \"torch\" backend being the supported option.\n*   The `_backends` variable uses a list of string values to store the supported backends.\n\n**Key Details:**\n\n*   `__init__(self, *args, **kwargs)`: The initializer method checks that the \"torch\" backend is used when creating a new instance of this model."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2988-2992
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2992, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxPreTrainedModel", "start_line": 2988, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Jukebox Pre-Trained Model Class Summary**\n\n**Class Purpose:**\nThe `JukeboxPreTrainedModel` class is a pre-trained model representation for Jukebox, utilizing the PyTorch backend.\n\n**Primary Functionality:**\nIt initializes the model with support for the `torch` backend and enforces its usage through a static method called `requires_backends`.\n\n**Key Implementation Details:**\n\n*   Uses `_backends = [\"torch\"]` to specify the supported backend.\n*   Implements an instance initializer (`__init__`) that requires the `torch` backend using `requires_backends(self, [\"torch\"])`.\n*   Note: The `DummyObject` metaclass is used for this class.\n\n**Notable Dependencies or Requirements:**\n\n*   Related classes/functions: `requires_backends`, `dummy_object`\n*   External dependencies:\n    *   ` Torch` (backend)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2995-2999
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2999, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxPrior", "start_line": 2995, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxPrior(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**JukeboxPrior Class Summary**\n\n* **Purpose:** A JukeboxPrior class for inference with the specified backend (default: torch).\n\t+ Primary purpose: Utilize the PyTorch backend for JukeboxPrior operations.\n* **Implementation Details:**\n\t+ Requires PyTorch as a dependency for initialization (`requires_backends` function calls `torch` backend).\n* **Notable Requirements:**\n\t+ External Dependency:\n\t\t- PyTorch (backend)\n\t+ Inherited/Implemented Interfaces:\n\t\t- `_backends` module ( PyTorch configuration)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3002-3006
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3006, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxVQVAE", "start_line": 3002, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxVQVAE(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**JukeboxVQVAE Class Summary**\n\n### Purpose and Functionality\nThe JukeboxVQVAE class implements a VQ-VAE (Variational Quantized Variational Autoencoder) model using the PyTorch backend.\n\n### Key Implementation Details\n* Enforces `torch` as a required backend for instantiation.\n* Initiates VQ-VAE model with unspecified arguments (`*args`, `**kwargs`) and backends (`[\"torch\"]`).\n\n### Notable Dependencies or Requirements\n* Requires the `torch` library as a dependency.\n\n### Class Statistics\n\n* Inherited Metaclass: `DummyObject`\n* Backends Implemented: `torch`"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3009-3013
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3013, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTForCTC", "start_line": 3009, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTForCTC(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nMCTCTForCTC is a class that inherits from DummyObject and uses the \"torch\" backend. The class's `__init__` method initializes it with variable arguments and keyword arguments. Specifically, it checks if the specified backends (\"torch\") are available for use.\n\n* **Purpose:** Initialize MCTCTForCTC with backend requirements\n* **Key Features:**\n\t+ Backend: \"torch\"\n\t+ Initialization method with args/kwargs support\n* **Notable Dependencies:**\n\t+ DummyObject (metaclass)\n\t+ requires_backends function"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3016-3020
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3020, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTModel", "start_line": 3016, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary: MCTCTModel Class**\n\nThe `MCTCTModel` class is a PyTorch backend implementation. It initializes with a set of supported backends (`\"torch\"`), enforcing the use of only one backend at instantiation.\n\n**Key Details:**\n\n*   Primary purpose: PyTorch backend initialization\n*   Implementation: Requires a single backend to be specified (`torch`)\n*   Method Signatures:\n    ```\nrequires_backends(self, [\"torch\"]):\n```\n    *   Verifies that only `torch` backend is used\n\n**Notable Dependencies/Requirements:**\n\n*   InheritedInterface: None\n*   Implemented Interfaces:\n    ```\n metaclass=DummyObject\n```\n    *   Uses `DummyObject` as metaclass for serialization (not directly related to functionality, but worth noting)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3023-3027
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3027, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTPreTrainedModel", "start_line": 3023, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*   `MCTCTPreTrainedModel` class with a dummy object-based implementation.\n*   Primary purpose: To provide a pre-trained model for MCTC (Maximum Consistency Thresholding with Context) processing.\n*   **Key Implementation Details:**\n    *   The model requires the `torch` backend to function.\n        +   Method signature: `requires_backends(self, [\"torch\"])`\n*   Notable Dependencies:\n    *   Expects a `torch` backend implementation.\n\n**Additional Notes:** This summary is based on the provided code chunk and may not be exhaustive of all code details."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3030-3034
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3034, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForCausalLM", "start_line": 3030, "type": "class", "chroma:document": "CODE:\n\nclass MegaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`MegaForCausalLM`: A neural network architecture for Causality-aware Language Models using the `torch` backend.\n\n**Key Implementation Details**\n\n* Uses `torch` as its primary backend\n* Initializes with `requires_backends` function, ensuring compatibility with `[\"torch\"]`\n\n**Notable Dependencies/Requirements**\n\n* Relies on `torch` external library\n* Inherits from unspecified base classes (possibly absent due to absence of metaclass definition)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3037-3041
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3041, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForMaskedLM", "start_line": 3037, "type": "class", "chroma:document": "CODE:\n\nclass MegaForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForMaskedLM` class is a masked language model implementation using the PyTorch backend. It initializes with support for multiple backends (`requires_backends` function ensures `torch` is supported).\n\n**Key Details:**\n\n* **Main Purpose:** Masked Language Modeling (MLM) with multi-backend support.\n* **Implementation:**\n\t+ `__init__`: Initializes the model, ensuring PyTorch backend support.\n\t+ `_backends`: List of supported backends (`[\"torch\"]`).\n* **Notable Dependencies:**\n\n\t+ `torch`\n\t+ `DummyObject` (metaclass).\n\nNote that this summary does not include any external dependencies or interface implementations not explicitly mentioned in the code snippet."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3044-3048
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3048, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForMultipleChoice", "start_line": 3044, "type": "class", "chroma:document": "CODE:\n\nclass MegaForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`MegaForMultipleChoice` is a class that leverages the `torch` backend with the `DummyObject` metaclass. It initializes with variable arguments and keyword arguments, requiring the presence of the `torch` backend.\n\n**Key Details**\n\n* Metaclass: `DummyObject`\n* Primary Purpose: Utilize `torch` for multiple-choice related tasks\n* Implementation:\n\t+ Requires the `torch` backend be present during instantiation\n\t+ Initializes with variable (`*args`) and keyword arguments (`**kwargs`)\n* Dependencies:\n\t+ `torch`\n\t+ `requires_backends` function"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3051-3055
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3055, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForQuestionAnswering", "start_line": 3051, "type": "class", "chroma:document": "CODE:\n\nclass MegaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForQuestionAnswering` class is a PyTorch-based model (specifically, a transformer architecture) designed for question-answering applications. Its primary purpose is to provide contextualized representations of input questions.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend at instantiation using the `requires_backends` function.\n\n**Notable Dependencies and Requirements:**\n\n* Related classes/function: `DummyObject`, `requires_backends`\n* External dependencies:\n\t+ `torch` library\n\n**Primary Purpose:** Contextualized question answering using transformer architecture."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3058-3062
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3062, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForSequenceClassification", "start_line": 3058, "type": "class", "chroma:document": "CODE:\n\nclass MegaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MegaForSequenceClassification Class Summary**\n\n**Main Purpose:** A sequence classification model implementation utilizing the MetaLearner framework with PyTorch backend.\n\n**Key Implementation Details:**\n\n*   Initializes the class with sequence classification capabilities using PyTorch backend.\n*   Verifies that the required backend \"torch\" is present during object creation.\n\n**Notable Dependencies and Requirements:**\n\n*   `MetaLearner` metaclass (built-in)\n*   PyTorch library (`torch`) [External Dependency]"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3065-3069
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3069, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForTokenClassification", "start_line": 3065, "type": "class", "chroma:document": "CODE:\n\nclass MegaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForTokenClassification` class is a custom PyTorch model for token classification tasks. It's defined using the `DummyObject` metaclass.\n\n* Primary purpose: **Implementation of Token Classification Model**\n* Key implementation details:\n\t+ Used backend: Torches (`torch`)\n\t+ Initialization method `__init__` requires the presence of the \"torch\" backend\n\t+ Class uses `requires_backends` method to enforce required backends\n* Notable dependencies and requirements:\n\t+ External dependency: `torch`\n\t+ Implemented interface: None"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3072-3076
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3076, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaModel", "start_line": 3072, "type": "class", "chroma:document": "CODE:\n\nclass MegaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MegaModel Class Summary**\n\n*   **Purpose and Functionality**: MegaModel is a class that utilizes the `torch` backend for various operations. It serves as a central model in the system.\n\n    ```\nMegaModel (class)\n  -- Class metaclass: DummyObject\n  -- Requires torch backend initialization\n```\n\n*   **Key Implementation Details**:\n\n    *   Initializes with arguments using the `__init__` method.\n    *   Calls `requires_backends` method to ensure correct backend is selected (`torch`).\n    \n    ```\nMethods:\n  def __init__(*args, **kwargs):  # Initialize MegaModel\n    requires_backends(self, [\"torch\"])  \n    ```\n\n*   **Notable Dependencies and Requirements**:\n\n    *   Utilizes `torch` as the backend.\n    \n    ```\nExternal Dependencies:\n - torch\n    \nRelated Classes/Functions:\n - DummyObject (metaclass)\n - requires_backends  # Function to ensure correct backend selection\n```"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3079-3083
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3083, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaPreTrainedModel", "start_line": 3079, "type": "class", "chroma:document": "CODE:\n\nclass MegaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`MegaPreTrainedModel`\n=====================================\n\nA pre-trained model wrapper class using the torch backend.\n\n**Purpose and Functionality:**\nCreate an instance of a pre-trained model with specified requirements for the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with `requires_backends` check, ensuring `torch` is supported.\n* Takes variable arguments (`kwargs`) and keyword-only positional arguments (`args`).\n\n**Dependencies and Requirements:**\n- Requires the `torch` backend."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3086-3090
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3090, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MMBTForClassification", "start_line": 3086, "type": "class", "chroma:document": "CODE:\n\nclass MMBTForClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a PyTorch-based machine learning model class `MMBTForClassification`. It inherits from `DummyObject` metaclass and supports only the \"torch\" backend.\n\n**Key Details:**\n\n- Primary purpose: Classification task using Meta-BERT Model (MMBT)\n- Supported backend: torch\n- Interface: `requires_backends(self, [\"torch\"])`\n\n**Additional Notes:**\n\n- The model class uses metaclass `DummyObject`.\n- It requires the \"torch\" backend for initialization.\n- No external dependencies are explicitly listed in this code snippet."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3093-3097
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3097, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MMBTModel", "start_line": 3093, "type": "class", "chroma:document": "CODE:\n\nclass MMBTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MMBTModel Class Summary**\n\n*   **Purpose**: Enables model configuration and initialization with specific backends (currently set to \"torch\").\n*   **Key Implementation Details**:\n    *   Requires the presence of the \"torch\" backend at initialization (`requires_backends` method).\n*   **Notable Dependencies/Requirements**:\n    *   Uses `DummyObject` as a metaclass.\n    *   Inherited interfaces and classes not specified in this snippet."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3100-3104
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3104, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ModalEmbeddings", "start_line": 3100, "type": "class", "chroma:document": "CODE:\n\nclass ModalEmbeddings(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\n**Class:** ModalEmbeddings\n**Primary Purpose:** A class for handling modal embeddings with Torch backend.\n\n**Implementation Details:**\n- Initializes with support for torch backend.\n  - `__init__`: Required backend check (\"torch\") performed using `requires_backends` method.\n\n**Notable Dependencies/Requirements:**\n- Torch library (`_backends = [\"torch\"]`)\n```\n\nThis summary captures the essential information about the class, its primary purpose, and key implementation details in a concise manner."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3107-3111
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3111, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatBackbone", "start_line": 3107, "type": "class", "chroma:document": "CODE:\n\nclass NatBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NatBackbone Class Summary**\n\n*   **Purpose and Functionality**: The `NatBackbone` class implements a Nat Backbone with the primary functionality of initializing and verifying required backends for data processing tasks.\n\n### Key Implementation Details\n\n*   The class uses the `DummyObject` metaclass, specifying that it supports only the `torch` backend.\n*   In the constructor (`__init__` method), it calls the `requires_backends` function to ensure that the `torch` backend is installed and compatible with the specified environment.\n\n### Notable Dependencies or Requirements\n\n*   External dependency: The class requires the `torch` backend to be installed and accessible for data processing tasks.\n*   Interfaces:\n    *   **requires\\_backends**: A function that checks if a list of backends are available, ensuring compatibility with the current environment."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3114-3118
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3118, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatForImageClassification", "start_line": 3114, "type": "class", "chroma:document": "CODE:\n\nclass NatForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NatForImageClassification Class Summary**\n\n*   **Purpose:** Provides the `NatForImageClassification` class, which implements an image classification model using the specified backend (`torch`).\n*   **Key Details:**\n    *   The class uses the `DummyObject` metaclass.\n    *   Requires the `torch` backend for initialization.\n*   **Implementations:**\n    *   Initializes with optional arguments and keyword arguments.\n*   **Dependencies/Requirements:**\n    *   Implies use of the `torch` library.\n    *   Inherently uses class methods or attributes not explicitly defined in this snippet.\n\nPlease note that without further context, some implementation details are left out. For a more extensive analysis, additional information about class attributes, method implementations or other necessary components would be needed."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3121-3125
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatModel", "start_line": 3121, "type": "class", "chroma:document": "CODE:\n\nclass NatModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`NatModel` class using `DummyObject` metaclass. It initializes with a list of required backends (`[\"torch\"]`) enforced at instantiation.\n\n**Key Details:**\n\n* **Method Signature:** `__init__(*args, **kwargs)`\n* **Implementation:** Initializes instance with backend requirement check.\n\n**Notable Dependencies/Requirements:**\n\n* `DummyObject` (metaclass)\n* `requires_backends` function\n* External dependency: `torch` (backend)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3128-3132
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3132, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatPreTrainedModel", "start_line": 3128, "type": "class", "chroma:document": "CODE:\n\nclass NatPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`NatPreTrainedModel` class:\n--------------------\n\n*   **Purpose:** Provides a pre-trained model using the `torch` backend.\n*   **Initial Setup:** Requires the `torch` backend for initialization; it does not support other backends.\n\n**Key Implementation Details:**\n\n*   Uses `DummyObject` metaclass.\n*   Defines `_backends` attribute with `\"torch\"` as the only supported backend.\n\n**Notable Dependencies/Requirements:**\n\n*   Relies on `requires_backends` function for backend handling.\n*   Inherently requires the `torch` library."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3135-3139
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForMaskedLM", "start_line": 3135, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaForMaskedLM` class is a PyTorch-based masked language model (MLM) implementation. It establishes the necessity of using the \"torch\" backend and initializes the object.\n\n**Key Highlights:**\n\n*   Metaclass: `DummyObject`\n*   Required Backends: \"torch\"\n*   Method Signatures:\n    *   `requires_backends(self, [...])`: Verifies that at least one required backend (\"torch\") is available.\n*   Inherited Interfaces: None"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3142-3146
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3146, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForMultipleChoice", "start_line": 3142, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForMultipleChoice` is a PyTorch-based multiple-choice classification model using the `DummyObject` metaclass.\n\n**Purpose:**\nCreate a model instance with specified backend support for torch.\n\n**Implementation Details:**\n- Uses `_backends` class attribute to store supported backends.\n- Initializes model with variable arguments (`*args`) and keyword arguments (`**kwargs`) in its constructor, ensuring proper backend validation via `requires_backends`.\n\n**Notable Dependencies/Requirements:**\n\n- Torcha (PyTorch) installed as a required backend."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3149-3153
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3153, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForNextSentencePrediction", "start_line": 3149, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForNextSentencePrediction`\n\n**Purpose and Functionality:** \nCustom class implementing a neural network model for next sentence prediction.\n\n**Key Implementation Details:**\n\n* Requires a specific backend (`\"torch\"`) for initialization.\n* Utilizes the `requires_backends` function to enforce this requirement.\n\n**Notable Dependencies or Requirements:**\n\n* Backend dependency on `\"torch\"`.\n* Inheritance and implementation of unspecified interfaces."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3156-3160
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3160, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForPreTraining", "start_line": 3156, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `NezhaForPreTraining` class is a PyTorch-based neural network model designed for pre-training tasks. Its primary purpose is to initialize and verify that the required backends are supported.\n\n**Breakdown**\n\n*   **Main Purpose**: Verifies required backends support.\n*   **Key Implementation Details**:\n    *   Uses `DummyObject` as its metaclass, implying a minimal setup or placeholder for more advanced initialization logic in future versions.\n    *   Initializes itself with various arguments using the `self` parameter.\n    *   Calls the `requires_backends` method to check if the necessary backends (\"torch\" in this case) are available before proceeding further.\n*   **Notable Dependencies/Requirements**:\n    *   Requires PyTorch as its backend, referred to by `self requires_backends[\"torch\"]`.\n    \nIn a RAG system, this summary would be used to provide context about the purpose and key implementation details of the `NezhaForPreTraining` class for developers working on or interacting with the codebase."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3163-3167
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3167, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForQuestionAnswering", "start_line": 3163, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForQuestionAnswering`\n\n**Purpose:** \nImplement a question answering model using the `Nezha` library and PyTorch backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with required `torch` backend.\n\n**Notable Dependencies/Requirements:**\n\n* `Nezha` library\n* `torch`\n* (`DummyObject` metaclass)\n\n**Additional Notes:** \nThis class inherits from `DummyObject` metaclass and uses the `requires_backends` function to verify if the system has a compatible backend."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3170-3174
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3174, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForSequenceClassification", "start_line": 3170, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NezhaForSequenceClassification Class Summary**\n\n### Main Purpose and Functionality\nThe `NezhaForSequenceClassification` class is implemented using PyTorch as its backend. It's designed for sequence classification tasks.\n\n### Key Implementation Details\n\n*   Initializes with `requires_backends` method, which checks if the required `\"torch\"` backend is supported.\n*   Uses a `DummyObject` metaclass.\n\n### Notable Dependencies or Requirements\n\n*   Requires the PyTorch library (`_backends = [\"torch\"]`)\n*   Replaces the need for importing PyTorch libraries explicitly within the class."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3177-3181
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3181, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForTokenClassification", "start_line": 3177, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`NezhaForTokenClassification` is a custom token classification model class using the `torch` backend.\n\n*Primary Functionality:* This class represents a PyTorch-based tokenize classification model.\n*Key Implementation Details:* The `__init__` method enforces the use of `torch` as a required backend for this model, ensuring compatibility with the chosen framework.\n**Notable Dependencies or Requirements:** \n- `torch`: A popular deep learning framework used within this model.\n\n(Note: DummyObject metaclass is used for demonstration purposes and not recommended for actual usage. In general, `metaclass` is typically set to `object`)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3184-3188
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3188, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaModel", "start_line": 3184, "type": "class", "chroma:document": "CODE:\n\nclass NezhaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaModel` class initializes with `torch` backend support. It requires the presence of either `\"torch\"` in its `backends` parameter through the `requires_backends` function.\n\n**Key Details:**\n\n* **Function Signatures:**\n\t+ `__init__(self, *args, **kwargs)` initializes the model instance.\n* **Backing:** Uses `torch` as a backend.\n* **Dependencies/Requirements:**\n\t+ External dependency on `torch`.\n\nNote: `DummyObject` is not analyzed as it's used solely as a metaclass and does not contribute to the class functionality."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3191-3195
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3195, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaPreTrainedModel", "start_line": 3191, "type": "class", "chroma:document": "CODE:\n\nclass NezhaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaPreTrainedModel` class is a PyTorch-based pre-trained model, inheriting from `DummyObject`. It supports the \"torch\" backend. The constructor initializes the model with required dependencies for the 'torch' backend.\n\n**Key Details:**\n\n* Primary purpose: Pre-trained model for PyTorch\n* Implementation details:\n  + Requires the \"torch\" backend\n* Notable dependencies:\n    * `torch`\n    * `DummyObject` (inherited from)\n* Dependencies:\n  + `requires_backends(self, [\"torch\"])`\n\nNote: Without additional code context, it's difficult to determine specific implementation details. However, this summary should provide a clear understanding of the model's purpose and dependencies."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3198-3202
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3202, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaForCausalLM", "start_line": 3198, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `OpenLlamaForCausalLM`**\n\n*Purpose:* Encapsulates the Open Llama model for Causal Language Modeling.\n\n**Main Purpose and Functionality:**\nThe class is designed to work with the torch backend, providing an interface for causal language modeling tasks. It initializes itself with various optional arguments.\n\n**Key Implementation Details:**\n\n* The `__init__` method ensures that the required torch backend is available.\n* The `requires_backends` function (not shown in this snippet) checks if the specified backends are installed and configured properly.\n\n**Notable Dependencies or Requirements:**\n\n* Torch backend (`torch`)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3205-3209
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3209, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaForSequenceClassification", "start_line": 3205, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`OpenLlamaForSequenceClassification` class.\n\n**Main Purpose and Functionality:**\nA PyTorch-based sequence classification model wrapper.\n\n**Key Implementation Details:**\n\n* Initializes the model with specific backend requirements (`torch`)\n* Calls `requires_backends` function to validate backend support\n\n**Notable Dependencies and Requirements:**\n\n* torch library (backend)\n\nNote: The metaclass used is `DummyObject`, but its purpose is not explicitly mentioned in the provided code snippet."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3212-3216
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3216, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaModel", "start_line": 3212, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `OpenLlamaModel`**\n\n**Main Purpose and Functionality:** \nThe `OpenLlamaModel` class is an implementation of a machine learning model using the OpenLMMA framework. It initializes the model with support for the PyTorch backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with optional arguments (`args`) and keyword arguments (`kwargs`).\n* Verifies that the PyTorch backend is required for this model instance using the `requires_backends` function.\n\n**Notable Dependencies or Requirements:**\n\n* Uses the OpenLMMA framework.\n* Requires PyTorch as a backend implementation."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3219-3223
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3223, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaPreTrainedModel", "start_line": 3219, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `OpenLlamaPreTrainedModel` class is a meta-class-based model with a specific backend requirement. It initializes the instance with additional arguments (`*args`, `**kwargs`) and asserts that the \"torch\" backend is required (and implicitly provided within the class's metaclass implementation).\n\n**Key Implementation Details:**\n\n- **Metaclass**: Using the `DummyObject` metaclass to define the `_backends` attribute, implying implicit initialization of the backend.\n- **Backend Requirements**: Asserting the need for the \"torch\" backend during instance creation.\n\n**Notable Dependencies or Requirements:**\n\n- `requites_backends`: A function that enforces a specific backend requirement (in this case, `\"torch\"`)."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3226-3230
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3230, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForMaskedLM", "start_line": 3226, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: QDQBertForMaskedLM**\n\n### Purpose and Functionality\n\n* `QDQBertForMaskedLM` is a masked language model based on BERT, utilizing the PyTorch backend.\n\n### Key Implementation Details\n\n* Initializes the model with support for the \"torch\" backend.\n* Requires both positional and attention masks in the input data.\n\n### Notable Dependencies and Requirements\n\n* Uses `RequiresBackends` to enforce use of the \"torch\" backend.\n* Inherths from a parent class (`DummyObject`) without explicitly mentioning its exact nature."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3233-3237
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3237, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForMultipleChoice", "start_line": 3233, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\nThe `QDQBertForMultipleChoice` class is a custom BERT model for multiple-choice tasks, utilizing the PyTorch backend (`torch`). It inherits from a dummy object metaclass and provides an initializer that requires the 'torch' backend.\n\n**Key Details:**\n\n* **Primary Purpose:** Custom BERT model for multiple-choice tasks.\n* **Implementation Details:**\n\t+ Utilizes the 'torch' backend for inference.\n\t+ The `requires_backends` function is used to enforce the required backend.\n* **Notable Dependencies:**\n\t+ Related classes/functions: None specified in this snippet, but implied by the use of PyTorch and the dummy object metaclass.\n\t+ External dependencies:\n\t\t- 'torch'\n\t+ Implemented interfaces: None specified.\n\n**Context:**\n\nThis class is likely part of a larger NLP project for multiple-choice questions. The custom BERT model would be used to classify input texts as correct or incorrect based on the provided choices."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3240-3244
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3244, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForNextSentencePrediction", "start_line": 3240, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`QDQBertForNextSentencePrediction` class:\n\n* Main purpose: Implementation of next sentence prediction model using BERT, leveraging PyTorch backend (`\"torch\"`).\n* Key implementation details:\n\t+ Uses `torch` as primary backend.\n\t+ Requires `torch` to be present for instantiation (`requires_backends` check).\n* Dependencies:\n\t+ `DummyObject` metaclass (not specified in class definition, assumed to be defined elsewhere)."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3247-3251
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3251, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForQuestionAnswering", "start_line": 3247, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`QDQBertForQuestionAnswering` is a PyTorch-based class for Question Answering (QA) using BERT architecture. The primary purpose is to facilitate QA tasks by providing pre-trained language model weights.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend to be available at instantiation (`requires_backends`)."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3254-3258
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3258, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForSequenceClassification", "start_line": 3254, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`QDQBertForSequenceClassification`: A PyTorch-based sequence classification model, leveraging BERT architecture.\n\n**Key Implementation Details**\n\n* Initializes with `torch` backend requirement\n* Utilizes the BERT architecture for sequence classification tasks\n\n**Notable Dependencies/Requirements**\n\n* `_backends = [\"torch\"]`: Relies on the Torcha library.\n* `requires_backends(self, [\"torch\"])`: Initializes model with Torch as a required dependency.\n\n**Note**: The class relies on BERT architecture but does not provide further details about its internal workings."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3261-3265
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3265, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForTokenClassification", "start_line": 3261, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nQDQBertForTokenClassification Class:\n\n* Primary purpose: A PyTorch-based token classification model inspired by BERT architecture.\n* Implements the `DummyObject` metaclass and relies on the \"torch\" backend.\n\n**Implementation Details:**\n\n* Initializes with arguments `args` and keyword arguments `kwargs`.\n* Requires the \"torch\" backend for instantiation.\n\n**Notable Dependencies/Requirements:**\n\n* Relies on the \"torch\" backend.\n* Uses the ` DummyObject` metaclass, specific to PyTorch."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3268-3272
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertLMHeadModel", "start_line": 3268, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `QDQBertLMHeadModel` class is a model head component for BERT (Bidirectional Encoder Representations from Transformers) with a specific configuration. It utilizes the PyTorch backend.\n\n* Primary purpose: Implements a customized model head for BERT-based language models.\n* Key implementation details:\n\t+ Utilizes `torch` as the backend, specifically through the `DummyObject` metaclass.\n\t+ Initializes with variable arguments (`args`, `kwargs`) via the `__init__` method.\n* Notable dependencies and requirements:\n\t+ Requires the `torch` backend to function."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3275-3279
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3279, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertModel", "start_line": 3275, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `QDQBertModel` class is a PyTorch-based implementation of the BERT algorithm for Natural Language Processing tasks. The model uses the \"torch\" backend and relies on external libraries for compatibility.\n\n**Key Implementation Details**\n\n* Inherents from `DummyObject` metaclass.\n* Requires `\"torch\"` backend for initialization.\n* Uses `requires_backends` function to ensure compatible backends are available.\n\n**Notable Dependencies and Requirements**\n\n* External dependencies:\n\t+ \"torch\"\n* Implemented interfaces:\n\t+ No explicit interfaces mentioned."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3282-3286
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3286, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertPreTrainedModel", "start_line": 3282, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a customized BERT pre-training model class `QDQBertPreTrainedModel`. It inherits from an unspecified parent class using the `DummyObject` metaclass.\n\n**Key Details**\n\n* The model requires the `torch` backend and ensures its availability using the `requires_backends` function.\n* The model's primary purpose is to provide a customized BERT pre-training framework.\n\n**Notable Dependencies/Requirements**\n\n* Backend: `torch`\n* Parent class: unspecified (`DummyObject`)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3289-3290
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3290, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_qdqbert", "start_line": 3289, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_qdqbert(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`load_tf_weights_in_qdqbert` loads weight files into the Qdqbert model. It leverages `load_tf_weights_in_qdqbert` backend to handle Torch weights.\n\n**Detailed Analysis:**\n\n- **Purpose:** Loads pre-trained weight files for the Qdqbert model.\n- **Functionality:**\n  - Utilizes the `load_tf_weights_in_qdqbert` backend specifically designed for handling Torch-related data.\n- **Key Implementation Details:**\n  - The function relies on the `load_tf_weights_in_qdqbert` backend to manage Torch weight loading.\n  - Implementation details related to loading and processing weights are not explicitly defined in this code snippet, but can be inferred through its usage of the backend.\n  \n**Notable Dependencies or Requirements:**\n\n- `load_tf_weights_in_qdqbert` backend for handling Torch-related data.\n- The Qdqbert model architecture (`not directly shown in this code snippet`)."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3293-3297
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3297, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmEmbedder", "start_line": 3293, "type": "class", "chroma:document": "CODE:\n\nclass RealmEmbedder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis `RealmEmbedder` class is designed to embed realm models using the Torch backend. The primary purpose is to handle initialization and backend requirements.\n\n**Key Details:**\n\n* Class Method Signature: `__init__(self, *args, **kwargs)`\n* Backend Requirement Enforcement: The `requires_backends` function checks if the required backend (\"torch\") is provided at instantiation.\n* Inherited Metaclass: `_backends = [\"torch\"]`\n\n**Notable Dependencies/Requirements:**\n\n* Torch frontend is required\n* `DummyObject` metaclass (imported in class definition)"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3300-3304
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3304, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmForOpenQA", "start_line": 3300, "type": "class", "chroma:document": "CODE:\n\nclass RealmForOpenQA(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `RealmForOpenQA` class is a backend-specific implementation for the Realm ORM framework. It defines a single backend, \"torch\", and initializes all instances with this backend.\n\n**Key Details**\n\n* Metaclass: `DummyObject`\n* Backend: `\"torch\"`\n* Initialization:\n\t+ Requires all backends to be set (inherited requirement)\n\t+ Only specifies a required backend: `\"torch\"`\n\n**Notable Dependencies/Requirements**\n\n* Inherited requirements from `requires_backends`:\n\t+ All backends must be set (currently only `\"torch\"` is specified)\n* External dependency:\n\t+ None explicitly stated, but may require Realm ORM framework implementation details"}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3307-3311
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3311, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmKnowledgeAugEncoder", "start_line": 3307, "type": "class", "chroma:document": "CODE:\n\nclass RealmKnowledgeAugEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `RealmKnowledgeAugEncoder` class is a custom encoder for augmented knowledge tasks. It utilizes the `torch` backend and requires it at initialization.\n\n**Key Points:**\n\n* **Class Structure**: Implements `DummyObject` metaclass\n* **Primary Purpose**: Augmented knowledge encoding with `torch` backend\n* **Dependencies**:\n\t+ External Dependency: `torch` (backend)\n* **Implementation Details**:\n\t+ Requires `torch` backend setup in constructor\n\nThis summary aims to capture the essential information about this code chunk, focusing on its purpose, structure, and key dependencies, while omitting generic or unnecessary details."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3314-3318
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3318, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmPreTrainedModel", "start_line": 3314, "type": "class", "chroma:document": "CODE:\n\nclass RealmPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmPreTrainedModel`\n\n* Purpose: A custom PyTorch model class for `realm` framework using pre-trained models.\n\n**Key Implementation Details:**\n\n- Uses only the PyTorch backend.\n- Initializes with any available command-line arguments (`*args`, **kwargs`).\n\n**Dependencies/Requirements:**\n\n- Requires the `torch` backend to be present. \n\nNo notable external dependencies are mentioned, nor does it inherit or implement any known interfaces."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3321-3325
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3325, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmReader", "start_line": 3321, "type": "class", "chroma:document": "CODE:\n\nclass RealmReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\nThe `RealmReader` class serves as an abstraction layer for interacting with various data storage backends. It has a fixed backend of \"torch\" and enforces this requirement during initialization.\n\n**Key Details**\n\n*   Initializes with `requires_backends(self, [\"torch\"])`, ensuring only the specified backend is used.\n*   Uses `DummyObject` as its metaclass, which seems to be a placeholder or mock implementation.\n\n**Notable Dependencies/ requirements**\n\n*   Requires \"torch\" as its supported backend."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3328-3332
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3332, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmRetriever", "start_line": 3328, "type": "class", "chroma:document": "CODE:\n\nclass RealmRetriever(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmRetriever` class:\n\n* Primary purpose: A retriever for retrieving data from various backends, in this case, only `torch`.\n \n* Key implementation details:\n  - Enforces backend requirements using the `requires_backends` function with specific requirements.\n \n\n* Dependencies and requirements:\n  - Utilizes `DummyObject` metaclass.\n  - Requires `torch` as a backend."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3335-3339
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3339, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmScorer", "start_line": 3335, "type": "class", "chroma:document": "CODE:\n\nclass RealmScorer(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmScorer` class with a metaclass of `DummyObject`. The class initializes the backend support for PyTorch.\n\n*   **Purpose:** Provides scoring functionality using specified backends (currently supports PyTorch).\n*   **Key implementation details:**\n    *   Initializes backend support for 'torch' using the `requires_backends` function.\n*   **Notable dependencies and requirements:**\n    *   Uses `DummyObject` metaclass.\n    *   Depends on `requires_backends` function with the keyword argument: \"torch\"\n   "}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3342-3343
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3343, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_realm", "start_line": 3342, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_realm(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `load_tf_weights_in_real` function (corrected spelling) is used to load pre-trained TensorFlow weights specifically for the PyTorch backend. \n\nIt has dependencies on the `load_tf_weights` function and requires the `torch` backend.\n\n**Key Details:**\n\n* Function signature: `load_tf_weights_in_realm(*args, **kwargs)`\n* Backend dependency: `torch`\n* Primary purpose: Loads pre-trained TensorFlow weights for PyTorch."}


created_at: 2025-02-07 11:22:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3346-3350
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3350, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RetriBertModel", "start_line": 3346, "type": "class", "chroma:document": "CODE:\n\nclass RetriBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`RetriBertModel` is a `dummy object metaclass` used to create BERT models, specifically supporting the `torch` backend. The class initializes with required backends, enforcing compatibility.\n\n**Implementation Details**\n\n- Uses `DummyObject` as its metaclass.\n- Requires `torch` backend for initialization.\n\n**Notable Dependencies/Requirements**\n\n* `requires_backends` function, imported but not shown in this code snippet.\n* `torch` backend implementation."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3353-3357
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3357, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RetriBertPreTrainedModel", "start_line": 3353, "type": "class", "chroma:document": "CODE:\n\nclass RetriBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**RetriBertPreTrainedModel Class Summary**\n\n*   **Main Purpose:** A class providing a pre-trained model for the RetriBert architecture, built on top of the `torch` backend.\n*   **Key Implementation Details:**\n    *   Initializes the model with custom arguments (`__init__` method).\n    *   Verifies the availability of the required `torch` backend using `requirements_backends`.\n*   **Notable Dependencies or Requirements:**\n    +   External Dependency: torch\n*   **Additional Notes:** The class utilizes the PyTorch framework (torch backend) and is built using a custom metaclass (`DummyObject`)."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3360-3364
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3364, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Speech2Text2ForCausalLM", "start_line": 3360, "type": "class", "chroma:document": "CODE:\n\nclass Speech2Text2ForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Speech2Text2ForCausalLM` class:\n \n* A PyTorch-based model for speech-to-text tasks using causal Language Models (cLM).\n* Initializes with support for only \"torch\" backend.\n\nKey Implementation Details:\n\n* Requires PyTorch (`requires_backends(self, [\"torch\"])`) for native implementation.\n\nNotable Dependencies/Requirements:\n\n* `torchnet` (dummy metaclass): `DummyObject`\n               "}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3367-3371
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3371, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Speech2Text2PreTrainedModel", "start_line": 3367, "type": "class", "chroma:document": "CODE:\n\nclass Speech2Text2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Speech2Text2PreTrainedModel Class Summary**\n\n*Main Purpose:* A pre-trained model for converting speech to text, utilizing the Torch backend.\n\n**Key Implementation Details:**\n\n* Requires the `torch` backend engine.\n* Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`).\n\n*Note:* The `requires_backends(self, [\"torch\"])` function is not shown in this code snippet."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3374-3378
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3378, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TrajectoryTransformerModel", "start_line": 3374, "type": "class", "chroma:document": "CODE:\n\nclass TrajectoryTransformerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**TrajectoryTransformerModel Summary**\n\n### Purpose and Functionality\nThe `TrajectoryTransformerModel` class is a machine learning model utilizing the `torch` backend for trajectory prediction tasks. It inherits from a metaclass (`DummyObject`) and initializes with optional arguments.\n\nPrimary purpose: Implementing a transformer-based model for trajectory prediction.\n\n### Key Implementation Details\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend for initialization.\n\n### Notable Dependencies/Requirements\n- Uses `torch` as the required backend."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3381-3385
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3385, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TrajectoryTransformerPreTrainedModel", "start_line": 3381, "type": "class", "chroma:document": "CODE:\n\nclass TrajectoryTransformerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise and focused summary of the provided class code chunk:\n\n**TrajectoryTransformerPreTrainedModel Class Summary**\n\nPurpose: A pre-trained transformer model optimized for trajectory processing.\n\nKey Implementation Details:\n\n* Uses the `torch` backend.\n* Requires initialization with compatible backends due to inherited dependencies from the parent `metaclass`.\n\nNotable Dependencies/Requirements:\n\n* `torch`\n* Inherited from `DummyObject`: \n    - `metaclass`"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3388-3392
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3392, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AdaptiveEmbedding", "start_line": 3388, "type": "class", "chroma:document": "CODE:\n\nclass AdaptiveEmbedding(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`AdaptiveEmbedding`\n\nA custom class extending `DummyObject`, used for adaptive embedding functionality. It requires the use of the \"torch\" backend.\n\n**Main Purpose and Functionality:**\n\nUtilizes the `requires_backends` function to enforce the use of the specified backend, in this case, \"torch\". The class facilitates adaptive embedding with the chosen backend.\n\n**Key Implementation Details:**\n\n- Extends `DummyObject`\n- Requires the use of the \"torch\" backend\n\n**Notable Dependencies and Requirements:**\n\n- Uses the `requires_backends` function\n- Relies on the \"torch\" backend"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3395-3399
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3399, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLForSequenceClassification", "start_line": 3395, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `TransfoXLForSequenceClassification` class is a custom transformer-based model. It inherits from a dummy metaclass (`DummyObject`) and is designed for sequence classification tasks.\n\n**Key Details:**\n\n* Primary Purpose: Sequence classification using transformer architecture (`TransfoXL`)\n* Backend Requirement: Supports Torch backend\n* Implementation Details:\n\t+ `requires_backends` function ensures the use of Torch as a required backend\n* Notable Dependencies:\n\t+ Inherits from `DummyObject` metaclass\n\t+ Exposes no external dependencies or interfaces"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3402-3406
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3406, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLLMHeadModel", "start_line": 3402, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`TransfoXLLMHeadModel`\n\nThis class represents a transformer-based language model with the `torch` backend.\n\n**Key Details:**\n\n* Initializes with support for only the \"torch\" backend.\n* Requires backends to be set to \"torch\" during object creation.\n \n**Implementation:**\n\n\nNo additional implementation details are provided in this snippet. However, it is worth noting that a typical implementation would follow the constructor and define subsequent methods or attributes.\n\n \n\nNote: I have preserved the original class name structure, used precise technical terms, and highlighted key algorithm/implementation detail such as the backend requirement to 'torch'."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3409-3413
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3413, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLModel", "start_line": 3409, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`TransfoXLModel` class implements a machine learning model using the `torch` backend. \n\n**Key Details:**\n- Initializes with a list of required backends (`requires_backends` method)\n- Accepted arguments: variable positions and keywords\n- Primary function: setting up the model for execution\n\n**Implementation Insights:**\n- Leverages `DummyObject` as its metaclass.\n- Depends on the `torch` backend for implementation. \n\n**External Dependencies:**\n- `torch`\n- `dummy_object` (presumably a built-in Python module or custom library)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3416-3420
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3420, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLPreTrainedModel", "start_line": 3416, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `TransfoXLPreTrainedModel` is a class implementing a pre-trained transformer model (`TransfoXL`) using the `torch` backend.\n\n**Key Implementation Details:**\n\n* `__init__`: Initializes the model, ensuring it requires only the `torch` backend.\n\t+ Method signature: no explicit signature provided\n* Notable dependencies:\n\t+ `requires_backends(self, [\"torch\"])`\n\n**Notable Inheritance/Interfaces:**\nNone mentioned in this snippet, but likely inherits from a base model class.\n\n**External Dependencies:** \n`torch`, presumably as a required backend."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3423-3424
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3424, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_transfo_xl", "start_line": 3423, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_transfo_xl(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nFunction `load_tf_weights_in_transfo_xl` loads TF weights using TransFOXL library. It relies on the `load_tf_weights_in_transfo_xl` backend, which is required to be configured for use with PyTorch backend.\n\n* Primary purpose: Loads TF weights\n* Key implementation detail: Uses `TransFOXL` library behind the scenes.\n* Notable dependencies:\n\t+ `torch`: backend dependency\n\t+ `load_tf_weights_in_transfo_xl` (backend class): required configuration and utility function."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3427-3431
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3431, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltForAudioVisualClassification", "start_line": 3427, "type": "class", "chroma:document": "CODE:\n\nclass TvltForAudioVisualClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n* Class Name: `TvltForAudioVisualClassification`\n* Primary Purpose: Instantiation of the TVL (Tensor Virtual Lab) model for audio-visual classification using Torch backend.\n* Key Implementation Details:\n\t+ Requires Torch backend (`requires_backends` method)\n\t+ Uses Torch in the construction of the `__init__` method\n* Notable Dependencies/Requirements:\n\t+ Torch framework (`_backends = [\"torch\"]`)\n\t+ An external library or module for using the TVL model (not specified in this snippet)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3434-3438
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3438, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltForPreTraining", "start_line": 3434, "type": "class", "chroma:document": "CODE:\n\nclass TvltForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `TvltForPreTraining`**\n\n* **Main Purpose and Functionality**: A pre-training model class `TvltForPreTraining` that utilizes the Torch backend for its operations.\n\n* **Key Implementation Details**:\n\t+ Initializes with optional arguments (`args`) and keyword arguments (`kwargs`).\n\t+ Sets the required backends to [\"torch\"] using the `requires_backends` method.\n\n* **Notable Dependencies or Requirements**:\n\t+ Inheriting from `DummyObject`.\n\t+ Dependence on the Torch backend.\n\t+ Usage of `requires_backends` method for backend checking."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3441-3445
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3445, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltModel", "start_line": 3441, "type": "class", "chroma:document": "CODE:\n\nclass TvltModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `TvltModel` class utilizes the `torch` backend. The constructor (`__init__`) verifies that the required backend is present before initialization.\n\n**Key Details:**\n\n*   Backend: `torch`\n*   Constructor (Verify) Parameters:\n    *   `self`: reference to the instance\n        +   Used as implicit first argument in Python methods, equivalent to `args[0]` or `kwargs[\"self\"]`.\n     *   `*args` and `**kwargs` for additional arguments (not used)\n\n**Note:** The metaclass is set to `DummyObject`, but its implementation is not shown."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3448-3452
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3452, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltPreTrainedModel", "start_line": 3448, "type": "class", "chroma:document": "CODE:\n\nclass TvltPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `TvltPreTrainedModel` class is a PyTorch-based pre-trained model class. It initializes with the following properties:\n\n*   **Backends:** Supports Python backend.\n*   **Initialization Requirements:**\n    *   Requires the support of at least two backends, specifically Python and Torch (verified by `requires_backends(self, [\"torch\"])` method).\n \n\n**Key Implementation Details:**\n\n*   `_backends = [\"torch\"]`: Specifies the supported backends for this model.\n*   `__init__(self, *args, **kwargs)`: Initializes the class with arguments.\n\n**Notable Dependencies or Requirements:**\n\n*   **pytorch**: The class specifically requires the torch backend to be supported. However, it does not explicitly require pytorch as a separate dependency in its summary; it assumes that 'torch' is included under the pytorch back-end of this problem context.)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3455-3459
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3459, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanForImageClassification", "start_line": 3455, "type": "class", "chroma:document": "CODE:\n\nclass VanForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`VanForImageClassification` is an image classification model class using the `torch` backend.\n\n**Key Implementation Details:**\n\n- Uses the `VAN For` algorithm for image classification.\n- Requires `torch` backend, explicitly specified in the constructor (`self.requires_backends([\"torch\"])`)."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3462-3466
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3466, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanModel", "start_line": 3462, "type": "class", "chroma:document": "CODE:\n\nclass VanModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`VanModel` is an implementation of a vanilla version of a model using the Torch backend, leveraging the `requires_backends` function to validate the required backend.\n\n**Key Implementation Details:**\n\n* Uses the Torch backend (`_backends = [\"torch\"]`)\n* Verifies compatibility with the specified backend in the `__init__` method\n\n**Notable Dependencies/Requirements:**\n\n* Requires the `DummyObject` metaclass\n* Assumes the existence of a `requires_backends` function for validation\n\nNo external dependencies are listed."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3469-3473
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3473, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanPreTrainedModel", "start_line": 3469, "type": "class", "chroma:document": "CODE:\n\nclass VanPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nClass `VanPreTrainedModel` extends `DummyObject`. It inherits from the following back-end:\n\n*   torch\n\nThe `__init__` method ensures that the required back-end is present (`torch`) at instantiation.\n\nKey aspects:\n\n*   Class name: `VanPreTrainedModel`\n*   Back-end support: `torch`\n\n**Detailed Code:**\n\n```python\nclass VanPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n**Dependencies/Requirements:**\n\n*   torch (back-end)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3476-3480
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3480, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridForImageClassification", "start_line": 3476, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ViTHybridForImageClassification` class is an image classification model using the PyTorch backend. It enforces the use of the PyTorch backend during initialization.\n\n**Key Details:**\n\n* Class: `ViTHybridForImageClassification`\n* Backend: PyTorch (`self._backends = [\"torch\"]`)\n* Primary Purpose: Image Classification\n\n**Implementation Details:**\n\n* Initialization Method: `__init__` with variable arguments and keyword arguments, enforcing the presence of the PyTorch backend.\n\n**Notable Dependencies:**\n\n* Backend: PyTorch (`requires_backends(self, [\"torch\"])`)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3483-3487
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3487, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridModel", "start_line": 3483, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ViTHybridModel` class is a hybrid machine learning model implementation using the PyTorch backend. It inherits from a metaclass `DummyObject`.\n\n*   **Purpose**: The primary purpose of this class is to provide a framework for creating and training hybrid machine learning models.\n*   **Key Implementation Details**:\n    *   The model requires at least one backend, which is \"torch\" in this case.\n*   **Notable Dependencies or Requirements**:\n    *   `requires_backends` function with PyTorch as one of the required backends.\n\n(Note: No external dependencies are explicitly listed, assuming PyTorch and the `DummyObject` metaclass are already installed.)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3490-3494
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3494, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridPreTrainedModel", "start_line": 3490, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ViTHybridPreTrainedModel` class is a hybrid model based on the ViT architecture. It has Torch as its primary backend.\n\n**Key Details:**\n\n* Primary purpose: Implementing Hybrid Vision Transformer (ViT) model with Torch backend\n* Important method signature:\n  - `__init__`: Initializes the model, including dependency check on Torch backend\n* Notable dependencies:\n  - `torch` (backend)\n  - `DummyObject` metaclass\n\n**Implementation Insights:**\n\nThe class uses a metaclass (`DummyObject`) and includes a check for required Torch backend in its constructor."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3497-3501
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3501, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetDecoder", "start_line": 3497, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetDecoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`XLMProphetNetDecoder`\n\n*Purpose:* A decoder class for ProphetNet models in the XLM framework.\n \n**Key Implementation Details:**\n- Initializes with `requires_backends` method to ensure required backend is installed (`torch`).\n- Utilizes `DummyObject` metaclass, but its actual functionality remains undefined.\n\n**Notable Dependencies/Requirements:**\n\n* Backend: `torch`\n \n**Note:** This analysis only covers the provided code snippet and may not include all possible implementation details or usage contexts."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3504-3508
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3508, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetEncoder", "start_line": 3504, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\nXLMProphetNetEncoder Class\n\n*   A class that leverages the PyTorch backend for encoding tasks.\n*   Initializes with configuration arguments `*args` and `**kwargs`.\n*   Requires the PyTorch backend to be installed (`requires_backends(self, [\"torch\"])`). \n\n**Key Details:**\n\n*   The class uses a metaclass (`DummyObject`) but its purpose is unknown without more context or implementation details.\n*   The `requires_backends` function (not shown) implies some backend-specific requirements.\n\n**Dependencies and Requirements:**\n\n*   PyTorch library for encoding tasks (`\"torch\"`).\n*   `requires_backends` function, possibly implementing backend installation checks."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3511-3515
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3515, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetForCausalLM", "start_line": 3511, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise summary of the given class code chunk:\n\n**Class XLMProphetNetForCausalLM**\n\n*   Main purpose: Provides an implementation of a neural network model using the ProphetNet architecture, specifically for causal language models (CaLMs), on top of the Transformers library with PyTorch backend.\n*   Key Implementation Details:\n    *   Uses the ProphetNet architecture.\n    *   Requires PyTorch as its backend.\n    *   Initializes the model instance with variable arguments and keyword arguments (`*args` and `**kwargs`).\n*   Notable Dependencies:\n    *   `torch`: Required backend library for this model.\n\nNote: As the provided code does not define any methods, attributes or interfaces, no additional information can be extracted from it."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3518-3522
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3522, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetForConditionalGeneration", "start_line": 3518, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`XLMProphetNetForConditionalGeneration`: A PyTorch-based model (backend: `torch`) for conditional generation.\n\n**Key Features**\n\n- Class uses `DummyObject` metaclass\n- Reqs backend support for torch (`requires_backends(self, [\"torch\"])`)\n\nNo implementation details are provided. The code initializes the class with optional arguments `*args` and `**kwargs`."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3525-3529
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3529, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetModel", "start_line": 3525, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `XLMProphetNetModel` class is a custom neural network model using the PyTorch backend. It initializes with the specified backends (`\"torch\"`). The model requires the PyTorch library for its computation.\n\n**Detailed Analysis:**\n\n*   **Main Purpose and Functionality:** This class represents a custom Prophet Net model, which is likely used in Natural Language Processing (NLP) applications.\n*   **Key Implementation Details:**\n    *   `requires_backends(self, [\"torch\"])`: Verifies that the PyTorch backend is available before creating an instance of the model.\n    *   Backend: `torch` - Specifies that this model uses the PyTorch library for its computation.\n*   **Notable Dependencies or Requirements:**\n    *   `DummyObject` (metaclass): Used to define the metaclass for the `XLMProphetNetModel` class. This indicates a custom approach to creating instances of this model.\n    *   `Provides` (method call): Used to create an instance of this model, ensuring that the required backends are available before instantiation.\n\n**RAG System Summary:**\n\n*   Primary Purpose: Custom Prophet Net model using PyTorch backend\n*   Key Implementation Details: requires_backends with torch backend dependency.\n*   Notable Dependencies: DummyObject metaclass and provides method."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3532-3536
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3536, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetPreTrainedModel", "start_line": 3532, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `XLMProphetNetPreTrainedModel` class is a PyTorch-based Prophet Net pre-trained model, utilizing the `DummyObject` metaclass. It has two key aspects:\n\n*   Requires PyTorch as its backend (`torch`) for execution.\n*   Initializes with generic input parameters (`args`, `kwargs`)."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3539-3543
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3543, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DepthAnythingForDepthEstimation", "start_line": 3539, "type": "class", "chroma:document": "CODE:\n\nclass DepthAnythingForDepthEstimation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DepthAnythingForDepthEstimation`**\n\n**Main Purpose and Functionality**: A class providing depth estimation functionality for various backends.\n\n**Key Implementation Details**:\n\n* Initializes instance with required backend support using the `requires_backends` method.\n* Enforces use of 'torch' backend.\n\n**Notable Dependencies or Requirements**:\n\n* Backend dependency: torch\n* Interface implemented: `requires_backends` (implied, but not explicitly defined in this code snippet)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3546-3550
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3550, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DepthAnythingPreTrainedModel", "start_line": 3546, "type": "class", "chroma:document": "CODE:\n\nclass DepthAnythingPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DepthAnythingPreTrainedModel` is a custom model class that inherits from `DummyObject`. It uses the PyTorch backend. The class has an `__init__` method that requires the `torch` backend, with no additional arguments.\n\n**Key aspects:**\n\n* Metaclass: ` DummyObject`\n* Backend: `PyTorch`\n* Primary purpose: Pre-trained model implementation\n* Important functions:\n\t+ `requires_backends(self, [\"torch\"])`: Verifies if the required `torch` backend is available.\n\nNo external dependencies are explicitly listed. However, it is implied that PyTorch is required to be installed and imported (`import torch`) for this class to function properly."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3553-3557
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3557, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrForObjectDetection", "start_line": 3553, "type": "class", "chroma:document": "CODE:\n\nclass DetrForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DetrForObjectDetection` is an object detection class using PyTorch as its backend.\n\n**Key Features:**\n\n* Initializes with optional arguments `args` and `kwargs`\n* Requires PyTorch backend (`\"torch\"`)\n\n**Implementation Details:**\n\n* The `requires_backends` method is used to specify the required backend.\n \n**Notable Interfaces and Dependencies:**\n\n* `DummyObject`: a metaclass used by the class\n* `require_backends` function: requires a specific backend for object detection"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3560-3564
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3564, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrForSegmentation", "start_line": 3560, "type": "class", "chroma:document": "CODE:\n\nclass DetrForSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DetrForSegmentation` is a PyTorch-based segmentation model class. It leverages the `DummyObject` metaclass and relies on the `torch` backend.\n\n**Key Implementation Details:**\n\n* Enforces the use of the `torch` backend during initialization.\n* Utilizes the `requires_backends` function to validate backend requirements.\n\n**Notable Dependencies:**\n\n* ` DummyObject`: metaclass used for the class definition (not a dependency in terms of external libraries, but rather a custom class).\n* `torch`: required backend for this model."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3567-3571
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3571, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrModel", "start_line": 3567, "type": "class", "chroma:document": "CODE:\n\nclass DetrModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: DetrModel**\n\n*   **Main Purpose**: The `DetrModel` class is an implementation of a detaction model using the `torch` backend.\n\n**Key Implementation Details**\n\n*   It uses the `requirements_backends` function to ensure that only `torch` is supported.\n*   Initializes the object with arguments (`args`) and keyword arguments (`kwargs`).\n\n**Notable Dependencies or Requirements**\n\n*   Requires the `torch` backend.\n*   Inherited from `DummyObject`, a metaclass."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3574-3578
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3578, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrPreTrainedModel", "start_line": 3574, "type": "class", "chroma:document": "CODE:\n\nclass DetrPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DetrPreTrainedModel` class is a custom model base class that inherits from the `DummyObject` metaclass. It has two notable features:\n\n* Required backend support for \"torch\" as indicated by `requires_backends`.\n* The class defines no specific implementation details, only setting up requirements.\n\n **Key Details:**\n\n- Inherited Metaclass: `DummyObject`\n- Primary Dependency: Supports \"torch\" backend\n\n**Notable Dependencies/Requirements:**\n\n- `DummyObject` metaclass\n- Torch backend (required)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3581-3585
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3585, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForCausalLM", "start_line": 3581, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DiffLlamaForCausalLM`\n\n**Main Purpose and Functionality:**\nInstantiates a custom diffusion model (`DiffLlama`) designed for causal language models.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend during initialization.\n\n**Notable Dependencies or Requirements:**\n\n* `DummyObject` metaclass\n* `requires_backends` function (imported but not shown in this snippet)\n* `torch` library (backend)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3588-3592
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3592, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForQuestionAnswering", "start_line": 3588, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\nThe `DiffLlamaForQuestionAnswering` class is an implementation of the Hugging Face model using Torch backend (`torch`), specifically designed for question answering tasks. It inherits from `DummyObject`.\n\n**Key Implementation Details:**\n\n* Initializes the object with *args and **kwargs\n* Requires Torch backend to be present during initialization, checking if it's already available\n\n**Notable Dependencies/Requirements:**\n\n* Torch (`\"torch\"`); requires its presence for proper backend setup."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3595-3599
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3599, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForSequenceClassification", "start_line": 3595, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`DiffLlamaForSequenceClassification` is a PyTorch-based sequence classification model variant of the DiffLaframodel. Its primary purpose is to leverage the strengths of both diffusion and language models.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires compilation at initialization, necessitating the use of the `requires_backends` function.\n\n**Notable Dependencies and Requirements:**\n\n* `DummyObject`: Metaclass used for instantiation.\n* `DiffLlamaForSequenceClassification` class itself serves as a required backend."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3602-3606
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3606, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForTokenClassification", "start_line": 3602, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DiffLlamaForTokenClassification` class is a tokenizer-specific model that leverages the `DiffLlama` architecture for token classification tasks. The primary purpose is to classify tokens into predefined categories.\n\n**Key Implementation Details**\n\n* Utilizes the `torch` backend.\n* Ensures compatibility with the `DiffLlama` framework by requiring the `torch` backend.\n\n**Notable Dependencies and Requirements**\n\n* Relies on the `DiffLlama` architecture from the `torch` backend."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3609-3613
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3613, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaModel", "start_line": 3609, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DiffLlamaModel` class is a model implementation specifically designed for the DiffLMLaSa (Dilated Llama for Long-distance Short-Attention) library, utilizing the PyTorch backend (`torch`). The class features an `__init__` method that requires the `torch` backend to be present before initialization.\n\n**Key Details:**\n\n* Primary Purpose: implements a model for DiffLMLaSa\n* Key Implementation Detail:\n\t+ Requires `torch` backend at initialization time (`requires_backends`)\n* Important Method Signature:\n\t+ `__init__`\n* Notable Dependencies and Requirements:\n\t+ `torch`\n\t+ `DummyObject` (class metaclass)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3616-3620
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3620, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaPreTrainedModel", "start_line": 3616, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n```markdown\nDiffLlamaPreTrainedModel (metaclass=DummyObject)\n\n  Primary Purpose:\n  - Pre-trained model for DiffLlama sequence-to-sequence task\n\n  Implementation Details:\n\n  - Initializes with required backend (torch)\n```\n\n**Key Algorithm/Pattern:** Use of `requires_backends` function to enforce specific backend support (in this case, torch)\n\n**Method Signatures:**\n\n- `__init__`: Initializes the model instance\n\n**Notable Dependencies:**\n\n- Related classes/functions: None\n- External dependencies:\n  - `torch`\n  - `DummyObject` (metaclass)\n```"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3623-3627
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3627, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatBackbone", "start_line": 3623, "type": "class", "chroma:document": "CODE:\n\nclass DinatBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DinatBackbone` class is a meta-class that inherits from `DummyObject`, specifying it as a backbone for Dinat models. It requires the use of only one backend: torch.\n\n**Key Details**\n\n* **Class Hierarchy**: `DinatBackline -> DummyObject`\n* **Backend Specification**: Requires the use of \"torch\" backend\n* **Initialization Method**: Calls `requires_backends` with a list of required backends, which is satisfied by the single requirement [\"torch\"].\n\n\n**External Dependencies**\n\n* Related method: `requires_backends`\n* Backend: torch"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3630-3634
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3634, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatForImageClassification", "start_line": 3630, "type": "class", "chroma:document": "CODE:\n\nclass DinatForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DinatForImageClassification` is a PyTorch-based class for image classification using Dinat (Diffusion-based Neural Architecture for Image and Text Generation) architecture. It initializes with required backend support (`torch`) and performs model setup.\n\n\n**Key Implementation Details**\n\n*   Initializes `self` with `args` and `kwargs`\n*   Verifies backup support is available for the \"torch\" backend\n*   Requires additional backend support is not explicitly handled in this code snippet\n\n\n**Notable Dependencies or Requirements**\n\n*   PyTorch library (`torch`)\n*   `requires_backends` function (backend agnostic model setup)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3637-3641
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3641, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatModel", "start_line": 3637, "type": "class", "chroma:document": "CODE:\n\nclass DinatModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DinatModel Class Summary**\n\n*   **Main Purpose and Functionality:**\n    *   `DinatModel` is a class using the `DummyObject` metaclass, primarily designed for use with the `torch` backend.\n*   **Key Implementation Details:**\n    *   The `__init__` method initializes the model instance by requiring the presence of the `\"torc\"` backend (corrected from \"torch\") using the `requires_backends` function. This implies that this class is intended for use with PyTorch-based models.\n*   **Notable Dependencies or Requirements:**\n    *   The `DinatModel` class uses the `DummyObject` metaclass, which suggests a custom metaclass implementation.\n    *   Although not explicitly stated, it is assumed to rely on related classes/functions available in the environment where this code is executed.\n\nCorrected note:\nThe backend name used by DinatModel was mistakenly typed as \"torc\". The correct spelling is indeed \"torch\"."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3644-3648
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3648, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatPreTrainedModel", "start_line": 3644, "type": "class", "chroma:document": "CODE:\n\nclass DinatPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Dinat Pre-Trained Model**\n\n### Purpose and Functionality\nThis class implements a pre-trained Dinat model for use with the specified backend(s). The `DummyObject` metaclass is used. \n\n### Key Implementation Details\n\n- Requires PyTorch (torch) as the primary backend.\n- Initializes the model using variable arguments (`*args`) and keyword arguments (`**kwargs`). \n- Verifies the presence of the \"torch\" backend with the `requires_backends` function.\n\n ### Notable Dependencies/Requirements\n - Torchtensor backend (\"torchtensor\")"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3651-3655
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3655, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2Backbone", "start_line": 3651, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2Backbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DinoV2Backbone`\n\n*   Class metaclass: `DummyObject`\n*   Primary purpose: A backbone class for integrating the DinoV2 model with various backends.\n*   Initializes the class by requiring at least one of the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   Checks if either of the 'backends' attributes are set to include \"torch\".\n\n**Notable Dependencies and Requirements:**\n\n*   `DummyObject`: used as a metaclass for the class.\n*   No external dependencies are listed."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3658-3662
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3662, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2ForImageClassification", "start_line": 3658, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2ForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`Dinov2ForImageClassification`\n\n**Purpose:** A PyTorch-based class for image classification using Dynet's Dinov 2 optimization framework.\n\n**Main Functionality:** Initializes the model and checks required backends (currently only supports \"torch\") before construction.\n\n**Notable Implementation Details:**\n\n* Requires backend support via `requires_backends` method to ensure exclusive use of PyTorch.\n* Utilizes `DummyObject` metaclass for class definition.\n\n**Key Dependencies:**\n\n* `torch`\n\n**External Requirements:** Specifically, requires Python implementation of PyTorch."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3665-3669
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3669, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2Model", "start_line": 3665, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`Dinov2Model` is a class that initializes a model using the `torch` backend (`_backends = [\"torch\"]`) and enforces the use of this backend through the `requires_backends` decorator.\n\n**Key Details:**\n\n- Initializes the model using the `__init__` method.\n- Enforces the use of the `torch` backend with the `requires_backends` decorator.\n\n**Notable Dependencies/Requirements:**\n\n- `DummyObject` metaclass\n- `requires_backends` decorator\n- `torch` (backend)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3672-3676
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3676, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2PreTrainedModel", "start_line": 3672, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Dinov2PreTrainedModel**\n\n**Purpose:** A pre-trained model class for Dinov2.\n\n**Primary Functionality:** Instantiates the `Dinov2PreTrainedModel` class with required backend setup.\n\n**Key Implementation Details:**\n\n- **`__init__` Method:** Initializes the model by checking and setting up the `torch` backend using `requires_backends`.\n\n**Notable Dependencies/Requirements:**\n\n- Requires TensorFlow version 3.x for compatibility with `torch`\n- Inherently requires backend configuration (`_backends = [\"torch\"]`)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3679-3683
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3683, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersBackbone", "start_line": 3679, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `Dinov2WithRegistersBackbone` class is a PyTorch-based neural network architecture with registers. It requires the \"torch\" backend and initializes with any additional keyword arguments.\n\n**Key Features:**\n\n* **Backend:** PyTorch\n* **Class:** `Dinov2WithRegistersBackbone`\n* **Purpose:** Neural Network Architecture\n* **Primary Method Signatures:** `__init__`\n\nNote:\n\n* `DummyObject` is used as the metaclass.\n* `requires_backends` function is imported but not defined in this chunk."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3686-3690
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3690, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersForImageClassification", "start_line": 3686, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Dinov2WithRegistersForImageClassification` class is a custom neural network model implemented using the PyTorch backend. It defines an initializer function that checks for the required 'torch' backend and performs some initialization steps.\n\n**Key Highlights:**\n\n* Primary purpose: Implementing a neural network model for image classification.\n* Backend:torch\n* Initialization Requirements:\n\t+ `requires_backends` method with argument \"torch\"\n\nNo notable dependencies or features are provided outside of this snippet."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3693-3697
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3697, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersModel", "start_line": 3693, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Dinov2WithRegistersModel` is a class model that:\n\n* **Primary Purpose:** Implements a PyTorch backend.\n\n* **Key Implementation Details:**\n\t+ Requires the `requires_backends` method to ensure it uses only the `torch` backend.\n\t+ Has an empty interface (due to `_backends` assignment) that defines its backend dependency.\n\n* **Dependencies:**\n\t+ Referred Class/Function: `requires_backends`. \n\t+ External Dependency: PyTorch."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3700-3704
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3704, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersPreTrainedModel", "start_line": 3700, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nDinoV2 Pre-Trained Model Class\n-----------------------------\n\n*   **Main Purpose and Functionality:** Dinov2WithRegisters is a pre-trained model class built using the `DummyObject` metaclass. It inherits requirements for the `torch` backend.\n\n### Key Implementation Details:\n\n*   The `__init__` method:\n    *   Requires the `torch` backend.\n    *   Initializes the instance with arbitrary arguments (`args`) and keyword arguments (`kwargs`).\n\n### Notable Dependencies or Requirements:\n\n*   The model requires the `torch` backend for execution."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3707-3711
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3711, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForMaskedLM", "start_line": 3707, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForMaskedLM`**\n\n* **Purpose:** A PyTorch-based implementation of DistilBert model for masked language modeling tasks, utilizing the TPU backend.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` backend using `requires_backends` method.\n* Utilizes `DummyObject` metaclass for customization.\n\n**Notable Dependencies and Requirements:**\n\n* Relies on `torch` tensor computation library.\n* Inheriting from a hypothetical `metaclass=DummyObject` class, but no further details provided."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3714-3718
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3718, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForMultipleChoice", "start_line": 3714, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary**\n\n`DistilBertForMultipleChoice` is a machine learning model class utilizing the PyTorch backend. It inherits from `DummyObject`.\n\n**Attributes:**\n- `_backends`: set to [\"torch\"]\n\n**Initialization:**\nThe model requires the PyTorch backend.\n\n**Implementation Details:**\nThe class does not specify any unique implementation details, but it builds upon the provided backends and initializes itself with generic data structure `*args` and keyword arguments (`**kwargs`)."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3721-3725
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3725, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForQuestionAnswering", "start_line": 3721, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForQuestionAnswering`**\n\n**Purpose and Functionality:**\nA PyTorch-based implementation of DistilBERT for question answering tasks.\n\n**Key Implementation Details:**\n\n* Initialized with support for the \"torch\" backend using `requires_backends`.\n* Note: The specific requirements and dependencies are not explicitly stated in this code snippet.\n\n**Notable Dependencies or Requirements:**\n\n* Related classes/functions not specified due to limited information.\n* Inherited/implemented interfaces:\n\t+ torch (backend)\n* External dependencies: \n\t- DistilBERT model (not shown in this snippet)"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3728-3732
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3732, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForSequenceClassification", "start_line": 3728, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DistilBertForSequenceClassification` is a sequence classification model based on the DistilBERT architecture. It uses the PyTorch backend.\n\n**Key Details:**\n\n- Models `distilbert-base-uncased` for tokenization\n- Uses `torch.nn.Module` as base class\n- Requires `torch` and `tokenizerclasses.Tokenizer` backends\n\n**Implementation Focus:**\n\nThe model's initialization (`__init__`) method checks if the required PyTorch backend is present, ensuring compatibility before proceeding.\n\n*   **Requires Backends:** Verifies the presence of the 'torch' backend to ensure compatibility.\n*   **Tokenization:** Utilizes `distilbert-base-uncased` for efficient tokenization of input data.\n\nThis class inherits from `DummyObject` which is likely used for metaclass purposes in Django."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3735-3739
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3739, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForTokenClassification", "start_line": 3735, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForTokenClassification`**\n\n* **Main Purpose and Functionality:** \nThe `DistilBertForTokenClassification` class is an implementation of a Token Classification model built on top of DistilBERT, using the PyTorch backend.\n\n* **Key Implementation Details:**\n\t+ Requires the \"torch\" backend.\n\t+ Utilizes the `DummyObject` metaclass for class definition customization.\n\n* **Dependencies and Requirements:**\n\t+ Associated with the PyTorch backend (\"torch\").\n\t+ Inherently uses the `requires_backends` function.\n \n* **Notable Interfaces/Methods:** \nNone specified within this provided code snippet."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3742-3746
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3746, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertModel", "start_line": 3742, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DistilBertModel` is a PyTorch-based implementation of the DistilBERT model using a `DummyObject` metaclass. The class initializes with support for the \"torch\" backend.\n \n**Key Details**\n- Primary Functionality: Implementing the DistilBERT model\n- Key Algorithms/Patterns: Uses the PyTorch framework\n\n**Notable Dependencies/Requirements**\n\n- Related Class: `requires_backends`\n- External Dependency: Python's `torch` library"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3749-3753
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3753, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertPreTrainedModel", "start_line": 3749, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DistilBertPreTrainedModel` class is a Python implementation of a pre-trained DistilBERT model using the PyTorch backend. It initializes itself with a set of required backends (`torch`) and allows for general-purpose arguments (`*args` and `**kwargs`). \n\n**Details:**\n\n* **Main purpose:** Implements a pre-trained DistilBERT model\n* **Purpose statement:** Primary function: [model initialization]\n* **Implementation details:**\n\t+ Utilizes the PyTorch backend (`torch`)\n\t+ Verifies that the required backend is available (`requires_backends` method)\n* **Notable dependencies:**\n\t+ Related class/functions: `DummyObject`, `requires_backends`\n\t+ External dependencies:\n\t\t- PyTorch (backend)\n\nNote: The `metaclass=DummyObject` and the `_backends = [\"torch\"]` lines are used for internal implementation purposes, which do not directly affect the external users of the class."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3756-3760
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3760, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DonutSwinModel", "start_line": 3756, "type": "class", "chroma:document": "CODE:\n\nclass DonutSwinModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n### DonutSwin Model Class\n\n*   **Principal Functionality**: Represents a DonutSwin model with support for the \"torch\" backend.\n*   **Implementation Details**:\n    *   Initializes the model using `requires_backends` method, which ensures the \"torch\" backend is supported.\n*   **Notable Dependencies**:\n    *   Extensively relies on the `DummyObject` metaclass.\n    *   Requires the `torch` backend implementation to be provided."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3763-3767
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3767, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DonutSwinPreTrainedModel", "start_line": 3763, "type": "class", "chroma:document": "CODE:\n\nclass DonutSwinPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DonutSwinPreTrainedModel` class is a PyTorch-based pre-trained model, utilizing the Swin transformer architecture. Its main functionality involves providing an implementation of the pre-trained Donut-Swin model using Meta-Transformers framework.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `\"torch\"` backend in its initialization (`requires_backends(self, [\"torch\"])`).\n\n**Notable Dependencies or Requirements:**\n\n* The class relies on the `DummyObject` metaclass and `meta_transformer` library for execution.\n\nPlease note that without further context, specifics about the Donut-Swin model and Meta-Transformers framework cannot be precisely determined."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3770-3774
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3774, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRContextEncoder", "start_line": 3770, "type": "class", "chroma:document": "CODE:\n\nclass DPRContextEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DPRContextEncoder` class is a Python subclass of `DummyObject`. It initializes with optional positional and keyword arguments.\n\n**Key Takeaways**\n\n* Primary purpose: Encode context for DPR (Deep Perturbation Reweighting) model\n* Implementation details:\n\t+ Requires the 'torch' backend to be present during initialization\n\t+ Uses the `requires_backends` function from a related class (not shown in this snippet)\n* Notable dependencies:\n\t+ `DummyObject` (metadata class)\n\t+ External dependency: `torch`\n* Interfaces and classes:\n\t+ None explicitly mentioned, but possibly using interfaces defined by `DummyObject`"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3777-3781
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3781, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedContextEncoder", "start_line": 3777, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedContextEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DPRPretrainedContextEncoder`**\n\n**Purpose:** A pre-trained context encoder class for DPR (Deep Purchasing Recommendation) model using the `torch` backend.\n\n**Main Functionality:** Initializes and configures theencoder with required dependencies and backends.\n\n### Key Implementation Details:\n\n*   Requires the `torch` backend.\n*   Uses a metaclass (`DummyObject`) to manage internal configuration.\n\n### External Dependencies:\n- **Backend Module:** torch\n\n### Other Notes:\n\nThis class is likely intended for deep learning model training or inference applications, utilizing the `DPR` (Deep Purchasing Recommendation) framework."}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3784-3788
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3788, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPreTrainedModel", "start_line": 3784, "type": "class", "chroma:document": "CODE:\n\nclass DPRPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n* **Class:** `DPRPreTrainedModel`\n* **Purpose:** A pre-trained model for DPR applications\n* **Functionality:** Instantiates the model with required backends (currently only \"torch\").\n\nThe key aspects of this code chunk are:\n\n* Inheriting from `DummyObject` metaclass with a backend constraint set to [\"torch\"]\n* Initializing the model with variable arguments and keyword arguments, enforcing a specific backend requirement"}


created_at: 2025-02-07 11:22:28
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3791-3795
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3795, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedQuestionEncoder", "start_line": 3791, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedQuestionEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DPRPretrainedQuestionEncoder` class is a wrapper around the Torch backend for the Pretrained Question Encoder model. The main purpose of this class is to provide a convenient interface for using pre-trained question encoding models.\n\n**Key Implementation Details:**\n\n* The class requires the Torch backend, which is specified in the `_backends` attribute.\n* The `__init__` method enforces this requirement at initialization time, ensuring the correct backend is used.\n\n**Notable Dependencies/Requirements:**\n\n* Related classes/interfaces:\n\t+ `DummyObject`: assumed metaclass for this class\n\t+ `requires_backends`: a function that verifies the necessary backends are present\n* External dependencies:\n\t+ Torch (backend)"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3798-3802
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3802, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedReader", "start_line": 3798, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DPRPretrainedReader` class is a trainable reader for pre-trained models in the BERT framework. It uses the \"torch\" backend.\n\n* Purpose: Trains and evaluates a model on pre-trained data.\n* Implementation Details:\n\t+ Initializes with various arguments (not shown).\n\t+ Requires the \"torch\" backend to be installed.\n* Dependencies:\n\t+ `requires_backends(self, [\"torch\"])`: Uses the \"torch\" library for computations."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3805-3809
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3809, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRQuestionEncoder", "start_line": 3805, "type": "class", "chroma:document": "CODE:\n\nclass DPRQuestionEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPRQuestionEncoder` class:\n=====================\n\n*   **Purpose and Functionality:** A class for encoding DPR (Dialog Rewriting Assistant) questions using the Torch backend.\n*   **Key Implementation Details:**\n    *   Initializes with optional arguments (*) and keyword arguments (**) using `__init__` method.\n    *   Requires Torch backend installation using `requires_backends` function.\n*   **Notable Dependencies or Requirements:**\n    +   Utilizes the Torch backend for implementation.\n    +   No specified external dependencies, but requires Torch library at runtime.\n\n**Detailed Version (for RAG system):**\n\nThe provided code defines a class `DPRQuestionEncoder`, which employs the Torch backend. It incorporates an `__init__` method with parameters (*) and (**), followed by the call to `requires_backends` function to confirm availability of the required Torch backend."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3812-3816
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3816, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRReader", "start_line": 3812, "type": "class", "chroma:document": "CODE:\n\nclass DPRReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DPRReader` class: A data reader for DPR (Deep Prefix Tuning Reader) with support for loading data using **PyTorch** backend.\n\n**Key Details**\n\n* Uses PyTorch backend (`torch`)\n* Enforces backend requirement at initialization\n* Method signature:\n```\ndef __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n```"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3819-3823
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3823, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTForDepthEstimation", "start_line": 3819, "type": "class", "chroma:document": "CODE:\n\nclass DPTForDepthEstimation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DPTForDepthEstimation` is a PyTorch implementation of the DPT (deep multi-path) algorithm for depth estimation. The class uses the \"torch\" backend.\n\n**Key Details**\n\n* Initializes with input arguments using `requires_backends` method, requiring `torch` as the primary backend.\n* Uses `metaclass=DummyObject` to define the class metaparameters.\n\n**Notable Dependencies/Requirements**\n\n* `torch` (as a required dependency)\n* `DummyObject` (used for class metaparameters)"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3826-3830
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3830, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTForSemanticSegmentation", "start_line": 3826, "type": "class", "chroma:document": "CODE:\n\nclass DPTForSemanticSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPTForSemanticSegmentation` is a PyTorch-based class for semantic segmentation. Its primary purpose is to initialize and configure the model with the `torch` backend.\n\n**Key Implementation Details:**\n\n* The model requires the `torch` backend, which is explicitly checked during initialization.\n* Supports variable input arguments (`*args`, `**kwargs`) in the constructor.\n\n**Notable Dependencies and Requirements:**\n\n* Requires `torch` as a backend."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3833-3837
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3837, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTModel", "start_line": 3833, "type": "class", "chroma:document": "CODE:\n\nclass DPTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPTModel` is a class that represents a deep learning model with TensorBoardBackend (not Torch as mentioned in comments). \n\n*   The primary purpose of the class is to initialize and manage the deep learning backend.\n*   Key implementation detail: It uses `requires_backends` function which asserts that at least \"torch\" backend was passed.\n\n**Key Details:**\n\n- `_backends = [\"torch\"]`: Specifies the required backend (TensorBoard).\n- `Def __init__(self, *args, **kwargs)`: Initializes the model with variable arguments.\n \n\n**Notable Dependencies and Requirements:**\n\n- None explicitly mentioned, however it depends on `DummyObject` metaclass."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3840-3844
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3844, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTPreTrainedModel", "start_line": 3840, "type": "class", "chroma:document": "CODE:\n\nclass DPTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*Class: `DPTPreTrainedModel`*\n \nThe `DPTPreTrainedModel` class is a PyTorch-based pre-trained model that inherits from the DummyObject metaclass. It requires the `torch` backend for initialization using the `requires_backends` function. The primary purpose of this model is to handle pre-trained data in a specific domain.\n\n**Implementation Details:**\n\n* Requires PyTorch as the primary backend.\n* Initializes with any additional arguments (`*args`) and keyword arguments (`**kwargs`).\n\n**Notable Dependencies/Requirements:**\n\n* `torch`: Required backend for initialization."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3847-3851
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3851, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetForImageClassification", "start_line": 3847, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\nEfficentNetForImageClassification Class Summary:\n\n - Purpose: Image classification model using the EfficientNet architecture.\n \n - Key Details:\n   - Initialize with support for only 'torch' backend.\n   \n   - Implementation Note:\n     - `requires_backends` function ensured that only 'torch' is required.\n     \n - Notable Dependencies:\n   - `DummyObject` metaclass (implicitly inherited)\n   - External Dependency: 'torch'\n```\n\n**Full Context:** This class seems to be part of a larger package handling neural network architectures. The main purpose is image classification, leveraging the EfficientNet model. The initialization process emphasizes support for only the \"torch\" backend. The context in which this code might appear could involve frameworks that integrate with various deep learning backends."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3854-3858
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3858, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetModel", "start_line": 3854, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `EfficientNetModel` class is a metaclass-based implementation of an EfficientNet model. It requires the `torch` backend for runtime.\n\n**Key Details:**\n\n* **Primary Purpose:** Implements an EfficientNet model using a metaclass (`DummyObject`) with `torch` as the required backend.\n* **Implementation:** Initializes the model instance, requiring the presence of at least the `torch` backend.\n\n**Notable Dependencies/Requirements:**\n\n* Related Class/Function: `requires_backends`\n* Backend: `torch`"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3861-3865
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3865, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetPreTrainedModel", "start_line": 3861, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EfficientNetPreTrainedModel`\n\n*   A PyTorch-based pre-trained model for EfficientNeet architectures, utilizing the `DummyObject` metaclass.\n\n**Key Features:**\n\n*   Initializes with `args` and `kwargs`, which are validated using the `requires_backends` function ensuring support for only the `\"torch\"` backend.\n*   Utilizes a specific backend configuration.\n\n**Implementation Details:**\n\nNone mentioned in this code chunk.\n\n**Notable Dependencies/Requirements:**\n\n*   `DummyObject`: An abstract base class used as the metaclass for this class.\n*   `requires_backends` function: A function used to validate supported backends."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3868-3872
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3872, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForCausalLM", "start_line": 3868, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ElectraForCausalLM`**\n\n*   **Main Purpose and Functionality:** This class implements the Electra model for causal language modeling.\n\n*   **Key Implementation Details:**\n    *   The `requires_backends` method is called in the constructor, ensuring that only the required backend (`torch`) can be used to initialize the object.\n    *   The model is expected to work on top of a specific setup or library not fully disclosed within this snippet.\n\n*   **Notable Dependencies or Requirements:**\n    +   The Electra model class inherits from `DummyObject`.\n    +   A minimal set of dependencies is specified through the `_backends` attribute, with only `torch` being required."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3875-3879
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3879, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForMaskedLM", "start_line": 3875, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ElectraForMaskedLM**\n\n**Purpose and Functionality:** \nThis class implements the Electra architecture for masked language modeling.\n\n**Key Implementation Details:**\n- `__init__` method initializes the instance with support for only the \"torch\" backend.\n- The `requires_backends` function ensures compatibility of the instance's arguments with the required backends.\n\n**Notable Dependencies or Requirements:**\n- **Backend Compatibility:** Only supports \"torch\" backend (`requires_backends(self, [\"torch\"])`).\n- No external dependencies listed. \n\n**Class Hierarchy Information:** \nThis class implements a subclass of `DummyObject`."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3882-3886
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3886, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForMultipleChoice", "start_line": 3882, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ElectraForMultipleChoice` is a PyTorch-based class implementing the ELECTRA model for multiple-choice tasks. The main purpose is to provide a customized Electra model backend.\n\n**Key Details:**\n\n- **Implementation:** Utilizes the `DummyObject` metaclass.\n- **Backends:** Only supports the \"torch\" backend.\n- **Initialization:** Requires a valid \"torch\" backend support using the `requires_backends` function with specific arguments.\n\n**Notable Dependencies/Requirements:**\n\n- `torch` (required), `dummy_object.py` (metaclass)"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3889-3893
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3893, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForPreTraining", "start_line": 3889, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`ElectraForPreTraining` is a PyTorch-based class for pre-training language models. The primary purpose of this class is to prepare the model for downstream NLP tasks.\n\n**Key Implementation Details**\n\n- Initializes ` ElectraForPreTraining` with support for only `torch` backend using `requires_backends`.\n\n**Notable Dependencies and Requirements**\n\n- Uses PyTorch for native functionality (`torch`).\n- Requires the `DummyObject` metaclass to instantiate the class."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3896-3900
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3900, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForQuestionAnswering", "start_line": 3896, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ElectraForQuestionAnswering` class is a PyTorch-based implementation of the Electra model for question answering. The primary purpose of this class is to provide an interface for loading and using pre-trained Electra models.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` as the required backend.\n* Implies use of `requires_backends` function, likely a utility function to ensure compatible backends are installed.\n\n**Notable Dependencies/Requirements:**\n\n* Torch library (`\"torch\"`).\n* Assumed usage of `requirements.backends` (not shown in this snippet) for compatibility verification.\n\nNote: The exact implementation details of the class, such as model architecture and training requirements, are not provided within this chunk. Further inspection of the surrounding codebase would be necessary to understand its complete functionality."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3903-3907
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3907, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForSequenceClassification", "start_line": 3903, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ElectraForSequenceClassification Class Summary**\n\n*   **Class Purpose:** Implementation of the Electra model for sequence classification tasks using the PyTorch backend.\n*   **Primary Functionality:**\n    *   Instantiation of the Electra model via the `__init__` method.\n    *   Backend requirement checking, ensuring that the \"torch\" backend is available.\n\n**Key Details:**\n\n*   Utilizes the `DummyObject` metaclass for class definition.\n*   Requires the PyTorch backend (`requires_backends`) during initialization to ensure compatibility.\n\n**Notable Dependencies and Requirements:**\n\n*   The `'torch'` backend is explicitly set as a dependency.\n*   No additional external libraries or frameworks are mentioned."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3910-3914
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3914, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForTokenClassification", "start_line": 3910, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ElectraForTokenClassification**\n\n**Purpose and Functionality:**\nThe `ElectraForTokenClassification` class is a PyTorch-based implementation of the Electra model for token classification tasks.\n\n**Key Implementation Details:**\n\n*   Initializes with the required `torch` backend.\n*   Uses the `requires_backends` function to ensure compatibility with the specified backends.\n\n**Notable Dependencies and Requirements:**\n\n*   The `_backends` class variable specifies that the \"torch\" backbone is supported.\n*   No external dependencies are listed, but it relies on PyTorch for its functionality.\n\n**Additional Notes:**\nThe `DummyObject` metaclass is used, implying some underlying configuration or customization mechanism. However, without further context, its purpose remains unclear."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3917-3921
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3921, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraModel", "start_line": 3917, "type": "class", "chroma:document": "CODE:\n\nclass ElectraModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Electric Model Class Summary**\n\n### Purpose and Functionality\n\nThe `ElectraModel` class is an implementation of the Electra model architecture using the PyTorch backend. It initializes the model with support for only the \"torch\" backend.\n\n### Key Implementation Details\n\n* The model requires the `torch` backend to be loaded during initialization.\n* The `requires_backends` method checks if the necessary backends are available and raises an error if they are not.\n\n### Dependencies and Requirements\n\n* PyTorch (default backend)\n \n\n### Class/Function Members\n `ElectraModel`: Electra model class implementation."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3924-3928
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3928, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraPreTrainedModel", "start_line": 3924, "type": "class", "chroma:document": "CODE:\n\nclass ElectraPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`ElectraPreTrainedModel`\n\nA PyTorch-based class implementing an Electra pre-trained model. \n\n**Key Implementation Details:**\n\n*   Initializes with `torch` backend.\n*   Calls `requires_backends` to ensure compatibility with the specified backend.\n\n**Notable Dependencies/Requirements:**\n\n*   [Requires Backend](https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L53) - Ensure presence of `torch` backend.\n\n**Class Signature:**\n```python\nclass ElectraPreTrainedModel(metaclass=DummyObject):\n```\nThis class uses a custom metaclass (`DummyObject`) and is designed to work exclusively with the `torch` backend."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3931-3932
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3932, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_electra", "start_line": 3931, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_electra(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`load_tf_weights_in_electra` loads pre-trained TF weights for the Electra model using the specified `torch` backend.\n\n**Key Details:**\n\n* Primary purpose: Load pre-trained TF weights\n* Backends: Supports `torch`\n* Method signature: `requires_backends.load_tf_weights_in_.*`\n\n**Notable Dependencies:**\n\n* Related class/interface: `load_tf_weights_in_electra` (built-in function)\n* External dependencies:\n\t+ torch and related libraries are required to function."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3935-3939
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3939, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3ForCausalLM", "start_line": 3935, "type": "class", "chroma:document": "CODE:\n\nclass Emu3ForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`Emu3ForCausalLM`\n\n*   **Primary Purpose**: Implementation of a causal language modeling model using the `torch` backend.\n\n**Key Implementation Details**\n\n*   Initializes with input arguments `*args` and `**kwargs`.\n*   Ensures required dependency: `torch`.\n\n**Notable Dependencies/Requirements**\n\n*   `torch`: Required backend for execution.\n*   `DummyObject`: Metaclass used for this class."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3942-3946
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3946, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3ForConditionalGeneration", "start_line": 3942, "type": "class", "chroma:document": "CODE:\n\nclass Emu3ForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Emu3ForConditionalGeneration`**\n\n**Main Purpose and Functionality:**\nThe `Emu3ForConditionalGeneration` class is an implementation of the Hugging Face Transformers library's conditional generation model, Emu-3. It uses the `torch` backend.\n\n**Implementation Details:**\n\n* Initializes the instance with required dependencies.\n\t+ Requires the `torch` backend to be installed and available.\n* Verifies that the required backend (`torch`) is present.\n\n**Notable Dependencies or Requirements:**\n\n* External dependency: `torch`\n* Inherited interface(s): None\n* Implemented interface(s): None\n\nNote: I've extracted the key points from the original code chunk, focusing on the main purpose, implementation details, and dependencies."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3949-3953
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3953, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3PreTrainedModel", "start_line": 3949, "type": "class", "chroma:document": "CODE:\n\nclass Emu3PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary**\n\n`Emu3PreTrainedModel` is a [ torch backend ](https://pytorch.org/) model class that assumes `torch` as the primary backend. It initializes with args and kwargs using an undocumented `requirements_backends` function.\n\nNotable Details:\n\n*   Implements `dummy object` metaclass (`DummyObject`) to ensure class definition.\n*   Requires `torch` backend for initialization (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3956-3960
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3960, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3TextModel", "start_line": 3956, "type": "class", "chroma:document": "CODE:\n\nclass Emu3TextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Emu3TextModel` class is an interface for text models using the `torch` backend. It has a single constructor (`__init__`) that ensures at least the `\"torch\"` backend is required.\n\n**Key Details:**\n\n*   Backend support: `\"torch\"`\n*   Constructor signature: `self, *args, **kwargs`\n*   Requirements check: `requires_backends(self, [\"torch\"])`"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3963-3967
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3967, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3VQVAE", "start_line": 3963, "type": "class", "chroma:document": "CODE:\n\nclass Emu3VQVAE(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Emu3VQVAE` is a class implementing VQVAE (Variational Quantum Approximate Representation) using the `torch` backend. It initializes with required backend `torch`.\n\n**Key Details:**\n\n- Primary purpose: Implement VQVAE algorithm using `torch`.\n- Notable Design Choice: Initializes with `requires_backends` to ensure valid backend support.\n\n**Implementation Details:**\n\n- Method Signature:\n  - `__init__(self, *args, **kwargs)`\n\n- Dependencies:\n  - Related class: ` DummyObject`\n  - External Dependency: `torch`"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3970-3974
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3974, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncodecModel", "start_line": 3970, "type": "class", "chroma:document": "CODE:\n\nclass EncodecModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`EncodecModel` class:\n-----------------------------------\n\n*   **Purpose**: An encoding model that requires the `torch` backend.\n\n*   **Key Implementation Details**:\n\n    *   Verifies that the required backend (`torch`) is present using the `requires_backends` method.\n    *   Initializes the model with variable arguments (`args`, `kwargs`).\n\n*   **Notable Dependencies**:\n\n    *   Requires the `torch` backend.\n    *   Inherited or Implemented interfaces: None.\n\nNote: The `DummyObject` metaclass is not explicitly used in the code snippet provided. For the sake of understanding, it's assumed to be a mock implementation."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3977-3981
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3981, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncodecPreTrainedModel", "start_line": 3977, "type": "class", "chroma:document": "CODE:\n\nclass EncodecPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `EncodecPreTrainedModel`**\n\n*   **Purpose and Functionality**: This class represents a pre-trained model for encoding tasks, utilizing the PyTorch backend.\n\n**Key Implementation Details**:\n\n*   The model is initialized with specific backend requirements during instantiation (`__init__` method).\n*   It relies on the `DummyObject` metaclass and has a fixed set of supported backends (`_backends = [\"torch\"]`).\n\n**Notable Dependencies or Requirements**:\n\n*   PyTorch: Required for this pre-trained model.\n*   `requires_backends` function (unimplemented): Expected to enforce specific backend requirements during model initialization.\n\n**Contextual Notes**: This class appears to be designed for encoding tasks and integrates with the specified backend, implying a deeper connection to natural language processing or computer vision applications."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3984-3988
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3988, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncoderDecoderModel", "start_line": 3984, "type": "class", "chroma:document": "CODE:\n\nclass EncoderDecoderModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `EncoderDecoderModel` class is a metadata model for an encoder-decoder architecture, utilizing the \"torch\" backend. It initializes with no specific requirements.\n\n**Key Details**\n\n* Primary purpose: Metdata model for encoder-decoder architecture\n* Use case: Utilizes \"torch\" backend\n* Key implementation: Requires \"torch\" as a backend\n\n**Notable Dependencies/Requirements**\n\n* `requires_backends` function for specifying required backends\n* \"torch\" backend dependency"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3991-3995
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3995, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForCausalLM", "start_line": 3991, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForCausalLM**\n\n*   **Purpose:** A PyTorch-based implementation of the Ernie model for causal language modeling (CLM).\n*   **Functionality:** The class inherits from `DummyObject` and provides an initializer that depends on the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`).\n*   Verifies that the `\"torch\"` backend is required using `requires_backends`.\n\n**Notable Dependencies or Requirements:**\n\n*   Requires the `\"torch\"` PyTorch backend.\n*   Inherited from `DummyObject`."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3998-4002
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4002, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForMaskedLM", "start_line": 3998, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ErnieForMaskedLM` class is a PyTorch-based model implemented using the Metaclass `DummyObject`. It is designed for Masked Language Modeling tasks. \n\n**Key Implementation Details**\n\n- Initializes with a require check to ensure the 'torch' backend is provided.\n- Utilizes the `requires_backends` function, which enforces the specified backend (`['torch']`) when instantiating the class.\n\n**Notable Dependencies/Requirements**\n\n- Reqs the 'torch' PyTorch backend."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4005-4009
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4009, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForMultipleChoice", "start_line": 4005, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ErnieForMultipleChoice`: A PyTorch-based model for multiple choice classification.\n\n**Key Implementation:**\n- Initializes with `*args` and `**kwargs`, requiring the `\"torch\"` backend via `requires_backends`.\n- Uses the PyTorch framework (`torch`) backend.\n\n**Dependencies/Requirements:**\n\n* PyTorch library\n* DummyObject metaclass (not explicitly listed, but assumed)\n* `requires_backends` function (not shown in this code snippet)"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4012-4016
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4016, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForNextSentencePrediction", "start_line": 4012, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ErnieForNextSentencePrediction` is a PyTorch-based model for next sentence prediction. It utilizes the `ErnieTokenizer` and relies on the `torch` backend.\n\n**Key Details:**\n\n* Initializes with `requires_backends` function, enforcing `torch` as a required backend.\n* Tokenizer dependency implicitly references `ErnieTokenizer`.\n* No specific model architecture details are provided.\n\nNote that this summary focuses on the critical aspects of the class, omitting unnecessary implementation details and generic descriptions."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4019-4023
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4023, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForPreTraining", "start_line": 4019, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForPreTraining**\n\n*   **Main Purpose:** A pre-trained language model class `ErnieForPreTraining` that leverages the `torch` backend.\n\n**Key Implementation Details:**\n\n*   Initializes with input arguments `args` and `kwargs`.\n*   Verifies that the required `torch` backend is installed using the `requires_backends` function.\n\n**Notable Dependencies or Requirements:**\n\n*   Requires `torch`, a popular deep learning library.\n*   Utilizes the `DummyObject` metaclass due to its undefined implications on class initialization behavior."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4026-4030
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4030, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForQuestionAnswering", "start_line": 4026, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForQuestionAnswering**\n\n* **Main Purpose and Functionality:** ErnieForQuestionAnswering is a pre-trained model for question answering tasks.\n* **Key Implementation Details:**\n\t+ Initializes with `torch` backend using the `requires_backends` method.\n\t+ Uses `DummyObject` metaclass.\n* **Notable Dependencies or Requirements:**\n\t+ Relies on `torch` backend for implementation."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4033-4037
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4037, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForSequenceClassification", "start_line": 4033, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`ErnieForSequenceClassification` Class:\n------------------------\n\n### Main Purpose and Functionality\n\n`ErnieForSequenceClassification` is a PyTorch-based sequence classification model that leverages the Ernie Large Language Model (`DummyObject`) as its backbone. The primary purpose is to classify input sequences.\n\n### Key Implementation Details\n\n*   Requires the `torch` backend for execution.\n*   Initializes with variable-length arguments and keyword-only arguments.\n\n**Notable Dependencies and Requirements**\n\n*   `[ErnieClassifier]`: Serves as a classification model interface, implying the need for a related implementation or library."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4040-4044
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4044, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForTokenClassification", "start_line": 4040, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ErnieForTokenClassification` is a PyTorch-based model for token classification. Its primary purpose is to classify tokens in a sequence of text using the Ernie tokenizer backend.\n\n**Key Implementation Details:**\n\n* Models are initialized with the \"torch\" backend, which enables automatic backend switching.\n* The `requires_backends` function ensures that the required \"torch\" backend is available before model initialization.\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch\n* DummyObject (metaclass)\n* ErnieTokenizer (presumably used by this class)"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4047-4051
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4051, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieModel", "start_line": 4047, "type": "class", "chroma:document": "CODE:\n\nclass ErnieModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ErnieModel` class is a PyTorch-based model class with the following characteristics:\n\n*   Primary purpose: Implements a machine learning model utilizing the PyTorch backend.\n*   Key implementation details:\n    *   Verifies if the required backend (`torch`) is present using the `requires_backends` function.\n    \n        **Code Snippet**\n        \n```python\nclass ErnieModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n**Additional Notes**\n\n*   Notable dependencies: This code depends on the `PyTorch` library and the `DummyObject` metaclass.\n*   Inherited/Implemented interfaces: None explicitly mentioned in this snippet. However, it's likely that the class implements the `torch._C._Module` interface."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4054-4058
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4058, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErniePreTrainedModel", "start_line": 4054, "type": "class", "chroma:document": "CODE:\n\nclass ErniePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Ernie Pre-Trained Model Class Summary**\n\n**Main Purpose and Functionality:**\nThe `ErniePreTrainedModel` class is a pre-trained model implemented using the `DummyObject` metaclass. It serves as a base class for Ernie models.\n\n**Key Implementation Details:**\n\n* The model requires the `torch` backend, ensuring compatibility.\n* The `__init__` method initializes the model instance with variable arguments (`args`) and keyword arguments (`kwargs`).\n\n**Notable Dependencies or Requirements:**\n\n* External dependency: `torch`\n\nNote that there are no external interfaces implemented."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4061-4065
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4065, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmFoldPreTrainedModel", "start_line": 4061, "type": "class", "chroma:document": "CODE:\n\nclass EsmFoldPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nErmia Fold Pre-trained Model (ESM): A language model built using EsmFold algorithm.\n\n**Key Details:**\n\n* Initializes with support for only `torch` backend (`requires_backends`).\n* Primary purpose is to leverage pre-trained weights obtained from the `EsmFold` algorithm.\n\n**Implementation:**\n```python\nclass EsmFoldPreTrainedModel:\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n**Dependencies:**\n\n* External dependency on `_DummyObject` for metaclass definition.\n* Requires `requires_backends` framework function for backend validation."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4068-4072
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4072, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForMaskedLM", "start_line": 4068, "type": "class", "chroma:document": "CODE:\n\nclass EsmForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`EsmForMaskedLM` is a PyTorch-based masked language model.\n\n**Key Implementations**\n\n- Applies T5-style Masked Language Modeling algorithm.\n- Uses `requires_backends` method to ensure compatible backend (torch) installed.\n\n**Dependencies/Requirements**\n\n- Requires backend installation (`torch`)."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4075-4079
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4079, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForProteinFolding", "start_line": 4075, "type": "class", "chroma:document": "CODE:\n\nclass EsmForProteinFolding(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:** \n\n`EsmForProteinFolding` class: A PyTorch-based implementation of the ESM (Evolutionary Information from Structure and Motion) model for protein folding prediction.\n\n**Key Functionality:**\n\n* Initializes with supported backends (`torch`) and requires compatibility check\n* Uses `provide` method to ensure required backend (`torch`) is available\n\n**Notable Implementation Details:**\n\n* `__init__` method initializes the class with optional arguments\n* Requires compatible backend check using `requires_backends` function\n\n**Dependencies:**\n\n* PyTorch library (`torch`)\n* `DummyObject` metaclass"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4082-4086
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4086, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForSequenceClassification", "start_line": 4082, "type": "class", "chroma:document": "CODE:\n\nclass EsmForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EsmForSequenceClassification`\n\n*   **Purpose:** Implements a PyTorch-based sequence classification model using the ESM embeddings.\n\n**Implementation Details:**\n\n*   Initializes the model using a custom `__init__` method, ensuring that `torch` is supported as a required backend.\n*   Calls the `requires_backends` function to enforce this requirement.\n\n**Dependencies and Requirements:**\n\n*   Supported backends: `torch`\n*   Inherited interface not specified."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4089-4093
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4093, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForTokenClassification", "start_line": 4089, "type": "class", "chroma:document": "CODE:\n\nclass EsmForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `EsmForTokenClassification` class is a PyTorch-based implementation of the ESMTokenClassifier model using the DummyObject metaclass. \n\nIt has a single requirement that the backend must be \"torch\". The constructor initializes the model with any required parameters.\n\nKey Implementation Details:\n\n*   Requires Torchaes to be present and initialized correctly.\n*   Uses the DummyObject metaclass, likely for testing or experimental purposes."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4096-4100
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4100, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmModel", "start_line": 4096, "type": "class", "chroma:document": "CODE:\n\nclass EsmModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EsmModel`\n================\n\n### Purpose and Functionality\n\nThe `EsmModel` class represents an ESM (Efficient Spatial Model) model. It initializes the backend to be used by default, which is set to `\"torch\"`.\n\n### Key Implementation Details\n\n- Initializes with a `requires_backends` method that checks if the required_backend(s) for each arg (args is of type variable arguments)\n    \n### Notable Dependencies or Requirements\n\n* Backend: `torch`\n \n\nNote:\n- `_backends` attribute can be accessed from outside the class \n-  requires_backends() is not built-in python function"}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4103-4107
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4107, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmPreTrainedModel", "start_line": 4103, "type": "class", "chroma:document": "CODE:\n\nclass EsmPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: EsmPreTrainedModel**\n\n*   **Main Purpose:** An implementation of pre-trained models using the ESM (Emergence-based Sequence Modeling) architecture.\n\n*   **Key Implementation Details:**\n    *   The model uses a specific backend, requiring `torch` environment.\n    *   The class utilizes the `requires_backends` function to enforce backend requirements in the initialization process.\n\n*   **Notable Dependencies and Requirements:**\n\n    *   External dependency on `PyTorch`.\n        *   Related class/function name: `torch`\n    *   Implemented interface: `DummyObject` (as a metaclass)\n    *   Additional dependencies/inherited interfaces not explicitly mentioned."}


created_at: 2025-02-07 11:22:32
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4110-4114
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4114, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "FalconForCausalLM", "start_line": 4110, "type": "class", "chroma:document": "CODE:\n\nclass FalconForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `FalconForCausalLM`**\n\n*   **Purpose:** A custom implementation of FALCON (Flexible Application for Large Language Models) for causal LLM (Large Language Model) training using the Torch backend.\n\n**Key Implementation Details:**\n\n*   Requires support for both positional and keyword arguments (`self, *args, **kwargs`) as input in the initializer method.\n*   `requires_backends` function check ensures the FALCON model uses only the required torch backend during initialization.\n\n**Notable Dependencies/Requirements:**\n\n*   Torch backend implementation (`torch`)\n*   Inherited interface: Not explicitly listed; however, all dependencies are assumed to be managed by the internal metaclass or parent classes.\n*   Parent metaclass: `DummyObject`"}


created_at: 2025-02-07 11:33:40
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py:92-335

metadata:
{"docstring": "", "end_line": 335, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py", "name": "parse_args", "start_line": 92, "type": "function", "chroma:document": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Finetune a transformers model on a Question Answering task\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\", type=int, default=1, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\"--do_predict\", action=\"store_true\", help=\"To do prediction on the question answering model\")\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--test_file\", type=str, default=None, help=\"A csv or a json file containing the Prediction data.\"\n    )\n    parser.add_argument(\n        \"--max_seq_length\",\n        type=int,\n        default=384,\n        help=(\n            \"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,\"\n            \" sequences shorter will be padded if `--pad_to_max_length` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--pad_to_max_length\",\n        action=\"store_true\",\n        help=\"If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=False,\n    )\n    parser.add_argument(\n        \"--config_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained config name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--tokenizer_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained tokenizer name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--use_slow_tokenizer\",\n        action=\"store_true\",\n        help=\"If passed, will use a slow tokenizer (not backed by the \ud83e\udd17 Tokenizers library).\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--doc_stride\",\n        type=int,\n        default=128,\n        help=\"When splitting up a long document into chunks how much stride to take between chunks.\",\n    )\n    parser.add_argument(\n        \"--n_best_size\",\n        type=int,\n        default=20,\n        help=\"The total number of n-best predictions to generate when looking for an answer.\",\n    )\n    parser.add_argument(\n        \"--null_score_diff_threshold\",\n        type=float,\n        default=0.0,\n        help=(\n            \"The threshold used to select the null answer: if the best answer has a score that is less than \"\n            \"the score of the null answer minus this threshold, the null answer is selected for this example. \"\n            \"Only useful when `version_2_with_negative=True`.\"\n        ),\n    )\n    parser.add_argument(\n        \"--version_2_with_negative\",\n        action=\"store_true\",\n        help=\"If true, some of the examples do not have an answer.\",\n    )\n    parser.add_argument(\n        \"--max_answer_length\",\n        type=int,\n        default=30,\n        help=(\n            \"The maximum length of an answer that can be generated. This is needed because the start \"\n            \"and end predictions are not conditioned on one another.\"\n        ),\n    )\n    parser.add_argument(\n        \"--max_train_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--max_eval_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of evaluation examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--max_predict_samples\",\n        type=int,\n        default=None,\n        help=\"For debugging purposes or quicker training, truncate the number of prediction examples to this\",\n    )\n    parser.add_argument(\n        \"--model_type\",\n        type=str,\n        default=None,\n        help=\"Model type to use if training from scratch.\",\n        choices=MODEL_TYPES,\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if (\n        args.dataset_name is None\n        and args.train_file is None\n        and args.validation_file is None\n        and args.test_file is None\n    ):\n        raise ValueError(\"Need either a dataset name or a training/validation/test file.\")\n    else:\n        if args.train_file is not None:\n            extension = args.train_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`train_file` should be a csv or a json file.\"\n        if args.validation_file is not None:\n            extension = args.validation_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`validation_file` should be a csv or a json file.\"\n        if args.test_file is not None:\n            extension = args.test_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`test_file` should be a csv or a json file.\"\n\n    if args.push_to_hub:\n        assert args.output_dir is not None, \"Need an `output_dir` to creat\nSUMMARY:\nThis function defines a script for fine-tuning a transformer model on a Question Answering (QA) task. The script takes in various command-line arguments that configure the training process, such as:\n\n* Data files and locations\n* Model type and parameters\n* Training and validation settings\n* Overwrite cache and push to hub options\n\nThe function performs sanity checks to ensure consistency and validity of input arguments. It then parses the argument using `argparse` and returns a `Namespace` object containing all the arguments.\n\nIn essence, this script allows users to customize the training process for a transformer-based QA model and provides a flexible way to perform fine-tuning with various configuration optio"}


created_at: 2025-02-07 11:33:41
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py:338-1039

metadata:
{"docstring": "", "end_line": 1039, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py", "name": "main", "start_line": 338, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_qa_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        data_files = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n            extension = args.train_file.split(\".\")[-1]\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n            extension = args.validation_file.split(\".\")[-1]\n        if args.test_file is not None:\n            data_files[\"test\"] = args.test_file\n            extension = args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(extension, data_files=data_files, field=\"data\")\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n\n    if args.config_name:\n        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)\n    elif args.model_name_or_path:\n        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)\n    else:\n        config = CONFIG_MAPPING[args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n\n    if args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.tokenizer_name, use_fast=True, trust_remote_code=args.trust_remote_code\n        )\n    elif args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.model_name_or_path, use_fast=True, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if args.model_name_or_path:\n        model = AutoModelForQuestionAnswering.from_pretrained(\n            args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in args.model_name_or_path),\n            config=config,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForQuestionAnswering.from_config(config, trust_remote_code=args.trust_remote_code)\n\n    # Preprocessing the datasets.\n    # Preprocessing is slightly different for training and evaluation.\n\n    column_names = raw_datasets[\"train\"].column_names\n\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\n\n    # Padding side determines if we do (question|context) or (context|question).\n    pad_on_right = tokenizer.padding_side == \"right\"\n\n    if args.max_seq_length > tokenizer.model_max_length:\n        logger.warning(\n            f\"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the \"\n            f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n        )\n\n    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\n\n    # Training preprocessing\n    def prepare_train_features(examples):\n        # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n        # left whitespace\n        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]\n\n        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\n        # in one example possible giving several features when a context is long, each of those features having a\n        # context that overlaps a bit the context of the previous feature.\n        tokenized_examples = tokenizer(\n            examples[question_column_name if pad_on_right else context_column_name],\n            examples[context_column_name if pad_on_right else question_column_name],\n            truncation=\"only_second\" if pad_on_right else \"only_first\",\n            max_length=max_seq_length,\n            stride=args.doc_stride,\n            return_overflowing_tokens=True,\n            return_offsets_mapping=True,\n            padding=\"max_length\" if args.pad_to_max_length else False,\n        )\n\n        # Since one example might give us several features if it has a long context, we need a map from a feature to\n        # its corresponding example. This key gives us just that.\n        sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n        # The offset mappings will give us a map from token to character position in the original context. This will\n        # help us compute the start_positions and end_positions.\n        offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n\n        # Let's label those examples!\n        tokenized_examples[\"start_positions\"] = []\n        tokenized_examples[\"end_positions\"] = []\n\n        for i, offsets in enumerate(offset_mapping):\n            # We will label impossible answers with the index of the CLS token.\n            input_ids = tokenized_examples[\"input_ids\"][i]\n            if tokenizer.cls_token_id in input_ids:\n                cls_index = input_ids.index(tokenizer.cls_token_id)\n            elif tokenizer.bos_token_id in input_ids:\n                cls_index = input_ids.index(tokenizer.bos_token_id)\n            else:\n                cls_index = 0\n\n            # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n            sequence_ids = tokenized_examples.sequence_ids(i)\n\n            # One example can give several spans, this is the index of the example conta\nSUMMARY:\nThis is a PyTorch script for training and evaluating a sequence-to-sequence model using the SQuADv2 dataset. The script covers various aspects of model training, including:\n\n1. Setting up the training environment\n2. Defining the model architecture and hyperparameters\n3. Training the model on the SQuADv2 dataset\n4. Evaluating the model's performance using precision and recall metrics\n5. Logging progress and results\n6. Saving the trained model to a local directory\n\nThe script is well-structured, with clear section headings and concise comments. However, it is quite long and might be improved with additional documentation or annotations.\n\nSome minor issues and suggestions for"}


created_at: 2025-02-07 11:33:42
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py:225-732

metadata:
{"docstring": "", "end_line": 732, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py", "name": "main", "start_line": 225, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_qa_beam_search\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if data_args.test_file is not None:\n            data_files[\"test\"] = data_args.test_file\n            extension = data_args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            field=\"data\",\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n        )\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config = XLNetConfig.from_pretrained(\n        model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n    )\n    tokenizer = XLNetTokenizerFast.from_pretrained(\n        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n    )\n    model = XLNetForQuestionAnswering.from_pretrained(\n        model_args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n    )\n\n    # Preprocessing the datasets.\n    # Preprocessing is slightly different for training and evaluation.\n    if training_args.do_train:\n        column_names = raw_datasets[\"train\"].column_names\n    elif training_args.do_eval:\n        column_names = raw_datasets[\"validation\"].column_names\n    else:\n        column_names = raw_datasets[\"test\"].column_names\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\n\n    # Padding side determines if we do (question|context) or (context|question).\n    pad_on_right = tokenizer.padding_side == \"right\"\n\n    if data_args.max_seq_length > tokenizer.model_max_length:\n        logger.warning(\n            f\"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the \"\n            f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n        )\n    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)\n\n    # Training preprocessing\n    def prepare_train_features(examples):\n        # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n        # left whitespace\n        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]\n\n        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\n        # in one example possible giving several features when a context is long, each of those features having a\n        # context that overlaps a bit the context of the previous feature.\n        tokenized_examples = tokenizer(\n            examples[question_column_name if pad_on_right else context_column_name],\n            examples[context_column_name if pad_on_right else question_column_name],\n            truncation=\"only_second\" if pad_on_right else \"only_first\",\n            max_length=max_seq_length,\n            stride=data_args.doc_stride,\n            return_overflowing_tokens=True,\n            return_offsets_mapping=True,\n            return_special_tokens_mask=True,\n            return_token_type_ids=True,\n            padding=\"max_length\",\n        )\n\n        # Since one example might give us several features if it has a long context, we need a map from a feature to\n        # its corresponding example. This key gives us just that.\n        sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n        # The offset mappings will give us a map from token to character position in the original context. This will\n        # help us compute the start_positions and end_positions.\n        offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).\n        special_tokens = tokenized_examples.pop(\"special_tokens_mask\")\n\n        # Let's label those examples!\n        tokenized_examples[\"start_positions\"] = []\n        tokenized_examples[\"end_positions\"] = []\n        tokenized_examples[\"is_impossible\"] = []\n        tokenized_examples[\"cls_index\"] = []\n        tokenized_examples[\"p_mask\"] = []\n\n        for i, offsets in enumerate(offset_mapping):\n            # We will label impossible answers with the index of the CLS token.\n            input_ids = tokenized_ex\nSUMMARY:\nThis is an example Python script that trains a Question Answering (QA) model using the Hugging Face Transformers library. The script uses a pre-trained QA model and fine-tunes it on a specific dataset.\n\nHere's a brief summary of the code:\n\n1. **Data preparation**: The script loads the trained pre-trained QA model, tokenizer, and dataset.\n2. **Training setup**: The script sets up the training process by creating an"}


created_at: 2025-02-07 11:33:42
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py:735-737

metadata:
{"docstring": "", "end_line": 737, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py", "name": "_mp_fn", "start_line": 735, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Function Summary**\n\n```markdown\n# Function Name: `_mp_fn(index)`\n# Purpose: Main entry point for multiprocessing on TPUs using XLA Spawn.\n\n# Key Details:\n# - Calls the `main()` function as the primary execution target under XLA Spawn.\n```\n\nNote: This code chunk does not provide sufficient information to determine the purpose of the `main()` call, which makes it difficult to generate a comprehensive summary. If more context is available, I can attempt to add more details."}


created_at: 2025-02-07 11:33:42
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:65-85

metadata:
{"docstring": "", "end_line": 85, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "save_prefixed_metrics", "start_line": 65, "type": "function", "chroma:document": "CODE:\n\ndef save_prefixed_metrics(results, output_dir, file_name: str = \"all_results.json\", metric_key_prefix: str = \"eval\"):\n    \"\"\"\n    Save results while prefixing metric names.\n\n    Args:\n        results: (:obj:`dict`):\n            A dictionary of results.\n        output_dir: (:obj:`str`):\n            An output directory.\n        file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):\n            An output file name.\n        metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):\n            A metric name prefix.\n    \"\"\"\n    # Prefix all keys with metric_key_prefix + '_'\n    for key in list(results.keys()):\n        if not key.startswith(f\"{metric_key_prefix}_\"):\n            results[f\"{metric_key_prefix}_{key}\"] = results.pop(key)\n\n    with open(os.path.join(output_dir, file_name), \"w\") as f:\n\n\nSUMMARY:\n**Summary:**\n\n### save_prefixed_metrics Function\n\n*   Saves prefixed metric results to a JSON file.\n*   Modifies input dictionary `results` by prefixing keys with a specified name (`metric_key_prefix`) if necessary.\n\n#### Key Details:\n\n*   **Function Argument:** \n    ```python\ndef save_prefixed_metrics(results: dict, output_dir: str, file_name: str = \"all_results.json\", metric_key_prefix: str = \"eval\")\n```\n*   **Data Structure Manipulation:** Prefixes dictionary keys with `metric_key_prefix` using `dict.pop()` and f-strings for renaming.\n*   **File Handling:** Saves modified dictionary to a JSON file with specified `output_dir` and `file_name`.\n\n#### Dependencies:\n\n*   External dependencies: \n    *   `os`: module used for interacting with the operating system, e.g., joining path components together (`os.path.join`)."}


created_at: 2025-02-07 11:33:43
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:88-297

metadata:
{"docstring": "", "end_line": 297, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "parse_args", "start_line": 88, "type": "function", "chroma:document": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Finetune a transformers model on a Question Answering task\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\", type=int, default=1, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\"--do_predict\", action=\"store_true\", help=\"Eval the question answering model\")\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--test_file\", type=str, default=None, help=\"A csv or a json file containing the Prediction data.\"\n    )\n    parser.add_argument(\n        \"--max_seq_length\",\n        type=int,\n        default=384,\n        help=(\n            \"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,\"\n            \" sequences shorter will be padded if `--pad_to_max_length` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--pad_to_max_length\",\n        action=\"store_true\",\n        help=\"If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=True,\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--doc_stride\",\n        type=int,\n        default=128,\n        help=\"When splitting up a long document into chunks how much stride to take between chunks.\",\n    )\n    parser.add_argument(\n        \"--n_best_size\",\n        type=int,\n        default=20,\n        help=\"The total number of n-best predictions to generate when looking for an answer.\",\n    )\n    parser.add_argument(\n        \"--null_score_diff_threshold\",\n        type=float,\n        default=0.0,\n        help=(\n            \"The threshold used to select the null answer: if the best answer has a score that is less than \"\n            \"the score of the null answer minus this threshold, the null answer is selected for this example. \"\n            \"Only useful when `version_2_with_negative=True`.\"\n        ),\n    )\n    parser.add_argument(\n        \"--version_2_with_negative\",\n        action=\"store_true\",\n        help=\"If true, some of the examples do not have an answer.\",\n    )\n    parser.add_argument(\n        \"--max_answer_length\",\n        type=int,\n        default=30,\n        help=(\n            \"The maximum length of an answer that can be generated. This is needed because the start \"\n            \"and end predictions are not conditioned on one another.\"\n        ),\n    )\n    parser.add_argument(\n        \"--max_train_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--max_eval_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of evaluation examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--max_predict_samples\",\n        type=int,\n        default=None,\n        help=\"For debugging purposes or quicker training, truncate the number of prediction examples to this\",\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to load in all available experiment trackers from the environment and use them for logging.\",\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if (\n        args.dataset_name is None\n        and args.train_file is None\n        and args.validation_file is None\n        and args.test_file is None\n    ):\n        raise ValueError(\"Need either a dataset name or a training/validation/test file.\")\n    else:\n        if args.train_file is not None:\n            extension = args.train_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`train_file` should be a csv or a json file.\"\n        if args.validation_file is not None:\n            extension = args.validation_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`validation_file` should be a csv or a json file.\"\n        if args.test_file is not None:\n            extension = args.test_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`test_file` should be a csv or a json file.\"\n\n    if args.push_to_hub:\n        assert args.output_dir is not None, \"Need an `output_dir` to create a repo when `--push_to_hub` is passed.\"\n\n\n\nSUMMARY:\n**Function Summary:**\n\nThis function, `parse_args()`, creates an ArgumentParser object and populates it with 35 optional arguments for fine-tuning a transformer model on a Question Answering task.\n\n**Purpose and Functionality:**\nThe primary purpose of this function is to parse command-line arguments for the fine-tuning process. It allows users to specify various parameters such as dataset name, model configuration, training data file, preprocessing configurations, and hyperparameters.\n\n**Key Implementation Details:**\n\n* The function supports both `dataset_name` and `train_file` modes of input for the first set of required arguments.\n* Various hyperparameters such as learning rate, batch size, number of epochs, and evaluation metrics are optional.\n* Additional parameters like `doc_stride`, `n_best_size`, and `null_score_diff_threshold` control specific aspects of the question answering system.\n\n**Overall:**\n\nThe function is designed to provide a flexible way to configure the fine-tuning process for transformer models on Question Answering datasets. It includes many options for customization, allowing users to tailor their experiments to suit their needs."}


created_at: 2025-02-07 11:33:44
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:300-1062

metadata:
{"docstring": "", "end_line": 1062, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "main", "start_line": 300, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_qa_beam_search_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        data_files = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n            extension = args.train_file.split(\".\")[-1]\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n            extension = args.validation_file.split(\".\")[-1]\n        if args.test_file is not None:\n            data_files[\"test\"] = args.test_file\n            extension = args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(extension, data_files=data_files, field=\"data\")\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n\n    config = XLNetConfig.from_pretrained(args.model_name_or_path)\n    tokenizer = XLNetTokenizerFast.from_pretrained(args.model_name_or_path)\n    model = XLNetForQuestionAnswering.from_pretrained(\n        args.model_name_or_path, from_tf=bool(\".ckpt\" in args.model_name_or_path), config=config\n    )\n\n    # Preprocessing the datasets.\n    # Preprocessing is slightly different for training and evaluation.\n    column_names = raw_datasets[\"train\"].column_names\n\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\n\n    # Padding side determines if we do (question|context) or (context|question).\n    pad_on_right = tokenizer.padding_side == \"right\"\n\n    if args.max_seq_length > tokenizer.model_max_length:\n        logger.warning(\n            f\"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the \"\n            f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n        )\n\n    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\n\n    # Training preprocessing\n    def prepare_train_features(examples):\n        # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n        # left whitespace\n        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]\n\n        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\n        # in one example possible giving several features when a context is long, each of those features having a\n        # context that overlaps a bit the context of the previous feature.\n        tokenized_examples = tokenizer(\n            examples[question_column_name if pad_on_right else context_column_name],\n            examples[context_column_name if pad_on_right else question_column_name],\n            truncation=\"only_second\" if pad_on_right else \"only_first\",\n            max_length=max_seq_length,\n            stride=args.doc_stride,\n            return_overflowing_tokens=True,\n            return_offsets_mapping=True,\n            return_special_tokens_mask=True,\n            return_token_type_ids=True,\n            padding=\"max_length\",\n        )\n\n        # Since one example might give us several features if it has a long context, we need a map from a feature to\n        # its corresponding example. This key gives us just that.\n        sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n        # The offset mappings will give us a map from token to character position in the original context. This will\n        # help us compute the start_positions and end_positions.\n        offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).\n        special_tokens = tokenized_examples.pop(\"special_tokens_mask\")\n\n        # Let's label those examples!\n        tokenized_examples[\"start_positions\"] = []\n        tokenized_examples[\"end_positions\"] = []\n        tokenized_examples[\"is_impossible\"] = []\n        tokenized_examples[\"cls_index\"] = []\n        tokenized_examples[\"p_mask\"] = []\n\n        for i, offsets in enumerate(offset_mapping):\n            # We will label impossible answers with the index of the CLS token.\n            input_ids = tokenized_examples[\"input_ids\"][i]\n            if tokenizer.cls_token_id in input_ids:\n                cls_index = input_ids.index(tokenizer.cls_token_id)\n            elif tokenizer.bos_token_id in input_ids:\n                cls_index = input_ids.index(tokenizer.bos_token_id)\n            else:\n                cls_index = 0\n            tokenized_examples[\"cls_index\"].append(cls_index)\n\n            # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n            sequence_ids = tokenized_examples[\"token_type_ids\"][i]\n            for k, s in enumerate(special_tokens[i]):\n                if s:\n                    sequence_ids[k] = 3\n            context_idx = 1 if pad_on_right else 0\n\n            # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.\n            # The cls token gets 1.0 too (for predictions of empty answers).\n            tokenized_examples[\"p_mask\"].append(\n                [\n                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0\n                    for k, s in enumerate(sequence_ids)\n                ]\n            )\n\n            # One example can give several spans, this is the index of the example containing this span of text.\n            sample_index = sample_mapping[i]\n            answers = examples[answer_column_name][sample_index]\n            # If no answers are given, set the cls_index as answer.\n            if len(answers[\"answer_start\"]) == 0:\n                tokenized_examples[\"start_positions\"].append(cls_index)\n                tokenized_examples[\"end_positions\"].append(cls_index)\n                tokenized_examples[\"is_impossible\"].append(1.0)\n            else:\n                # Start/end character index of the answer in the text.\n                start_char = answers[\"answer_start\"\nSUMMARY:\nHere is a concise summary of the code:\n\nThis is an implementation of the DistilBERT model on the Stanford Question Answering Dataset (SQuAD). The code trains the model using Adam optimizer and cross-entropy loss, and evaluates its performance on both t"}


created_at: 2025-02-07 11:33:44
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_seq2seq_qa.py:33-163

metadata:
{"docstring": "", "end_line": 163, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_seq2seq_qa.py", "name": "QuestionAnsweringSeq2SeqTrainer", "start_line": 33, "type": "class", "chroma:document": "CODE:\n\nclass QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):\n    def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.eval_examples = eval_examples\n        self.post_process_function = post_process_function\n\n    # def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = \"eval\"):\n    def evaluate(\n        self,\n        eval_dataset: Optional[Dataset] = None,\n        eval_examples=None,\n        ignore_keys: Optional[List[str]] = None,\n        metric_key_prefix: str = \"eval\",\n        **gen_kwargs,\n    ) -> Dict[str, float]:\n        gen_kwargs = gen_kwargs.copy()\n\n        # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the\n        # training args\n        if gen_kwargs.get(\"max_length\") is None and self.args.generation_max_length is not None:\n            gen_kwargs[\"max_length\"] = self.args.generation_max_length\n        if gen_kwargs.get(\"num_beams\") is None and self.args.generation_num_beams is not None:\n            gen_kwargs[\"num_beams\"] = self.args.generation_num_beams\n        self._gen_kwargs = gen_kwargs\n\n        eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset\n        eval_dataloader = self.get_eval_dataloader(eval_dataset)\n        eval_examples = self.eval_examples if eval_examples is None else eval_examples\n\n        # Temporarily disable metric computation, we will do it in the loop here.\n        compute_metrics = self.compute_metrics\n        self.compute_metrics = None\n        start_time = time.time()\n        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop\n        try:\n            output = eval_loop(\n                eval_dataloader,\n                description=\"Evaluation\",\n                # No point gathering the predictions if there are no metrics, otherwise we defer to\n                # self.args.prediction_loss_only\n                prediction_loss_only=True if compute_metrics is None else None,\n                ignore_keys=ignore_keys,\n                metric_key_prefix=metric_key_prefix,\n            )\n        finally:\n            self.compute_metrics = compute_metrics\n        total_batch_size = self.args.eval_batch_size * self.args.world_size\n        if f\"{metric_key_prefix}_jit_compilation_time\" in output.metrics:\n            start_time += output.metrics[f\"{metric_key_prefix}_jit_compilation_time\"]\n        output.metrics.update(\n            speed_metrics(\n                metric_key_prefix,\n                start_time,\n                num_samples=output.num_samples,\n                num_steps=math.ceil(output.num_samples / total_batch_size),\n            )\n        )\n\n        if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:\n            # Only the main node write the results by default\n            eval_preds = self.post_process_function(eval_examples, eval_dataset, output)\n            metrics = self.compute_metrics(eval_preds)\n\n            # Prefix all keys with metric_key_prefix + '_'\n            for key in list(metrics.keys()):\n                if not key.startswith(f\"{metric_key_prefix}_\"):\n                    metrics[f\"{metric_key_prefix}_{key}\"] = metrics.pop(key)\n\n            metrics.update(output.metrics)\n        else:\n            metrics = output.metrics\n\n        if self.args.should_log:\n            # Only the main node log the results by default\n            self.log(metrics)\n\n        if self.args.tpu_metrics_debug or self.args.debug:\n            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)\n            xm.master_print(met.metrics_report())\n\n        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)\n        return metrics\n\n    def predict(\n        self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = \"test\", **gen_kwargs\n    ):\n        self._gen_kwargs = gen_kwargs.copy()\n\n        predict_dataloader = self.get_test_dataloader(predict_dataset)\n\n        # Temporarily disable metric computation, we will do it in the loop here.\n        compute_metrics = self.compute_metrics\n        self.compute_metrics = None\n        start_time = time.time()\n        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop\n        try:\n            output = eval_loop(\n                predict_dataloader,\n                description=\"Prediction\",\n                # No point gathering the predictions if there are no metrics, otherwise we defer to\n                # self.args.prediction_loss_only\n                prediction_loss_only=True if compute_metrics is None else None,\n                ignore_keys=ignore_keys,\n                metric_key_prefix=metric_key_prefix,\n            )\n        finally:\n            self.compute_metrics = compute_metrics\n\n        total_batch_size = self.args.eval_batch_size * self.args.world_size\n        if f\"{metric_key_prefix}_jit_compilation_time\" in output.metrics:\n            start_time += output.metrics[f\"{metric_key_prefix}_jit_compilation_time\"]\n        output.metrics.update(\n            speed_metrics(\n                metric_key_prefix,\n                start_time,\n                num_samples=output.num_samples,\n                num_steps=math.ceil(output.num_samples / total_batch_size),\n            )\n        )\n        if self.post_process_function is None or self.compute_metrics is None:\n            return output\n\n        predictions = self.post_process_function(predict_examples, predict_dataset, output, \"predict\")\n        metrics = self.compute_metrics(predictions)\n\n        # Prefix all keys with metric_key_prefix + '_'\n        for key in list(metrics.keys()):\n            if not key.startswith(f\"{metric_key_prefix}_\"):\n                metrics[f\"{metric_key_prefix}_{key}\"] = metrics.pop(key)\n        metrics.update(output.metrics)\n\n\nSUMMARY:\n**Summary:**\n\nThis is an implementation of the `QuestionAnsweringSeq2SeqTrainer` class in Hugging Face Transformers. It inherits from `Seq2SeqTrainer` and provides functionality for training and evaluating question-answering models using sequence-to-sequence learning.\n\n**Main Functionality:**\n\n1. Evaluates a model on a dataset using the `evaluate` method.\n2. Generates predictions on a test dataset using the `predict` method.\n3. Preprocesses and post-processes input data for evaluation or prediction.\n4. Computation of metrics, such as speed, compilation time, and loss.\n\n**Key Implementation Details:**\n\n* `evaluate`: uses `prediction_loop` or `evaluation_loop`, disables metric computation temporarily, and updates output metrics with speed statistics.\n* `predict`: uses `prediction_loop` or `evaluation_loop`, generates predictions using `post_process_function`, and computes metrics on the prediction output.\n\n**Notable Dependencies:**\n\n* Hugging Face Transformers library\n* PyTorch library\n\nExternal dependencies:\n\n* None\n\nInterfaces:\n\n* `Seq2SeqTrainer`\n* `Dataset`\n* `PredictionOutput`\n\nThis summary provides a concise overview of the key functionality, implementation details, and dependencies of the provided class code chunk."}


created_at: 2025-02-07 11:33:45
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py:272-733

metadata:
{"docstring": "", "end_line": 733, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py", "name": "main", "start_line": 272, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_seq2seq_qa\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if data_args.test_file is not None:\n            data_files[\"test\"] = data_args.test_file\n            extension = data_args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            field=\"data\",\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n        )\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config = AutoConfig.from_pretrained(\n        model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    tokenizer = AutoTokenizer.from_pretrained(\n        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        use_fast=model_args.use_fast_tokenizer,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    model = AutoModelForSeq2SeqLM.from_pretrained(\n        model_args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddings(len(tokenizer))\n\n    if model.config.decoder_start_token_id is None:\n        raise ValueError(\"Make sure that `config.decoder_start_token_id` is correctly defined\")\n\n    # Preprocessing the datasets.\n    # We need to generate and tokenize inputs and targets.\n    if training_args.do_train:\n        column_names = raw_datasets[\"train\"].column_names\n    elif training_args.do_eval:\n        column_names = raw_datasets[\"validation\"].column_names\n    elif training_args.do_predict:\n        column_names = raw_datasets[\"test\"].column_names\n    else:\n        logger.info(\"There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.\")\n        return\n\n    # Get the column names for input/target.\n    dataset_columns = question_answering_column_name_mapping.get(data_args.dataset_name, None)\n    if data_args.question_column is None:\n        question_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n    else:\n        question_column = data_args.question_column\n        if question_column not in column_names:\n            raise ValueError(\n                f\"--question_column' value '{data_args.question_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n    if data_args.context_column is None:\n        context_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n    else:\n        context_column = data_args.context_column\n        if context_column not in column_names:\n            raise ValueError(\n                f\"--context_column' value '{data_args.context_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n    if data_args.answer_column is None:\n        answer_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n    else:\n        answer_column = data_args.answer_column\n        if answer_column not in column_names:\n            raise ValueError(\n                f\"--answer_column' value '{data_args.answer_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n\n    # Temporarily set max_answer_length for training.\n    max_answer_length = data_args.max_answer_length\n    padding = \"max_length\" if data_args.pad_to_max_length else False\n\n    if training_args.label_smoothing_factor > 0 and not hasattr(model, \"prepare_decoder_input_ids_from_labels\"):\n        logger.warning(\n            \"label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for \"\n            f\"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory\"\n        )\n\n    if data_args.max_seq_length > tokenizer.model_max_length:\n        logger.warning(\n            f\"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the \"\n            f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n        )\n    max_s\nSUMMARY:\nThis code is for training and evaluating a Question Answering (QA) model using the Hugging Face Transformers library. It includes dataset processing, model training, evaluation, prediction, and model pushing to a hub.\n\nHere's a high-level overview:\n\n1. **Dataset Preprocessing**: The code preprocesses the QA data by applying tokenization, label encoding, and padding.\n2. **Model Training**: It trains the model using the `QuestionAnsweringSeq2SeqTrainer` class, which combines sequence-to-sequence training with loss-based optimization.\n3. **Eval"}


created_at: 2025-02-07 11:33:46
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py:736-738

metadata:
{"docstring": "", "end_line": 738, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py", "name": "_mp_fn", "start_line": 736, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Summary:**\n\n### Function Analysis\n\n* Name: `_mp_fn`\n* Purpose: Executes the `main()` function, likely as part of parallel processing or multiprocessing using TPUs via `xla_spawn`.\n\n* Implementation Details:\n\t+ Calls the outer-scoped `main()` function directly.\n\n### Notable Dependencies and Requirements:\n\n* `xla_spawn` for TPU execution\n* Assumed availability of an outer-scoped `main()` function."}


created_at: 2025-02-07 11:33:46
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py:92-320

metadata:
{"docstring": "", "end_line": 320, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py", "name": "parse_args", "start_line": 92, "type": "function", "chroma:document": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Finetune a transformers model on a summarization task\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--ignore_pad_token_for_loss\",\n        type=bool,\n        default=True,\n        help=\"Whether to ignore the tokens corresponding to padded labels in the loss computation or not.\",\n    )\n    parser.add_argument(\n        \"--max_source_length\",\n        type=int,\n        default=1024,\n        help=(\n            \"The maximum total input sequence length after \"\n            \"tokenization.Sequences longer than this will be truncated, sequences shorter will be padded.\"\n        ),\n    )\n    parser.add_argument(\n        \"--source_prefix\",\n        type=str,\n        default=None,\n        help=\"A prefix to add before every source text (useful for T5 models).\",\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\",\n        type=int,\n        default=None,\n        help=\"The number of processes to use for the preprocessing.\",\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--max_target_length\",\n        type=int,\n        default=128,\n        help=(\n            \"The maximum total sequence length for target text after \"\n            \"tokenization. Sequences longer than this will be truncated, sequences shorter will be padded. \"\n            \"during ``evaluate`` and ``predict``.\"\n        ),\n    )\n    parser.add_argument(\n        \"--val_max_target_length\",\n        type=int,\n        default=None,\n        help=(\n            \"The maximum total sequence length for validation \"\n            \"target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be \"\n            \"padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` \"\n            \"param of ``model.generate``, which is used during ``evaluate`` and ``predict``.\"\n        ),\n    )\n    parser.add_argument(\n        \"--num_beams\",\n        type=int,\n        default=None,\n        help=(\n            \"Number of beams to use for evaluation. This argument will be \"\n            \"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.\"\n        ),\n    )\n    parser.add_argument(\n        \"--pad_to_max_length\",\n        action=\"store_true\",\n        help=\"If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=False,\n    )\n    parser.add_argument(\n        \"--config_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained config name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--tokenizer_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained tokenizer name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--text_column\",\n        type=str,\n        default=None,\n        help=\"The name of the column in the datasets containing the full texts (for summarization).\",\n    )\n    parser.add_argument(\n        \"--summary_column\",\n        type=str,\n        default=None,\n        help=\"The name of the column in the datasets containing the summaries (for summarization).\",\n    )\n    parser.add_argument(\n        \"--use_slow_tokenizer\",\n        action=\"store_true\",\n        help=\"If passed, will use a slow tokenizer (not backed by the \ud83e\udd17 Tokenizers library).\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--model_type\",\n        type=str,\n        default=None,\n        help=\"Model type to use if training from scratch.\",\n        choices=MODEL_TYPES,\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if args.dataset_name is None and args.train_file is None and args.validation_file is None:\n        raise ValueError(\"Need either a dataset name or a training/validation file.\")\n    else:\n        if args.train_file is not None:\n            extension = args.train_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`train_file` should be a csv or a json file.\"\n        if args.validation_file is not None:\n            extension = args.validation_file.split(\".\")[-1]\n            assert extension in [\"csv\", \"json\"], \"`validation_file` should be a csv or a json file.\"\n\n    if args.push_to_hub:\n        assert args.output_dir is not None, \"Need an `output_dir` to create a repo when `--push_to_hub` is passed.\"\n\n\n\nSUMMARY:\n**Function Summary**\n\nThis function parses command-line arguments for the Hugging Face Transformers library. The function takes in various hyperparameters and optimization settings for training a model, including:\n\n* Model architecture parameters\n* Training data configuration\n* Hyperparameter tunables (e.g., learning rate, weight decay)\n* Optimization settings (e.g., scheduler type, accumulation steps)\n\nThe function returns a dictionary of parsed arguments.\n\n**Key Parameters**\n\n* `--train_data`: Training dataset file(s) or dataset name\n* `--validation_data`: Validation dataset file(s) or dataset name\n* `--output_dir`: Directory for saving the trained model\n* `--learning_rate`: Initial learning rate\n* `--num_train_epochs`: Total number of training epochs\n* `--scheduler_type`: Optimization scheduler type (e.g., linear, cosine)\n* `--gradient_accumulation_steps`: Accumulation steps for gradient updates\n\n**Return Value**\n\nA dictionary containing the parsed command-line arguments."}


created_at: 2025-02-07 11:33:47
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py:323-796

metadata:
{"docstring": "", "end_line": 796, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py", "name": "main", "start_line": 323, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    args = parse_args()\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_summarization_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n    if args.source_prefix is None and args.model_name_or_path in [\n        \"google-t5/t5-small\",\n        \"google-t5/t5-base\",\n        \"google-t5/t5-large\",\n        \"google-t5/t5-3b\",\n        \"google-t5/t5-11b\",\n    ]:\n        logger.warning(\n            \"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with \"\n            \"`--source_prefix 'summarize: ' `\"\n        )\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        data_files = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n            extension = args.train_file.split(\".\")[-1]\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n            extension = args.validation_file.split(\".\")[-1]\n        raw_datasets = load_dataset(extension, data_files=data_files)\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    if args.config_name:\n        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)\n    elif args.model_name_or_path:\n        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)\n    else:\n        config = CONFIG_MAPPING[args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n\n    if args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    elif args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if args.model_name_or_path:\n        model = AutoModelForSeq2SeqLM.from_pretrained(\n            args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in args.model_name_or_path),\n            config=config,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForSeq2SeqLM.from_config(config, trust_remote_code=args.trust_remote_code)\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddings(len(tokenizer))\n    if model.config.decoder_start_token_id is None:\n        raise ValueError(\"Make sure that `config.decoder_start_token_id` is correctly defined\")\n\n    prefix = args.source_prefix if args.source_prefix is not None else \"\"\n\n    # Preprocessing the datasets.\n    # First we tokenize all the texts.\n    column_names = raw_datasets[\"train\"].column_names\n\n    # Get the column names for input/target.\n    dataset_columns = summarization_name_mapping.get(args.dataset_name, None)\n    if args.text_column is None:\n        text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n    else:\n        text_column = args.text_column\n        if text_column not in column_names:\n            raise ValueError(\n                f\"--text_column' value '{args.text_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n    if args.summary_column is None:\n        summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n    else:\n        summary_column = args.summary_column\n        if summary_column not in column_names:\n            raise ValueError(\n                f\"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n\n    if args.val_max_target_length is None:\n        args.val_max_target_length = args.max_target_length\n\n    # Temporarily set max_target_length for training.\n    max_target_length = args.max_target_length\n    padding = \"max_length\" if args.pad_to_max_length else False\n\n    def preprocess_function(examples):\n        inputs = examples[text_column]\n        targets = examples[summary_column]\n        inputs = [prefix + inp for inp in inputs]\n        model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)\n\n        # Tokenize targets with the `text_target` keyword argument\n        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)\n\n        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n        # padding in the loss.\n        if padding == \"max_length\" and args.ignore_pad_token_for_loss:\n            labels[\"input_ids\"] = [\n                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n            ]\n\n        model_inputs[\"labels\"] = labels[\"input_ids\"]\n        return model_inputs\n\n    with accelerator.main_process_first():\n        train_dataset = raw_datasets[\"train\"].map(\n            preprocess_function,\n            batched=True,\n            num_proc=args.preprocessing_num_workers,\n            remove_columns=column_names,\n            load_from_cache_file=not args.overwrite_cache,\n            desc=\"Running tokenizer on dataset\",\n        )\n\n        # Temporarily set max_\nSUMMARY:\nThis code is the main training loop for a large language model. Here's a concise summary:\n\n**Overview**\n\nThe code trains a pre-trained language model on a custom dataset using transformers from Hugging Face's library. The training process involves two phases: inference and validation.\n\n**Key Components**\n\n1. **Training Loop**: This section contains the core training logic, including loading data, configuring hyperparameters, and iterating over batches to perform one step of backpropagation through time (PTT) or asynchronous training.\n2. **Inference Phase**: After a specified number of epochs, the model switches from training mode to inference mode. In this phase, the model evaluates itself on a validation set based on pre-defined evaluation metrics.\n3. **Evaluation and Results**: T"}


created_at: 2025-02-07 11:33:48
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py:311-763

metadata:
{"docstring": "", "end_line": 763, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py", "name": "main", "start_line": 311, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_summarization\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    if data_args.source_prefix is None and model_args.model_name_or_path in [\n        \"google-t5/t5-small\",\n        \"google-t5/t5-base\",\n        \"google-t5/t5-large\",\n        \"google-t5/t5-3b\",\n        \"google-t5/t5-11b\",\n    ]:\n        logger.warning(\n            \"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with \"\n            \"`--source_prefix 'summarize: ' `\"\n        )\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files this script will use the first column for the full texts and the second column for the\n    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if data_args.test_file is not None:\n            data_files[\"test\"] = data_args.test_file\n            extension = data_args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n        )\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config = AutoConfig.from_pretrained(\n        model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    tokenizer = AutoTokenizer.from_pretrained(\n        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        use_fast=model_args.use_fast_tokenizer,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    model = AutoModelForSeq2SeqLM.from_pretrained(\n        model_args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddings(len(tokenizer))\n\n    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):\n        if isinstance(tokenizer, MBartTokenizer):\n            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.lang]\n        else:\n            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.lang)\n\n    if model.config.decoder_start_token_id is None:\n        raise ValueError(\"Make sure that `config.decoder_start_token_id` is correctly defined\")\n\n    if (\n        hasattr(model.config, \"max_position_embeddings\")\n        and model.config.max_position_embeddings < data_args.max_source_length\n    ):\n        if model_args.resize_position_embeddings is None:\n            logger.warning(\n                \"Increasing the model's number of position embedding vectors from\"\n                f\" {model.config.max_position_embeddings} to {data_args.max_source_length}.\"\n            )\n            model.resize_position_embeddings(data_args.max_source_length)\n        elif model_args.resize_position_embeddings:\n            model.resize_position_embeddings(data_args.max_source_length)\n        else:\n            raise ValueError(\n                f\"`--max_source_length` is set to {data_args.max_source_length}, but the model only has\"\n                f\" {model.config.max_position_embeddings} position encodings. Consider either reducing\"\n                f\" `--max_source_length` to {model.config.max_position_embeddings} or to automatically resize the\"\n                \" model's position encodings by passing `--resize_position_embeddings`.\"\n            )\n\n    prefix = data_args.source_prefix if data_args.source_prefix is not None else \"\"\n\n    # Preprocessing the datasets.\n    # We need to tokenize inputs and targets.\n    if training_args.do_train:\n        if \"train\" not in raw_datasets:\n            raise ValueError(\"--do_train requires a train dataset\")\n        column_names = raw_datasets[\"train\"].column_names\n    elif training_args.do_eval:\n        if \"validation\" not in raw_datasets:\n            raise ValueError(\"--do_eval requires a validation dataset\")\n        column_names = raw_datasets[\"validation\"].column_names\n    elif training_args.do_predict:\n        if \"test\" not in raw_datasets:\n            raise ValueError(\"--do_predict requires a test dataset\")\n        column_names = raw_datasets[\"test\"].column_names\n    else:\n        log\nSUMMARY:\nThis code is for training and testing a text summarization model using the Hugging Face library. Here's a high-level overview of what it does:\n\n1. **Data preparation**: Loads pre-processed data from a file ( dataset_path ) and splits it into training, validation, and prediction datasets.\n2. **Model configuration**: Configures the model with hyperparameters such as number of beams, max length for predictions, and other settings.\n3. **Trainer setup**: Creates a trainer instance to handle training, evaluation, and prediction tasks.\n4. **Training loop**: Trains the model using th"}


created_at: 2025-02-07 11:33:48
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py:766-768

metadata:
{"docstring": "", "end_line": 768, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py", "name": "_mp_fn", "start_line": 766, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Summary**\n\n`_mp_fn` is an internal function that calls the `main` function, likely for use with XLA Spawn (TPUs), but its context and purpose are unclear due to incomplete information. \n\n**Key Implementation Details:**\n\n* Calls the `main` function without specifying any additional parameters or transformations.\n\n**Notable Dependencies/Requirements:**\n\n* Uses XLA Spawn (TPUs) context manager, hinted by `_mp_fn`.\n* Reference to `main` function.\n* External dependency on XLA/TF inference framework."}


created_at: 2025-02-07 11:33:49
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py:67-265

metadata:
{"docstring": "", "end_line": 265, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py", "name": "parse_args", "start_line": 67, "type": "function", "chroma:document": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Finetune a transformers model on a Masked Language Modeling task\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--validation_split_percentage\",\n        default=5,\n        help=\"The percentage of the train set used as validation set in case there's no validation split\",\n    )\n    parser.add_argument(\n        \"--pad_to_max_length\",\n        action=\"store_true\",\n        help=\"If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=False,\n    )\n    parser.add_argument(\n        \"--config_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained config name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--tokenizer_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained tokenizer name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--use_slow_tokenizer\",\n        action=\"store_true\",\n        help=\"If passed, will use a slow tokenizer (not backed by the \ud83e\udd17 Tokenizers library).\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--model_type\",\n        type=str,\n        default=None,\n        help=\"Model type to use if training from scratch.\",\n        choices=MODEL_TYPES,\n    )\n    parser.add_argument(\n        \"--max_seq_length\",\n        type=int,\n        default=None,\n        help=(\n            \"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.\"\n        ),\n    )\n    parser.add_argument(\n        \"--line_by_line\",\n        type=bool,\n        default=False,\n        help=\"Whether distinct lines of text in the dataset are to be handled as distinct sequences.\",\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\",\n        type=int,\n        default=None,\n        help=\"The number of processes to use for the preprocessing.\",\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--mlm_probability\", type=float, default=0.15, help=\"Ratio of tokens to mask for masked language modeling loss\"\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--low_cpu_mem_usage\",\n        action=\"store_true\",\n        help=(\n            \"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. \"\n            \"If passed, LLM loading time and RAM consumption will be benefited.\"\n        ),\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if args.dataset_name is None and args.train_file is None and args.validation_file is None:\n        raise ValueError(\"Need either a dataset name or a training/validation file.\")\n    else:\n        if args.train_file is not None:\n            extension = args.train_file.split(\".\")[-1]\n            if extension not in [\"csv\", \"json\", \"txt\"]:\n                raise ValueError(\"`train_file` should be a csv, json or t\nSUMMARY:\n**Functionality Summary:**\n\nThe `parse_args()` function creates an ArgumentParser instance to parse command-line arguments for fine-tuning transformer models. It supports various hyperparameters, dataset configuration options, and model-specific parameters.\n\n**Key Implementation Details:**\n\n*   The parser includes arguments for dataset configuration (`dataset_name`, `.dataset_config_name`), training data files (`train_file`, `validation_file`), hyperparameter settings (e.g., batch size, learning rate), model architecture (model name or path, model type), and additional options for caching, logging, and model deployment.\n\n**Notable Dependencies:**\n\n*   The `argparse.ArgumentParser` class from the Python Standard Library\n*   External dependencies:\n    *   **_datasets**: for loading dataset files\n    *   **transformers**: for transformer models\n    *   **torch**: for PyTorch\n\n**Interfaces and Inherited Methods:**\n\n*   none explicitly stated, but inheritance from `argparse.ArgumentParser` implies the use of its methods (e.g., `parse_args`, `print_help`) to construct a parser that handles command-line arguments.\n\nHere is an example code snippet to demonstrate how this function might be used to create and parse command-line arguments for a transformer model fine-tuning scenario:\n\n```python\nimport argparse\n\n# Create the ArgumentParser instance, as shown in the provided function\nparser = argparse.ArgumentParser(description=\"Fine-tuning transformer models\")\n\n# Define some common hyperparameter settings\nparser.add_argument(\"--batch_size\", type=int, default=32)\nparser.add_argument(\"--learning_rate\", type=float, default=5e-4)\n\n# Define a few example dataset names and their paths\ndataset_config_names = [\"bert-base-uncased\"]\ndataset_paths = {\n    \"bert-base-uncased\": \"/data/bert-base-uncased\",\n}\n\ndef create_argparse_args():\n    # Parse command-line arguments, including the dataset configuration option, training data files, etc.\n    return parser.parse_args()\n\n# Create argument parses with specific configs\nargparse_obj = argparse.ArgumentParser()\nargparse_obj.add_argument(\"-dc\", \"--dataset-config-name\", type=str,\n                         default=dataset_config_names[0],\n                         choices=[name for name in dataset_config_names])\nargparse_obj.add_argument(\"-tr_file\", \"--train-file\", type=str, required=False)\nargparse_obj.add_argument(\"-va_file\", \"--validation-file\", type=str, required=False)\n... # Other args\n\n# Parse and return\nargs = create_argparse_args()\n\n```\n\nNote that you may choose your own options, as the provided `create_argparse_args()` method is just a demonstration of how similar such functions could be constructed using a"}


created_at: 2025-02-07 11:33:50
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py:268-758

metadata:
{"docstring": "", "end_line": 758, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py", "name": "main", "start_line": 268, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_mlm_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n            extension = args.train_file.split(\".\")[-1]\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n            extension = args.validation_file.split(\".\")[-1]\n        if extension == \"txt\":\n            extension = \"text\"\n        raw_datasets = load_dataset(extension, data_files=data_files)\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    if args.config_name:\n        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)\n    elif args.model_name_or_path:\n        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)\n    else:\n        config = CONFIG_MAPPING[args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n\n    if args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    elif args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if args.model_name_or_path:\n        model = AutoModelForMaskedLM.from_pretrained(\n            args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in args.model_name_or_path),\n            config=config,\n            low_cpu_mem_usage=args.low_cpu_mem_usage,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=args.trust_remote_code)\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddings(len(tokenizer))\n\n    # Preprocessing the datasets.\n    # First we tokenize all the texts.\n    column_names = raw_datasets[\"train\"].column_names\n    text_column_name = \"text\" if \"text\" in column_names else column_names[0]\n\n    if args.max_seq_length is None:\n        max_seq_length = tokenizer.model_max_length\n        if max_seq_length > 1024:\n            logger.warning(\n                \"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value\"\n                \" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can\"\n                \" override this default with `--block_size xxx`.\"\n            )\n            max_seq_length = 1024\n    else:\n        if args.max_seq_length > tokenizer.model_max_length:\n            logger.warning(\n                f\"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the \"\n                f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n            )\n        max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\n\n    if args.line_by_line:\n        # When using line_by_line, we just tokenize each nonempty line.\n        padding = \"max_length\" if args.pad_to_max_length else False\n\n        def tokenize_function(examples):\n            # Remove empty lines\n            examples[text_column_name] = [\n                line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()\n            ]\n            return tokenizer(\n                examples[text_column_name],\n                padding=padding,\n                truncation=True,\n                max_length=max_seq_length,\n                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it\n                # receives the `special_tokens_mask`.\n                return_special_tokens_mask=True,\n            )\n\n        with accelerator.main_process_first():\n            tokenized_datasets = raw_datasets.map(\n                tokenize_function,\n                batched=True,\n                num_proc=args.preprocessing\nSUMMARY:\nHere is a concise summary of the code:\n\n** overview**\n\nThis is a training script for a language model using the Hugging Face Transformers library. It trains the model on a dataset and evaluates its performance.\n\n**main components**\n\n1. **Data loading**: The script loads the dataset and shuffles it.\n2. **Model initialization**: The script initializes the model with the specified configuration and architecture.\n3. **Training loop**: The script runs through a series of epochs, where:\n\t* It trains the model on the shuffled data for a specified number of steps.\n\t* It evaluates the model's performance using evaluation m"}


created_at: 2025-02-07 11:33:51
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py:72-320

metadata:
{"docstring": "", "end_line": 320, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py", "name": "parse_args", "start_line": 72, "type": "function", "chroma:document": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(\n        description=\"Finetune a transformers model on a causal language modeling task using fill-in-the middle objective\"\n    )\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv, txt or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv, txt or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--validation_split_percentage\",\n        default=5,\n        help=\"The percentage of the train set used as validation set in case there's no validation split\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=False,\n    )\n    parser.add_argument(\n        \"--config_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained config name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--tokenizer_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained tokenizer name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--use_slow_tokenizer\",\n        action=\"store_true\",\n        help=\"If passed, will use a slow tokenizer (not backed by the \ud83e\udd17 Tokenizers library).\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--model_type\",\n        type=str,\n        default=None,\n        help=\"Model type to use if training from scratch.\",\n        choices=MODEL_TYPES,\n    )\n    parser.add_argument(\n        \"--block_size\",\n        type=int,\n        default=None,\n        help=(\n            \"Optional input sequence length after tokenization. The training dataset will be truncated in block of\"\n            \" this size for training. Default to the model max input length for single sentence inputs (take into\"\n            \" account special tokens).\"\n        ),\n    )\n    parser.add_argument(\n        \"--fim_rate\",\n        type=float,\n        default=0.5,\n        help=(\n            \" Optional probability with which the FIM transformation is applied to the example.\"\n            \" Default is 0.5. A rate of 1.0 means every example will undergo FIM transformation,\"\n            \" while a rate of 0.0 means no example will.\"\n        ),\n    )\n    parser.add_argument(\n        \"--fim_spm_rate\",\n        type=float,\n        default=0.5,\n        help=(\n            \"Within the examples undergoing FIM transformation, this rate determines the probability\"\n            \" of applying the Sentence Permutation Mode (SPM).\"\n            \" Default is 0.5. A rate of 1.0 means all FIM transformations will use SPM,\"\n            \" while a rate of 0.0 means none will.\"\n        ),\n    )\n    parser.add_argument(\n        \"--truncate_or_pad\",\n        type=bool,\n        default=True,\n        help=(\n            \"Indicates whether the transformed example should be truncated or padded to maintain\"\n            \" the same length as the original example.\"\n            \" Default is True. If False, the function will not truncate or pad the examples.\"\n        ),\n    )\n    parser.add_argument(\n        \"--fim_prefix_token\",\n        type=str,\n        default=\"<fim_prefix>\",\n        help=\"Fill-in-Middle Prefix token. Defaults to '<fim_prefix>'.\",\n    )\n    parser.add_argument(\n        \"--fim_middle_token\",\n        type=str,\n        default=\"<fim_middle>\",\n        help=\"Fill-in-Middle Middle token. Defaults to '<fim_middle>'.\",\n    )\n    parser.add_argument(\n        \"--fim_suffix_token\",\n        type=str,\n        default=\"<fim_suffix>\",\n        help=\"Fill-in-Middle Middle token. Defaults to '<fim_suffix>'.\",\n    )\n    parser.add_argument(\n        \"--fim_pad_token\",\n        type=str,\n        default=\"<fim_pad>\",\n        help=(\n            \"Fill-in-Middle Pad token. Used only when 'truncate_or_pad' is set to True.\" \" Defaults to '<fim_pad>'.\"\n        ),\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\",\n        type=int,\n        default=None,\n        help=\"The number of processes to use for the preprocessing.\",\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--no_keep_linebreaks\", action=\"store_true\", help=\"Do not keep line breaks when using TXT files.\"\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--low_cpu_mem_usage\",\n        action=\"store_true\",\n        help=(\n            \"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. \"\n            \"If passed, LLM loading time and RAM consumption will \nSUMMARY:\nThe provided code snippet is written in Python and appears to be the main entry point for a script that utilizes the Hugging Face Transformers library for natural language processing tasks. The script accepts various command-line arguments that define how the model should be trained, saved, and potentially pushed to the Model Hub.\n\nHere's a summary:\n\n*   **Usage**: This script can be run from the command line, passing in various arguments that control its behavior.\n*   **Arguments**:\n    *   Training and validation file paths (csv, json, or txt format)\n    *   Hyperparameters for training, such as learning rate, batch size, etc.\n    *   Model configuration options\n    *   Integration with the Model Hub\n    *   Experiment tracking options\n\n**Example Argument Parsers:**\n\n```python\n# Example argument parsers\nif __name__ == \"__main__\":\n    parser = TrainerArguments(\n        resume_model=None,\n        output_dir=None,\n        push_to_hub=False,\n        hub_model_id=None,\n        hub_token=None,\n        save_steps=700,\n        save_total_limit=None,\n        save_on_each_device=True,\n        num_train_epochs=3.0,\n        per_device train_batch_size=16,\n        per_device_val_batch_size=64,\n        warmup_steps=500,\n        weight_decay=0.01,  # Add this argument\n    )\n```\n\n**Key Features:**\n\n*   **Training**: Automatic model training and tuning.\n*   **Model Saving**: Model saving op"}


created_at: 2025-02-07 11:33:52
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py:323-911

metadata:
{"docstring": "", "end_line": 911, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py", "name": "main", "start_line": 323, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_fim_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n        # Set a numpy random state for FIM transformations\n        np_rng = np.random.RandomState(seed=args.seed)\n    else:\n        # Still set a random state for FIM transformations\n        np_rng = np.random.RandomState(seed=42)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n            # Clone repo locally\n            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        dataset_args = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n        extension = args.train_file.split(\".\")[-1]\n        if extension == \"txt\":\n            extension = \"text\"\n            dataset_args[\"keep_linebreaks\"] = not args.no_keep_linebreaks\n        raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n                **dataset_args,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n                **dataset_args,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.html.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    if args.config_name:\n        config = AutoConfig.from_pretrained(\n            args.config_name,\n            trust_remote_code=args.trust_remote_code,\n        )\n    elif args.model_name_or_path:\n        config = AutoConfig.from_pretrained(\n            args.model_name_or_path,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        config = CONFIG_MAPPING[args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n\n    if args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    elif args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if args.model_name_or_path:\n        model = AutoModelForCausalLM.from_pretrained(\n            args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in args.model_name_or_path),\n            config=config,\n            low_cpu_mem_usage=args.low_cpu_mem_usage,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)\n\n    # Add the new FIM tokens to the tokenizer and resize model's vocab embeddings\n    special_tokens = [args.fim_prefix_token, args.fim_middle_token, args.fim_suffix_token]\n    if args.truncate_or_pad:\n        special_tokens.append(args.fim_pad_token)\n\n    # Get the factor by which the embedding layer should be padded based on the device\n    pad_factor = 1\n    if torch.cuda.is_availble():\n        pad_factor = 8\n\n    elif is_torch_tpu_available():\n        pad_factor = 128\n\n    # Add the new tokens to the tokenizer\n    tokenizer.add_tokens(special_tokens)\n    original_embeddings = model.get_input_embeddings()\n\n    if is_deepspeed_zero3_enabled():\n        import deepspeed\n\n        with deepspeed.zero.GatheredParameters(original_embeddings.weight, modifier_rank=0):\n            # Get the pre-expansion embeddings of the model and resize the embedding layer\n            model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)\n            embeddings = model.get_input_embeddings()\n\n            # Sample the embeddings for the new tokens from a multivariate normal distribution\n            # We do this so that the new embeddings are close to the original embeddings and not necessarily zero\n            # More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html\n            mean = original_embeddings.mean(dim=0)\n            n = original_embeddings.size()[0]\n            sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n\n            dist = torch.distributions.multivariate_normal.MultivariateNormal(\n                mean,\n                covariance_matrix=1e-5 * sigma,\n            )\n            new_token_embeddings = torch.stack(\n                tuple((dist.sample() for _ in range(len(special_tokens)))),\n                dim=0,\n            )\n    else:\n        original_embeddings = model.get_input_embeddings()\n        # Get the pre-expansion embeddings of the model and resize the embedding layer\n        model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)\n        embeddings = model.get_input_embeddings()\n\n        # Sample the embeddings for the new tokens from a multivariate normal distribution\n        # We do this so that the new embeddings are close to the original embeddings and not necessarily\nSUMMARY:\nHere is a concise summary:\n\nThis is the code for a PyTorch-based machine translation model training script. The script trains the model on an evaluation dataset, keeps track of losses and perplexities at each epoch, and saves the final model to a specified output directory when training completes. The "}


created_at: 2025-02-07 11:33:52
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py:228-574
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 574, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py", "name": "main", "start_line": 228, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_plm\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                trust_remote_code=data_args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                trust_remote_code=data_args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if extension == \"txt\":\n            extension = \"text\"\n        raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n    }\n    if model_args.config_name:\n        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)\n    elif model_args.model_name_or_path:\n        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)\n    else:\n        config = XLNetConfig()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n        if model_args.config_overrides is not None:\n            logger.info(f\"Overriding config: {model_args.config_overrides}\")\n            config.update_from_string(model_args.config_overrides)\n            logger.info(f\"New config: {config}\")\n\n    tokenizer_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"use_fast\": model_args.use_fast_tokenizer,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n    }\n    if model_args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n    elif model_args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if model_args.model_name_or_path:\n        model = XLNetLMHeadModel.from_pretrained(\n            model_args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n            config=config,\n            cache_dir=model_args.cache_dir,\n            revision=model_args.model_revision,\n            token=model_args.token,\n            low_cpu_mem_usage=model_args.low_cpu_mem_usage,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = XLNetLMHeadModel(config)\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddi\nSUMMARY:\nThis is a PyTorch code snippet that defines a script for training and evaluating a pre-trained model, likely on the Hugging Face Transformers library. The script:\n\n1. Initializes a dataset from a specified data source (e.g., text file) and collates it into batches of permutated language inputs.\n2. Setups a `Trainer` object with the trained model, training arguments, and evaluation datasets.\n3. Trains the model using the trainer, saving checkpoints as needed.\n4. Evaluates the model on the validation dataset, logging metrics.\n\nSome high-level points:\n\n* The script is designed to work with Hugging Face's Transformers library, which provides a simple framework for text processing and machine learning tasks.\n* The `Trainer` object is used to manage training and evaluation of the model, including saving checkpoints and metrics.\n* The script uses PyTorch's built-in tensors and data structures to process and store input data.\n* The final script saves output models, logs train and eval metrics, and optionally pushes the trained model to Hugging Face's Hub.\n\nExample use case:\n\nTo use this code as a startin"}


created_at: 2025-02-07 11:33:53
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py:577-579
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 579, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py", "name": "_mp_fn", "start_line": 577, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Summary:**\n\n`_mp_fn(index)` appears to be a function wrapper for distributing computation across multiple computing resources, likely relevant for TPUs (Tensor Processing Units). The main purpose is to execute the `main()` function in an multi-processing context. \n\n**Key Details:**\n\n- Uses `_mp_fn(index)`, suggesting potential multi-threading or process management.\n- Calls `main()`, indicating that this function serves as a wrapper around the primary computation logic.\n\n**Notable Dependencies/Requirements:**\n\n- Relies on XLA Spawn (TPUs), a technology introduced by Google to simplify machine learning inference on TPUs."}


created_at: 2025-02-07 11:33:53
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py:248-681
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 681, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py", "name": "main", "start_line": 248, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_mlm\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    # Set the verbosity to info of the Transformers logger (on main process only):\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this\n    # behavior (see below)\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            streaming=data_args.streaming,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if extension == \"txt\":\n            extension = \"text\"\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n        )\n\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.config_name:\n        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)\n    elif model_args.model_name_or_path:\n        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)\n    else:\n        config = CONFIG_MAPPING[model_args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n        if model_args.config_overrides is not None:\n            logger.info(f\"Overriding config: {model_args.config_overrides}\")\n            config.update_from_string(model_args.config_overrides)\n            logger.info(f\"New config: {config}\")\n\n    tokenizer_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"use_fast\": model_args.use_fast_tokenizer,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n    elif model_args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if model_args.model_name_or_path:\n        torch_dtype = (\n            model_args.torch_dtype\n            if model_args.torch_dtype in [\"auto\", None]\n            else getattr(torch, model_args.torch_dtype)\n        )\n        model = AutoModelForMaskedLM.from_pretrained(\n            model_args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n            config=config,\n            cache_dir=model_args.cache_dir,\n            revision=model_args.model_revision,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n            torch_dtype=torch_dtype,\n            low_cpu_mem_usage=model_args.low_cpu_mem_usage,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=model_args.trust_remote_code)\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if \nSUMMARY:\nThis is a code snippet for fine-tuning a pre-trained language model (such as BERT) on the fill-mask task using Hugging Face's Trainer API. The goal is to create a custom trained model that can be used to solve the fill-mask problem from a given dataset.\n\nHere are the key points:\n\n1. Loading and preparing datasets\n2. Creating and configuring the model and args\n3. Defining data collators, preprocessors, and metrics\n4. In"}


created_at: 2025-02-07 11:33:54
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py:684-686
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 686, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py", "name": "_mp_fn", "start_line": 684, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Summary:**\n\nThe `_mp_fn` function is part of a multi-processing framework and serves as an entry point for executing tasks in parallel. It calls the `main` function when operating on Google TPU's (`xla_spawn`). The precise purpose is:\n\n*   **Primary Functionality:** Execute task in parallel using multiple processes (TPUs).\n*   **Key Implementation Detail:** Calls `main()` as a fallback for xla_spawn environment.\n*   **Notable Dependencies/Requirements:**\n    *   `xla_spawn` (Google TPU)\n   "}


created_at: 2025-02-07 11:33:54
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py:237-647
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 647, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py", "name": "main", "start_line": 237, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_clm\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            streaming=data_args.streaming,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        dataset_args = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n        extension = (\n            data_args.train_file.split(\".\")[-1]\n            if data_args.train_file is not None\n            else data_args.validation_file.split(\".\")[-1]\n        )\n        if extension == \"txt\":\n            extension = \"text\"\n            dataset_args[\"keep_linebreaks\"] = data_args.keep_linebreaks\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            **dataset_args,\n        )\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                **dataset_args,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                **dataset_args,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n\n    config_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.config_name:\n        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)\n    elif model_args.model_name_or_path:\n        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)\n    else:\n        config = CONFIG_MAPPING[model_args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n        if model_args.config_overrides is not None:\n            logger.info(f\"Overriding config: {model_args.config_overrides}\")\n            config.update_from_string(model_args.config_overrides)\n            logger.info(f\"New config: {config}\")\n\n    tokenizer_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"use_fast\": model_args.use_fast_tokenizer,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n    elif model_args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if model_args.model_name_or_path:\n        torch_dtype = (\n            model_args.torch_dtype\n            if model_args.torch_dtype in [\"auto\", None]\n            else getattr(torch, model_args.torch_dtype)\n        )\n        model = AutoModelForCausalLM.from_pretrained(\n            model_args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n            config=config,\n            cache_dir=model_args.cache_dir,\n            revision=model_args.model_revision,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n            torch_dtype=torch_dtype,\n            low_cpu_mem_usage=model_args.low_cpu_mem_usage,\n        )\n    else:\n        model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)\n        n_params = sum({p.data_ptr(): p\nSUMMARY:\nThis code is used to train and evaluate a transformer-based language generation model using the Hugging Face Python library. Here's a high-level overview of what it does:\n\n1. Loads pre-trained tokenizer and model from specific paths or configurations.\n2. Downloads and preprocesses text data for training and evaluation (e.g., dataset names, file paths).\n3. Initializes a `Trainer` object with the trained model, hyperparameters, and necessary datasets (training, evaluation) if specified.\n4. Tra"}


created_at: 2025-02-07 11:33:55
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py:650-652
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 652, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py", "name": "_mp_fn", "start_line": 650, "type": "function", "chroma:document": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**Function Summary: `_mp_fn`**\n\n* Primary purpose: The `_mp_fn` function appears to be part of an implementation supporting Google Cloud Tensor Processing Units (TPUs) execution. It likely serves as the main entry point for spawning multi-threading or parallel processing on TPUs.\n* Key implementation details:\n\t+ Calls the `main()` function, which is not defined in this code snippet.\n\t+ Notable dependency: Uses `xla_spawn` library, specifically designed for TPU execution.\n* Notable dependencies and requirements:\n\t+ Inherently uses external libraries (`xla_spawn`) related to TPU execution."}


created_at: 2025-02-07 11:33:55
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py:304-854
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 854, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py", "name": "main", "start_line": 304, "type": "function", "chroma:document": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_fim\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Set a numpy random state for FIM transformations\n    np_rng = np.random.RandomState(seed=training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            streaming=data_args.streaming,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                data_args.dataset_name,\n                data_args.dataset_config_name,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                streaming=data_args.streaming,\n                trust_remote_code=model_args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        dataset_args = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n        extension = (\n            data_args.train_file.split(\".\")[-1]\n            if data_args.train_file is not None\n            else data_args.validation_file.split(\".\")[-1]\n        )\n        if extension == \"txt\":\n            extension = \"text\"\n            dataset_args[\"keep_linebreaks\"] = data_args.keep_linebreaks\n        raw_datasets = load_dataset(\n            extension,\n            data_files=data_files,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            **dataset_args,\n        )\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{data_args.validation_split_percentage}%]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                **dataset_args,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{data_args.validation_split_percentage}%:]\",\n                cache_dir=model_args.cache_dir,\n                token=model_args.token,\n                **dataset_args,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.html.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n\n    config_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.config_name:\n        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)\n    elif model_args.model_name_or_path:\n        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)\n    else:\n        config = CONFIG_MAPPING[model_args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n        if model_args.config_overrides is not None:\n            logger.info(f\"Overriding config: {model_args.config_overrides}\")\n            config.update_from_string(model_args.config_overrides)\n            logger.info(f\"New config: {config}\")\n\n    tokenizer_kwargs = {\n        \"cache_dir\": model_args.cache_dir,\n        \"use_fast\": model_args.use_fast_tokenizer,\n        \"revision\": model_args.model_revision,\n        \"token\": model_args.token,\n        \"trust_remote_code\": model_args.trust_remote_code,\n    }\n    if model_args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n    elif model_args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **token\nSUMMARY:\nThis is the code for a Hugging Face Transformers training script. It's used to train and evaluate a text generation model on a dataset of your choice.\n\n**Key Features:**\n\n*   Supports both fine-tuning and training from scratch.\n*   Handles streaming datasets with a progress bar and metric logging.\n*   Trains models on different devices (GPU/TPU) if available and optimizes for performance, memory, or mixed-mode training.\n*   Automatically saves checkpoints and logs metrics to help track the training progress.\n\n**Preprocessing Steps:**\n\n1.  **Load Data**: The script loads a dataset using `tokenized_datasets.load()`.\n2.  **Perform Fine-tuning or Training from Scratch**: Before starting the actual training process, it fine-tunes or trains your model using `Trainer.train()` if you already have data. Otherwise, it starts with empty datasets and begins preprocessing your data.\n3.  **Fine-tune or Train Model**: Depending on whether you want to continue or start anew, it processes your dataset before calling `Trainer.train()`.\n4.  **Logging of Metrics**: As part of the training process, metrics like accuracy, perplexity, train loss, and validation metrics are logged.\n\n**Customization Options:**\n\nYou can customize several aspects of your experiment, including:\n\n*   The task type (e.g., \"text-generation\")\n*   Model configuration\n*   Data collator (by specifying `default_data_collator` as you can if you desire)\n*   Training settings (`do_train`, `do_eval`, `max Train samples`, `max eval sampl"}


