# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


dir: sysdb
filename: 00006-collection-segment-metadata.sqlite.sql
hash: 4eea7468935bf25d4604a0fed2366116
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE collection_metadata ADD COLUMN bool_value INTEGER;
ALTER TABLE segment_metadata ADD COLUMN bool_value INTEGER;


dir: sysdb
filename: 00007-collection-config.sqlite.sql
hash: 1c7e63bba346a42a18b6ab7f1c989bed
sql:
-- Stores collection configuration dictionaries.
ALTER TABLE collections ADD COLUMN config_json_str TEXT;


dir: sysdb
filename: 00008-maintenance-log.sqlite.sql
hash: 0a0e7e93111a01789addf64961c6127c
sql:
-- Records when database maintenance operations are performed.
-- At time of creation, this table is only used to record vacuum operations.
CREATE TABLE maintenance_log (
  id INT PRIMARY KEY,
  timestamp INT NOT NULL,
  operation TEXT NOT NULL
);


dir: sysdb
filename: 00009-segment-collection-not-null.sqlite.sql
hash: 054355aef9e63702bf54ea29e61563f1
sql:
-- This makes segments.collection non-nullable.
CREATE TABLE segments_temp (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    collection TEXT REFERENCES collection(id) NOT NULL
);

INSERT INTO segments_temp SELECT * FROM segments;
DROP TABLE segments;
ALTER TABLE segments_temp RENAME TO segments;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


dir: metadb
filename: 00004-metadata-indices.sqlite.sql
hash: fb36603a45ee2cd0254cef3ef86585e8
sql:
CREATE INDEX IF NOT EXISTS embedding_metadata_int_value ON embedding_metadata (key, int_value) WHERE int_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_float_value ON embedding_metadata (key, float_value) WHERE float_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_string_value ON embedding_metadata (key, string_value) WHERE string_value IS NOT NULL;


# embeddings_queue

created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:158-162
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 162, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SquadDataset", "start_line": 158, "type": "class", "chroma:document": "CODE:\n\nclass SquadDataset(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`SquadDataset` is a dataset class that utilizes the `torch` backend. It initializes with `torch` as a required backend and takes any additional arguments.\n\n**Key Details:**\n\n* Backend: `torch`\n* Primary purpose: Utilize `torch` for dataset operations\n* Method Signature: `__init__(self, *args, **kwargs)`\n\n**Notable Dependencies/Requirements:**\n\n* Related class: `requires_backends`\n* External dependency:\n\t+ Backend: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:165-169
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 169, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SquadDataTrainingArguments", "start_line": 165, "type": "class", "chroma:document": "CODE:\n\nclass SquadDataTrainingArguments(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: SquadDataTrainingArguments**\n\n### Purpose and Functionality\n\nThe `SquadDataTrainingArguments` class is designed to handle training arguments for the Hugging Face Transformers' model. It ensures that at least the \"torch\" backend is supported.\n\n### Key Implementation Details\n\n- The `_backends` attribute specifies the supported backends, which in this case is just \"torch\".\n- The `__init__` method requires the \"torch\" backend to be present (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:172-176
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 176, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TextDataset", "start_line": 172, "type": "class", "chroma:document": "CODE:\n\nclass TextDataset(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `TextDataset`**\n\n*   **Purpose**: Provides a dataset class for text data, requiring the use of PyTorch (`torch`) as its backend.\n\n**Key Implementation Details:**\n\n*   Initializes with optional arguments (`*args`, `**kwargs`)\n*   Verifies that PyTorch is available as required by the `requires_backends` method\n\n**Notable Dependencies/Requirements:**\n\n*   PyTorch (`torch`)\n*   Requires access to the `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:179-183
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 183, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TextDatasetForNextSentencePrediction", "start_line": 179, "type": "class", "chroma:document": "CODE:\n\nclass TextDatasetForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`TextDatasetForNextSentencePrediction`\n\nA dataset class for training models to predict the next sentence in a sequence, designed to work with the `torch` backend.\n\n**Implementation Details:**\n\n- Initializes with support for multiple backends (currently only `torch`)\n- Requires the `torch` backend to be specified at instantiation (`text_dataset.requires_backends([\"torch\"])`)\n\n**Dependencies/Requirements:**\n\n* Required backend: `torch`\n* Inherited/Implemented Interfaces: Not specified"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:186-190
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 190, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlternatingCodebooksLogitsProcessor", "start_line": 186, "type": "class", "chroma:document": "CODE:\n\nclass AlternatingCodebooksLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AlternatingCodebooksLogitsProcessor` class is a meta-class-based processor for alternating codebook logit computations. It primarily utilizes the Torch backend.\n\n**Key Details:**\n\n* **Purpose:** Computes alternating codebook logit values.\n* **Backend:** Single-supported backend, `'torch'`, with no additional configurations available.\n* **Implementation Notes:** The `requires_backends` method is used to validate the supported backends during class initialization. \n\nThis summary captures the essence of the provided class chunk while preserving specific details such as class name, metaclass, and backend dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:193-197
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 197, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BayesianDetectorConfig", "start_line": 193, "type": "class", "chroma:document": "CODE:\n\nclass BayesianDetectorConfig(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BayesianDetectorConfig` class is a configuration model for Bayes-based detectors. It specifies the required backend as `torch`. The class initializes with optional arguments and ensures that the `requires_backends` function is called to specify the backend requirement.\n\n**Key Details:**\n\n- **Class purpose:** Configuration for bayesian detector\n- **Backend requirement:** Only supports \"torch\" backend\n- **Implementation highlight:** `requires_backends` function is invoked to enforce backend specification\n\n**Dependencies and Requirements:**\n\n- _uses_ `torch` backend\n- Inherited from `DummyObject` metaclass\n- Requires no external dependencies (built-in with Python)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:200-204
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 204, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BayesianDetectorModel", "start_line": 200, "type": "class", "chroma:document": "CODE:\n\nclass BayesianDetectorModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BayesianDetectorModel`**\n\nThe `BayesianDetectorModel` class is a Bayesian detector model implemented using the \"torch\" backend. \n\n*   **Primary Purpose:** The primary purpose of this model is to perform classification tasks using Bayesian detection algorithms.\n*   **Key Implementation Details:** \n    *   Uses the `torch` backend for computations.\n    *   Implements the `__init__` method, which requires the \"torch\" backend to be installed (`requires_backends` function).\n*   **Notable Dependencies:**\n    *   Relies on related classes/functions:\n        *   `DummyObject`: The metaclass used for this class definition.\n        *   `requires_backends`: A function that ensures the required backends are loaded.\n    *   External dependencies:\n        *   `torch`: The machine learning library used as the backend for computations.\n\n**Additional Context:**\n\n`BayesianDetectorModel` is likely intended to be used in a larger application performing Bayesian detection tasks, such as anomaly detection or classification."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:207-211
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 211, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeamScorer", "start_line": 207, "type": "class", "chroma:document": "CODE:\n\nclass BeamScorer(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BeamScorer` class is a scoring functionality for beam search algorithms. \n\n*Primary purpose:* To initialize and validate the BeamScorer instance with required backends (currently only \"torch\").\n \n*Key implementation details:*\n \n   - Requires the \"torch\" backend to be initialized.\n   - The `__init__` method validates that the required backend is present.\n\n**Notable dependencies or requirements:**\n\n- Backend: torch\n- Method signature:\n\n     ```python\ndef __init__(self, *args, **kwargs):\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:214-218
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 218, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeamSearchScorer", "start_line": 214, "type": "class", "chroma:document": "CODE:\n\nclass BeamSearchScorer(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary: Beam Search Scorer Class**\n\n**Main Purpose and Functionality:** A beam search scorer class designed for natural language processing tasks, utilizing the `torch` backend.\n\n**Key Implementation Details:**\n\n* The `_backends` attribute is set to `[\"torch\"]`, indicating the supported backends.\n* The `__init__` method requires the `\"torch\"` backend using `requires_backends`.\n \n**Notable Dependencies and Requirements:**\n\n* Relies on the `torch` backend for execution."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:221-225
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 225, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClassifierFreeGuidanceLogitsProcessor", "start_line": 221, "type": "class", "chroma:document": "CODE:\n\nclass ClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ClassifierFreeGuidanceLogitsProcessor**\n\nA `ClassifierFreeGuidanceLogitsProcessor` class used for processing guidance logits in classification tasks. It requires the `torch` backend and initializes with a list of supported backends.\n\n### Key Implementation Details:\n\n*   **Initialization**: The `__init__` method initializes the object with required dependencies (`requires_backends`)\n*   Supported Backends: `[torch]`\n\n### Notable Dependencies/Requirements:\n\n*   Required Backend: `torch`\n*   Inherited Interface:\n    *   `requires_backends`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:228-232
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 232, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConstrainedBeamSearchScorer", "start_line": 228, "type": "class", "chroma:document": "CODE:\n\nclass ConstrainedBeamSearchScorer(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ConstrainedBeamSearchScorer Class Summary**\n\n*   **Purpose:** Implements a constrained beam search scorer utilizing the `torch` backend.\n*   **Main Functionality:**\n    *   Initializes and configure the scorer for use with the specified backends (`torch`).\n*   **Key Implementation Details:**\n    *   Verifies that the required backend (`torch`) is set using the `requires_backends` function.\n        +   Specific backend: `\"torch\"`\n*   **Notable Dependencies/Requirements:**\n    ```\n    Requires: torch\n    Inherited/IImplemented Interfaces:\n        None mentioned\n    External Dependencies:\n        torch\n    ```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:235-239
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 239, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Constraint", "start_line": 235, "type": "class", "chroma:document": "CODE:\n\nclass Constraint(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `Constraint` class is a metaclass that defines the backend requirements for its instances. It verifies if the instance has been initialized with at least one of the specified backends (`\"torch\"`). \n\n**Key Details:**\n\n*   **Class Purpose:** Defines backend-specific constraints on class instances.\n*   **Implementation:** Verifies if the instance has been initialized with a valid backend using `requires_backends`.\n*   **Notable Dependencies/Requirements:** Uses `DummyObject` as its metaclass and specifies `\"torch\"` as its required backend.\n\n**Method Signatures:**\n\n*   `__init__`: Initializes the constraint instance, verifying the specified backend requirements."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:242-246
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 246, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConstraintListState", "start_line": 242, "type": "class", "chroma:document": "CODE:\n\nclass ConstraintListState(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ConstraintListState`\n\n*   **Purpose:** Represents the state of a constraint list for a specific backend (currently \"torch\").\n*   **Key Implementation Details:**\n    *   Initializes with arguments `args` and `kwargs`, enforcing backend requirement \"torch\" using the `requires_backends` function.\n*   **Notable Dependencies:** None\n*   **Interfaces/Inherited:** \n    *   Implemented by `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:249-253
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 253, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DisjunctiveConstraint", "start_line": 249, "type": "class", "chroma:document": "CODE:\n\nclass DisjunctiveConstraint(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: DisjunctiveConstraint**\n\n**Purpose and Functionality:**\nThe `DisjunctiveConstraint` class is designed to represent a constraint that combines multiple backend-specific constraints. It enforces that at least one of the specified backends (`torch`) is required.\n\n**Implementation Details:**\n\n* The initialisation method (`__init__`) takes variable arguments (`*args` and `**kwargs`) but is not used in this implementation.\n* The `requires_backends` function checks if at least one of the expected backends is present, which enforces the requirement.\n\n**Dependency:**\n\n* The class uses the ` DummyObject` metaclass.\n \nNote: Further analysis would be required to fully understand the functionality and requirements of the `DisjunctiveConstraint` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:256-260
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 260, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncoderNoRepeatNGramLogitsProcessor", "start_line": 256, "type": "class", "chroma:document": "CODE:\n\nclass EncoderNoRepeatNGramLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`EncoderNoRepeatNGramLogitsProcessor` is a class used for processing encoder no repeat N-gram logits. \n\n**Key Details:**\n\n* **Primary Purpose:** Processes encoder no repeat N-gram logits.\n*   The primary purpose of this class can be broken down into:\n    * Using the `torch` backend.\n\n### Class Signature\n\n*   `EncoderNoRepeatNGramLogitsProcessor(metaclass=DummyObject)`: An instance of `EncoderNoRepeatNGramLogitsProcessor`\n\nThe class constructor accepts multiple arguments, but one notable implementation detail is:\n\n`\nrequires_backends(self, [\"torch\"]):\n`\n\nThis indicates that only the `torch` backend will be used.\n\n### Additional Dependencies\n\n*   No external dependencies are mentioned in this snippet; however:\n    *   This class likely depends on the `DummyObject` metaclass for construction.\n    *"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:263-267
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 267, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncoderRepetitionPenaltyLogitsProcessor", "start_line": 263, "type": "class", "chroma:document": "CODE:\n\nclass EncoderRepetitionPenaltyLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`EncoderRepetitionPenaltyLogitsProcessor`: A class that processes logits for repetition penalty encoding using the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` backend requirement\n* Calls `requires_backends` function to enforce required backend versions\n\n**Notable Dependencies/Requirements:**\n\n* `_backends = [\"torch\"]`\n* External dependency: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:270-274
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 274, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EosTokenCriteria", "start_line": 270, "type": "class", "chroma:document": "CODE:\n\nclass EosTokenCriteria(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: EosTokenCriteria**\n\n### Purpose and Functionality\n`EosTokenCriteria` is a class that defines token criteria for a specific backend (`torch`). Its primary purpose is to validate the presence of required backends.\n\n### Key Implementation Details\n\n* Enforces backend compatibility using `_requires_backends` method, which checks for the presence of [\"torch\"].\n* Used metaclass `DummyObject`.\n\n### Dependencies and Requirements\nNone explicitly stated. However, as indicated by the `_backends` variable, `EosTokenCriteria` is designed to work with the `torch` backend.\n\nNo external dependencies listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:277-281
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 281, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EpsilonLogitsWarper", "start_line": 277, "type": "class", "chroma:document": "CODE:\n\nclass EpsilonLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`EpsilonLogitsWarper` class:\n=====================================\n\nClass Purpose: \nThe `_EpsilonLogitsWarper` class is designed to warps the logits of a model using epsilon regularization. It meets the backend requirement by accepting only \"torch\".\n\nKey Implementation Details:\n\n*   Initializes with `args`, `kwargs` and requires \"torch\" backend.\n*   `_requires_backends` method ensures the backend is set to [\"torch\"].\n\nNotable Dependencies and Requirements:\n\n*   External dependency: `torch`\n*   Required classes/functions:\n    *   `_requires_backends` function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:284-288
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 288, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EtaLogitsWarper", "start_line": 284, "type": "class", "chroma:document": "CODE:\n\nclass EtaLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`EtaLogitsWarper` class:\n=================\nA custom class that warps logits for model training with the `torch` backend.\n\n### Main Purpose and Functionality\n\nWarms logits to prepare input data for training.\n\n### Key Implementation Details\n\n*   Implements a metaclass `DummyObject`, likely to bypass some Python feature restrictions.\n*   Requires `torch` as the default backend in its initialization (`__init__` method).\n*   Calls `requires_backends()` function with the \"torch\" backend list, ensuring compatibility.\n\n### Notable Dependencies and Requirements\n\n*   References `torch` as a required backend for this class.\n*   No external dependencies listed. If `DummyObject` or other internal functions are used by this class implicitly (besides what has been shown here), they would need be noted.\n\nThis summary accurately captures the main purpose, key implementation details, and any necessary dependencies. It's written in a concise manner to meet the requested standards for clarity and focus."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:291-295
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 295, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ExponentialDecayLengthPenalty", "start_line": 291, "type": "class", "chroma:document": "CODE:\n\nclass ExponentialDecayLengthPenalty(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ExponentialDecayLengthPenalty`\n\n*   Primary Purpose: Enforces length-based penalties using exponential decay.\n*   **Metaclass:** `DummyObject`\n*   **Backends Required:** `torch`\n*   **Dependencies:** None explicit, but references \"requires_backends\" and \"DummyObject\".\n\n**Key Implementation Details:**\n\n*   `__init__`: Initializes the penalty with optional arguments (`*args`, `**kwargs`).\n*   Mandatory backend verification using `requires_backends`.\n*   Unspecified `Provide` method call; likely a placeholder or custom implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:298-302
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 302, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ForcedBOSTokenLogitsProcessor", "start_line": 298, "type": "class", "chroma:document": "CODE:\n\nclass ForcedBOSTokenLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ForcedBOSTokenLogitsProcessor` is a class that processes BoSToken features for BERT-like models (`PyTorch` backend). It requires the `torch` backend to be initialized and checks for its availability.\n\n**Key Implementation Details:**\n\n* Initializes with `requires_backends` method, which specifies the required backend: `[\"torch\"]`.\n* Uses `DummyObject` metaclass, but no implementation details are provided in this class.\n \n**Notable Dependencies or Requirements:**\n\n* `PyTorch` library (backend)\n* `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:305-309
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 309, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ForcedEOSTokenLogitsProcessor", "start_line": 305, "type": "class", "chroma:document": "CODE:\n\nclass ForcedEOSTokenLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ForcedEOSTokenLogitsProcessor**\n\n*   **Main Purpose:** A processor class for forcing East Asian script token logit output in NLP models using the specified backend (`torch`).\n*   **Key Implementation Details:**\n    *   Initially requires `torch` as a supported backend.\n    *   Utilizes the `requires_backends` decorator to enforce required backends on instance initialization.\n*   **Notable Dependencies:**\n    *   `_backends` class attribute referencing supported backends (`[\"torch\"]`).\n    *   The `DummyObject` metaclass used for defining this class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:312-316
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 316, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GenerationMixin", "start_line": 312, "type": "class", "chroma:document": "CODE:\n\nclass GenerationMixin(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`GenerationMixin`\n================\n\nA mixin class providing a common base for generation-related functionality. Its primary purpose is to ensure the presence of specific backends.\n\n### Key Implementation Details\n\n* Requires `torch` backend in its implementation (`requires_backends(self, [\"torch\"])`)\n* Uses `DummyObject` as its metaclass\n* Exposes the default backend(s) via `_backends` class attribute (`_backends = [\"torch\"]`)\n\n### Dependencies and Requirements\n\n* `torch` (backend dependency)\n* ` DummyObject` (metaclass implementation)\n\nThis summary aims to provide a clear understanding of the class's purpose, key features, and dependencies, making it easier for developers to integrate this mixin into their codebase."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:319-323
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 323, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "HammingDiversityLogitsProcessor", "start_line": 319, "type": "class", "chroma:document": "CODE:\n\nclass HammingDiversityLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**HammingDiversityLogitsProcessor Class Summary**\n\n### Purpose and Functionality\nThe HammingDiversityLogitsProcessor is a class that processes logits using the Hamming diversity metric. It ensures compatibility with specific backends.\n\n### Key Details\n- **Initialization**: The processor takes no explicit arguments, relying on `requires_backends` to assert backend compatibility.\n- **Backend Dependence**: Specifically supports the \"torch\" backend.\n\n### Notable Dependencies and Requirements\n- **Backend**: torch (specifically)\n- **Interface Requirements**:\n  - `requires_backends`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:326-330
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 330, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "InfNanRemoveLogitsProcessor", "start_line": 326, "type": "class", "chroma:document": "CODE:\n\nclass InfNanRemoveLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`InfNanRemoveLogitsProcessor` class: This processor removes logits in cases where their values contain infinite or NaN (Not-a-Number) values. It is designed to work with the `torch` backend.\n\n**Key Implementation Details**\n\n* Uses `requires_backends` function to ensure compatibility with the `torch` backend.\n* Implemented by `InfNanRemoveLogitsProcessor` class, utilizing a dummy object metaclass (`DummyObject`).\n\n**Dependencies and Requirements**\n\n* `torch` (backend requirement)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:333-337
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 337, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "LogitNormalization", "start_line": 333, "type": "class", "chroma:document": "CODE:\n\nclass LogitNormalization(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `LogitNormalization` class is a metaclass that implements logit normalization for neural networks. It ensures compatibility with the \"torch\" backend by leveraging the `requires_backends` function.\n\n**Key Details:**\n\n* Primary purpose: Enforces logit normalization using the provided backends.\n* Implementation details:\n\t+ Initializes the backend system (`requires_backends`)\n\t+ Requires the `\"torch\"` backend for normalization.\n* Notable dependencies and requirements:\n\t+ Uses the `[requires_backends` custom function to validate backend compatibility.\n\t+ Assumes availability of the `torch` backend for logit normalization.\n\n**Note:** The existence of a `DummyObject` metaclass is implied but not explicitly detailed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:340-344
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 344, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "LogitsProcessor", "start_line": 340, "type": "class", "chroma:document": "CODE:\n\nclass LogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**LogitsProcessor Class Summary**\n\nThe `LogitsProcessor` class is designed to process logit values with specific backend integration. Its primary purpose is to handle data processing for neural networks.\n\n**Key Functionality:**\n\n- Processes logit values using specified backends\n- Ensures required backend \"torch\" is met\n\n**Implementation Details:**\n\n- Requires the `requires_backends` function, which is not shown in this snippet\n- Uses a `DummyObject` metaclass with a predefined list of supported backends (_backends)\n\n**Notable Dependencies/Requirements:**\n\n- Requires the `torch` backend for operation"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:347-351
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 351, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "LogitsProcessorList", "start_line": 347, "type": "class", "chroma:document": "CODE:\n\nclass LogitsProcessorList(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`LogitsProcessorList`: A class that processes logit values using a list of predefined backends.\n\n**Key Features**\n\n* Primarily designed to work with the \"torch\" backend\n* Manages a list of `LogitsProcessor` instances for efficient evaluation\n\n**Initialization**\n\n- Requires torch as a supported backend (`requires_backends(self, [\"torch\"])`)\n\n**Technical Details**\n\n- Utilizes a metaclass (`DummyObject`) \n- Inherited from an unnamed class (indicating internal implementation details)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:354-358
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 358, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "LogitsWarper", "start_line": 354, "type": "class", "chroma:document": "CODE:\n\nclass LogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`LogitsWarper`: A utility class that warps logits (output) from multiple backends to conform to a single backend's API. Specifically, it supports the `torch` backend.\n\n**Key Implementation Details:**\n\n* Requires all input data to support the `torch` backend.\n* Initializer (`__init__`) method is overloaded with variable arguments for future backend support extension.\n\n**Notable Dependencies and Requirements:**\n\n* ` torch`: The primary supported backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:361-365
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 365, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MaxLengthCriteria", "start_line": 361, "type": "class", "chroma:document": "CODE:\n\nclass MaxLengthCriteria(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: MaxLengthCriteria**\n\n### Purpose and Functionality\nThe `MaxLengthCriteria` class is a backend-agnostic metadata constraint that ensures maximum length of input parameters passed to models.\n\n### Implementation Details\nThe constructor `__init__` initializes the class, requiring the presence of a \"torch\" backend, which defines how lengths are enforced. No explicit length enforcement is performed in this class.\n\n### Dependencies and Requirements\n\n*   Backend: `torch`\n*   Interface: None\n*   Inherited Interfaces: None implemented"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:368-372
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 372, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MaxTimeCriteria", "start_line": 368, "type": "class", "chroma:document": "CODE:\n\nclass MaxTimeCriteria(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThis `_MaxTimeCriteria` class validates whether it has the required backend (`torch`) before using external functions.\n\n**Key Details:**\n\n- Functionality: Verifies presence of specific backend.\n- Implementation:\n  - `requires_backends` method is used to check for supported backends.\n  - Backend validation is performed during object initialization.\n  \n **Technical Terms and Acronyms Used:**\n\n*   `_MaxTimeCriteria`: A custom class that validates required backends before usage.\n*   `DummyObject`: The metaclass used by the `_MaxTimeCriteria` class.\n\n**Dependencies/Requirements:**\n\n- External dependency on `torch`.\n- No inherited or implemented interfaces noted."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:375-379
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 379, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MinLengthLogitsProcessor", "start_line": 375, "type": "class", "chroma:document": "CODE:\n\nclass MinLengthLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MinLengthLogitsProcessor Class Summary**\n\n*   **Class Purpose:** MinLengthLogitsProcessor is a class that processes logits with a minimum length constraint for a PyTorch backend (currently only \"torch\").\n\n**Implementation Details:**\n\n*   The `__init__` method initializes the processor and checks if the required backend (\"torch\") is available.\n*   It utilizes the `requires_backends` function to verify the presence of the specified backend.\n\n**Notable Dependencies/Requirements:**\n\n*   `DummyObject`: the metaclass used for this class definition.\n*   `requires_backends` function (no external dependencies listed).\n*   PyTorch library (backend-dependent)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:382-386
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 386, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MinNewTokensLengthLogitsProcessor", "start_line": 382, "type": "class", "chroma:document": "CODE:\n\nclass MinNewTokensLengthLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`MinNewTokensLengthLogitsProcessor` is a class for calculating logits in a language model. It's initialized with the `torch` backend and requires this backend to be available.\n\n**Key Details:**\n\n- Primary purpose: Calculate logits\n- Key implementation details:\n  - Implemented using `RequiresBackends` with compatible backend `\"torch\"`\n- Notable dependencies:\n  - `torch`: backend dependency required for initialization\n  - `DummyObject`: metaclass used for class definition"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:389-393
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 393, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MinPLogitsWarper", "start_line": 389, "type": "class", "chroma:document": "CODE:\n\nclass MinPLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*Class Name: `MinPLogitsWarper`\n*Purpose: A class for performing logits warping using the specified backend (`torch`).\n*Main Functionality:\n\t+ Initializes the instance with required backends and performs input validation.\n\n**Implementation Details:**\n \n * Primary Algorithm/Pattern: Logits Warping\n * Key Method Signatures:\n   - `__init__`: Initializes instance with inputs.\n * Notable Dependencies/Requirements:\n    - Backend: `torch`\n    - Validation function: `{requires_backends}` \n\n*Note*: The class definition uses a metaclass (`DummyObject`) and relies on external validation functions."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:396-400
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 400, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NoBadWordsLogitsProcessor", "start_line": 396, "type": "class", "chroma:document": "CODE:\n\nclass NoBadWordsLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary: NoBadWordsLogitsProcessor Class**\n\n*   **Purpose**: A class for processing logit filters with no bad words functionality, utilizing the Torch backend (`torch`).\n*   **Implementation Details**:\n    *   Requires the `torch` backend at initialization using the `requires_backends` method.\n*   **Dependencies/Requirements**:\n    *   External dependency: `torch`\n    *   Metaclass used: `DummyObject`\n\nNote: The specific implementation of the class (methods, etc.) is not included in this summary as it was not provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:403-407
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 407, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NoRepeatNGramLogitsProcessor", "start_line": 403, "type": "class", "chroma:document": "CODE:\n\nclass NoRepeatNGramLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n`NoRepeatNGramLogitsProcessor` is a class that processes NGrams (N-gram representations) with the primary purpose of producing logit values without repetition.\n\n**Key Implementation Details:**\n\n*   Initializes a backend dependency check for torch.\n*   Requires the specified backend (\"torch\") to function properly.\n*   Utilizes `dummy_object` as its metaclass.\n\n**Dependencies and Requirements:**\n\n*   **Related Classes and Functions:**\n    *   `requires_backends` ( referenced but not defined in this code snippet)\n*   **External Dependencies:**\n    *   torch\n\nThis class assumes the existence of a `DummyObject` metaclass (`DummyObject`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:410-414
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 414, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "PhrasalConstraint", "start_line": 410, "type": "class", "chroma:document": "CODE:\n\nclass PhrasalConstraint(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n*Class PhrasalConstraint*\n================================\n\n*Meta-class*: `DummyObject`\n*Primary Purpose:* Enforces compatibility with specific backends (`torch`) for the phrasal constraint class.\n\n**Key Implementation Details:**\n- Initializes the class instance while ensuring the presence of the required backend using `requires_backends` method.\n- Repeatedly called requirements check in initialization to raise an error if any backend is not met.\n\n**Notable Dependencies:**\n- `dummy_object`: The meta-class used to create the PhrasalConstraint class (`metaclass=DummyObject`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:417-421
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 421, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "PrefixConstrainedLogitsProcessor", "start_line": 417, "type": "class", "chroma:document": "CODE:\n\nclass PrefixConstrainedLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`PrefixConstrainedLogitsProcessor`\n\n*   Class type: `mixin`\n*   Main Purpose:\n    -  Process logits with prefix constraint for specified backends (currently `torch`)\n*   Primary purpose: applies prefix constraints to logits data for efficient processing on specific devices.\n\n\n**Key Implementation Details**\n\n*   Uses the `requires_backends` method to enforce backend compatibility.\n*   Does not define any additional methods beyond initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:424-428
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 428, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RepetitionPenaltyLogitsProcessor", "start_line": 424, "type": "class", "chroma:document": "CODE:\n\nclass RepetitionPenaltyLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: RepetitionPenaltyLogitsProcessor**\n\n### Purpose and Functionality\n\nThe `RepetitionPenaltyLogitsProcessor` is a class used to apply repetition penalty logits in a machine learning model. It inherits from the `DummyObject` metaclass.\n\n### Key Implementation Details\n\n* Applies repetition penalty using torch as the backend.\n* The `requires_backends` function checks if the required backend (in this case, \"torch\") is supported.\n\n### Notable Dependencies and Requirements\n\n* Torch as the primary backend (required for implementation)\n \n This summary provides a concise overview of the class's purpose, key implementation details, and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:431-435
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 435, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SequenceBiasLogitsProcessor", "start_line": 431, "type": "class", "chroma:document": "CODE:\n\nclass SequenceBiasLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `SequenceBiasLogitsProcessor`**\n\nThe `SequenceBiasLogitsProcessor` class is a processor responsible for processing sequence bias logits. It inherits from the `DummyObject` metaclass.\n\n**Primary Purpose:** Process sequence bias logits using one or more supported backends.\n\n**Key Implementation Details:**\n\n*   Initializes a backend framework with support for the \"torch\" backend.\n*   Requires specific backends to be installed before instantiation.\n\n**Notable Dependencies/Requirements:**\n\n*   `DummyObject` metaclass\n*   `torch` library (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:438-442
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 442, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "StoppingCriteria", "start_line": 438, "type": "class", "chroma:document": "CODE:\n\nclass StoppingCriteria(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`StoppingCriteria` class: Evaluates stopping conditions for specific backends. Its main purpose is to ensure the use of required backends (\"torch\") in its operations.\n\n**Key Implementation Details**\n\n* Initializes with `dummyObject` metaclass, which implicitly inherits from `object`.\n* Requires \"torch\" backend to be provided (`requires_backends(self, [\"torch\"])`) at object initialization.\n\n**Notable Dependencies/Requirements**\n\n* Backend: torch\n* Inherited Interfaces: None ( DummyObject metaclass)\n* Implementated Interfaces: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:445-449
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 449, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "StoppingCriteriaList", "start_line": 445, "type": "class", "chroma:document": "CODE:\n\nclass StoppingCriteriaList(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: StoppingCriteriaList**\n\n*   **Purpose and Functionality:** The `StoppingCriteriaList` class is designed to manage a list of stopping criteria for a specific backend (`torch`) in a machine learning or deep learning context.\n\n    The underlying `DummyObject` metaclass ensures the creation of instances with attributes defined by keyword only, allowing for flexible initialization via additional arguments.\n\n**Key Implementation Details:**\n\n*   Requires specification of required backends (in this case, just `\"torch\"`) using the `requires_backends` method.\n*   Instance variables can be accessed and modified dynamically through its metaclass (`_backends` is implicitly created).\n\n    -   **Method Signature:** `def __init__(self, *args, **kwargs):`\n\n**Notable Dependencies or Requirements:**\n\n*   The class relies on the `dummy_objectmetaclass` module (`DummyObject`) from which it inherits.\n*   Requires access to related classes and functions via exact names as specified in its class structure."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:452-456
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 456, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "StopStringCriteria", "start_line": 452, "type": "class", "chroma:document": "CODE:\n\nclass StopStringCriteria(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`StopStringCriteria` is a class that uses the `torch` backend. It inherits from `DummyObject`.\n\n**Key Details:**\n\n* Initializes with arbitrary arguments using keyword-only parameters (`*args`, `**kwargs`)\n* Requires the `torch` backend, ensuring it's available for execution\n\t+ Backend requirements are managed by the `requires_backends` function\n\n**Notable Dependencies and Requirements:**\n\n* Used backend(s): `torch`\n* Implemented interface/interface: `DummyObject`\n\nNote: No external dependencies or classes/functions are referenced in this code snippet. If you'd like me to analyze a different part of the class, please provide it."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:459-463
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 463, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SuppressTokensAtBeginLogitsProcessor", "start_line": 459, "type": "class", "chroma:document": "CODE:\n\nclass SuppressTokensAtBeginLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: SuppressTokensAtBeginLogitsProcessor**\n\n### General Information\nThis class is a Python implementation of a data processor used to filter out tokens at the beginning of logits.\n\n### Primary Purpose\nThe primary purpose of this class is to suppress unwanted tokens from a PyTorch backend output.\n\n### Key Implementation Details\n\n*   **Requiring Backends**: The class ensures that only the \"torch\" backend is required for operation. (`requires_backends(self, [\"torch\"])`)\n\n### Notable Dependencies and Requirements\n-   `DummyObject`: A metaclass used to define DummyObjects.\n-   `torch`, referenced through `self._backends`.\n-   No external dependencies are mentioned.\n\n### Method Signatures and Interfaces\n*   `__init__`: Initializes the processor with optional arguments (`*args` and `**kwargs`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:466-470
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 470, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SuppressTokensLogitsProcessor", "start_line": 466, "type": "class", "chroma:document": "CODE:\n\nclass SuppressTokensLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere's a concise summary of the provided code chunk:\n\n**`SuppressTokensLogitsProcessor` Class Summary**\n\n*   **Purpose:** This class is designed to suppress logging-related tokens in logits data using the `torch` backend as specified by the `_backends` attribute.\n*   **Key Implementation:**\n    *   Initializes with optional arguments and checks if required backends (`torch`) are specified.\n*   **Dependencies/Interfaces:**\n    +   Uses the `DummyObject` metaclass.\n    +   Requires the `\"torch\"` backend to function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:473-477
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 477, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SynthIDTextWatermarkDetector", "start_line": 473, "type": "class", "chroma:document": "CODE:\n\nclass SynthIDTextWatermarkDetector(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`SynthIDTextWatermarkDetector` class:\n\n*   Purpose: Watermark detector for Synth ID text data using `torch` backend.\n    The primary purpose is to detect watermarks in synthesized text IDs.\n\n**Key Implementation Details**\n\n*   Uses the `torch` backend, allowing it to leverage PyTorch's computation capabilities.\n*   Specifies a dependency on the `requires_backends` decorator, which ensures the correct backend is used when initializing the detector."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:480-484
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 484, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SynthIDTextWatermarkingConfig", "start_line": 480, "type": "class", "chroma:document": "CODE:\n\nclass SynthIDTextWatermarkingConfig(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n*Class: `SynthIDTextWatermarkingConfig`\n*Purpose: Configures Synth ID text watermarking using the specified backend (`torch`).\n*Implementation:\n\t+ Requires backend support via `requires_backends`\n\t+ Initializes with `args` and `kwargs`, but does not assign values to instance variables\n*Dependencies:\n\t+ `torch` backend\n\n**Key Insights**\n\n* Instance initialization uses `__init__()`, but no instance variables are assigned.\n* `requires_backends()` is called at class definition level to validate required backends."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:487-491
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 491, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "SynthIDTextWatermarkLogitsProcessor", "start_line": 487, "type": "class", "chroma:document": "CODE:\n\nclass SynthIDTextWatermarkLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`SynthIDTextWatermarkLogitsProcessor` is a class that processes logits for synthesize id text watermark with `torch` backend. It initializes the backend and verifies required dependencies.\n\n**Key Details:**\n\n- `_backends = [\"torch\"]`: Supported backends\n- `requires_backends(self, [\"torch\"])`: Verifies compatibility with required backend\n\n**Notable Dependencies:**\n\n- `DummyObject`: Metaclass used for instantiation\n- `torch`: Required backend"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:494-498
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 498, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TemperatureLogitsWarper", "start_line": 494, "type": "class", "chroma:document": "CODE:\n\nclass TemperatureLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Temperature Logits Warper Class Summary**\n\n* **Purpose**: A class `TemperatureLogitsWarper` that warps temperature logits for input transformation, specifically designed to work with the \"torch\" backend.\n\n**Key Implementation Details:**\n\n* Uses the `DummyObject` metaclass.\n* Initializes with a list of required backends (\"torch\").\n\n**Notable Dependencies or Requirements:**\n\n* Requires \"torch\" backend implementation (referenced by `requires_backends` method).\n \n**Additional Context:** This class likely implements temperature scaling to regulate model predictions."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:501-505
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 505, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TopKLogitsWarper", "start_line": 501, "type": "class", "chroma:document": "CODE:\n\nclass TopKLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `TopKLogitsWarper` class is a metaclass class that warps log probabilities of a classification model (backed by PyTorch) to top-k values. It ensures the required backend is available before initialization.\n\n**Key Details:**\n\n* Primary purpose: Warp log probabilities to top-k values in supervised classification.\n* Key implementation detail: Requiring PyTorch as the backend for warping operations.\n* Method signature: `__init__(self, *args, **kwargs)`, which calls `requires_backends(self, [\"torch\"])`.\n\n**Dependencies and Requirements:**\n\n* Backends: Requires PyTorch (`torch` as a class/function).\n* Inheritance/Interface: None specified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:508-512
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 512, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TopPLogitsWarper", "start_line": 508, "type": "class", "chroma:document": "CODE:\n\nclass TopPLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nClass `TopPLogitsWarper` is a transformation class for logit warping, utilizing the PyTorch backend. It initializes with a variable number of arguments and ensures that only the \"torch\" backend is required.\n\n### Main Purpose and Functionality\n\n* Transformation class for logit warping\n* Uses PyTorch as the primary backend\n\n### Key Implementation Details\n\n* Initializes `Requires` object with required backends (`torch`)\n* Accepts variable arguments and keyword arguments\n\n### Notable Dependencies or Requirements\n\n* External dependency: PyTorch (specifically, `\"torch\"` backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:515-519
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 519, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TypicalLogitsWarper", "start_line": 515, "type": "class", "chroma:document": "CODE:\n\nclass TypicalLogitsWarper(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`TypicalLogitsWarper` is a class that wraps logits into a warping dimension using `torch` backend.\n\n**Key Details:**\n\n- Initializes with optional arguments (`*args`, `**kwargs`)\n- Requires `torch` as a backend\n- Applies `requires_backends` function to validate backend compatibility\n\n**Notable Dependencies:**\n\n- `DummyObject` metaclass (inherited)\n- `torch` library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:522-526
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 526, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "UnbatchedClassifierFreeGuidanceLogitsProcessor", "start_line": 522, "type": "class", "chroma:document": "CODE:\n\nclass UnbatchedClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: UnbatchedClassifierFreeGuidanceLogitsProcessor**\n\n**Purpose:** A logging processor for batchless classification guidance logits, leveraging the PyTorch backend.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` backend requirement.\n* Supports object creation via metaclass `DummyObject`.\n\n**Notable Dependencies and Interfaces:**\n- Uses `PyTorch` as the primary backend.\n- Inherents from `DummyObject` metaclass for class initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:529-533
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 533, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "WatermarkDetector", "start_line": 529, "type": "class", "chroma:document": "CODE:\n\nclass WatermarkDetector(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `WatermarkDetector` class is a custom detector that relies on the `torch` backend. It initializes with an optional argument dictionary (`*args`, `**kwargs`) and ensures the presence of the `torch` backend.\n\n**Key Implementation Details:**\n\n- Relies on `torch` as a required backend\n  - `require_backends(self, [\"torch\"])`: Verifies the availability of `torch`\n\n**Notable Dependencies and Requirements:**\n\n- External dependency:\n  - `torch`\n- Inherited/Implemented interfaces:\n  None (none explicitly listed)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:536-540
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 540, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "WatermarkLogitsProcessor", "start_line": 536, "type": "class", "chroma:document": "CODE:\n\nclass WatermarkLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`WatermarkLogitsProcessor`\n\n**Purpose and Functionality**\nThe `WatermarkLogitsProcessor` class applies watermarking to logits data using the specified backend (`torch`). It initializes self with required backend support.\n\n**Key Implementation Details**\n\n* Initializes `self` with a check for required backend support using `requires_backends`.\n* Requires backend support for `torch`.\n\n**Notable Dependencies or Requirements**\n\n* Backend: `torch`\n* Interfaces/Inheritance: None\n* External Dependencies: torch library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:543-547
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 547, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "WhisperTimeStampLogitsProcessor", "start_line": 543, "type": "class", "chroma:document": "CODE:\n\nclass WhisperTimeStampLogitsProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: WhisperTimeStampLogitsProcessor**\n\n*   **Purpose:** Logit processor for timestamped inputs in the Whisper framework, utilizing the Torch backend.\n\n**Implementation Details:**\n\n*   The `WhisperTimeStampLogitsProcessor` class inherits from `DummyObject`.\n*   It requires the \"torch\" backend to be specified during initialization.\n*   The `requires_backends` function is used to enforce this requirement.\n\n**Notable Dependencies and Requirements:**\n\n*   External dependency: Torch backend (`torch`).\n*   Implemented interface: inherits from `DummyObject`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:550-554
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 554, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TorchExportableModuleWithStaticCache", "start_line": 550, "type": "class", "chroma:document": "CODE:\n\nclass TorchExportableModuleWithStaticCache(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**TorchExportableModuleWithStaticCache**\n\n**Main Purpose and Functionality:**\nThis class creates a Torch exportable module with static caching capabilities.\n\n**Key Implementation Details:**\n\n* The `__init__` method initializes the object, requiring the \"torch\" backend to be available.\n* A static cache is not defined in this snippet; it's likely implemented elsewhere in the class.\n\n**Notable Dependencies or Requirements:**\n\n* Torch (`_backends = [\"torch\"]`)\n* DummyObject (dummy metaclass)\n\n**Code Structure Notes:** \n* This class uses a dummy object as its metaclass, which suggests advanced customization capabilities.\n* Further investigation is needed to understand how caching and backends are implemented."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:557-558
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 558, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "convert_and_export_with_cache", "start_line": 557, "type": "function", "chroma:document": "CODE:\n\ndef convert_and_export_with_cache(*args, **kwargs):\n\n\nSUMMARY:\n**Function Summary**\n\n`convert_and_export_with_cache`\n=====================================\n\n*Primary Purpose*: This function facilitates the conversion and export of data using caching.\n\n**Key Details**:\n\n+ Uses the `requires_backends` decorator to enforce the use of the \"torch\" backend.\n+ Takes variable arguments with an optional keyword argument scheme (`*args, **kwargs`).\n\n**Notable Dependencies/Requirements**:\n\n+ Referred class: `convert_and_export_with_cache` (self)\n+ Inherited interface: None\n+ Implemented interfaces: None\n+ External dependencies:\n  - torch (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:564-568
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 568, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "PreTrainedModel", "start_line": 564, "type": "class", "chroma:document": "CODE:\n\nclass PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThis `PreTrainedModel` class is an abstract base class that defines a pre-trained model's metadata and initialization requirements. It requires the `torch` backend.\n\n**Key Details:**\n\n* Primary purpose: Define a pre-trained model with a specific backend requirement.\n* Key algorithm/pattern/design choice: Uses the `DummyObject` metaclass to enforce backend requirements.\n* Notable method signatures:\n\t+ `__init__(self, *args, **kwargs)`: Initializes the model, requiring the `torch` backend.\n\n**Notable Dependencies:**\n\n* Extends: `metaclass=DummyObject`\n* Requires backend: `torch`\n\nThis class serves as a foundation for pre-trained models and enforces specific requirements for its instantiation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:571-575
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 575, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForMaskedLM", "start_line": 571, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AlbertForMaskedLM**\n\n**Purpose:** implements the Albert model for masked language modeling.\n\n**Key Implementation Details:**\n\n* Utilizes the PyTorch backend (`torch`).\n* Requires the `torch` backend in its initialization process.\n\n**Notable Dependencies:** None reported.\n \n**Interfaces Implemented:** Requires the `train.py` module's `requires_backends` function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:578-582
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 582, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForMultipleChoice", "start_line": 578, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AlbertForMultipleChoice**\n\n**Main Purpose and Functionality**\nThe `AlbertForMultipleChoice` class is an implementation of the Albert model specialized for multiple-choice tasks. It uses the PyTorch backend.\n\n**Key Implementation Details**\n\n* The model requires at least one specified backend, with `torch` being a required backend.\n* The class inherits from `DummyObject`, specifying that it should be instantiated without passing any arguments (`__init__: *args, **kwargs: None`).\n\n**Notable Dependencies and Requirements**\n\n* Requires the `torch` backend for functionality\n* Inherited from `DummyObject`\n* Has no specific external dependencies noted."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:585-589
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 589, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForPreTraining", "start_line": 585, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AlbertForPreTraining**\n\n**Main Purpose and Functionality:** AlbertForPreTraining is a PyTorch-based pre-training model for natural language processing tasks.\n\n**Key Implementation Details:**\n\n*   Initializes the model with the required `torch` backend using the `requires_backends` function.\n*   Uses the `_backends` class variable to specify the supported backends.\n\n**Notable Dependencies and Requirements:**\n\n*   Requires the `torch` library as a dependency.\n*   Inherited Interface: Uses the `dummyobject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:592-596
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 596, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForQuestionAnswering", "start_line": 592, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AlbertForQuestionAnswering` class is a PyTorch-based implementation of the Albert model for question answering. The primary purpose is to provide a pre-trained language model for downstream NLP tasks.\n\n**Key Details:**\n\n* Utilizes the `torch` backend.\n* Requires additional backends (potentially not supported in this implementation).\n\n**Notable Dependencies/Requirements:**\n\n* Expects PyTorch (`\"torch\"`) as the primary backend.\n* No inherited or implemented interfaces specified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:599-603
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 603, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForSequenceClassification", "start_line": 599, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AlbertForSequenceClassification**\n\n*   **Main Purpose and Functionality:** This class implements the Albert model architecture for sequence classification tasks using the PyTorch backend.\n\n*   **Implementation Details:**\n    *   The `__init__` method initializes the class instance, requiring the PyTorch backend (`requires_backends(self, [\"torch\"])`).\n    *   It provides functionality to configure the class, likely setting up the model architecture and other necessary parameters.\n\n*   **Notable Dependencies/Requirements:**\n    *   `torch`: The primary dependency for this implementation.\n    *   `_DummyObject` (not shown in the snippet): A metaclass used by the AlbertForSequenceClassification class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:606-610
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 610, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertForTokenClassification", "start_line": 606, "type": "class", "chroma:document": "CODE:\n\nclass AlbertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AlbertForTokenClassification**\n\n*   **Purpose:** AlbertForTokenClassification is an instance of the Transformer model architecture for token classification tasks.\n*   **Primary Functionality:** Designed to perform sequence labeling and text classification.\n*   **Key Implementation Details:**\n\n    *   Utilizes the PyTorch backend, specified in `_backends = [\"torch\"]`.\n    *   **Initialization Method Signature:** `__init__(self, *args, **kwargs)`, which calls `requires_backends(self, [\"torch\"])` to ensure compatibility with chosen backends.\n*   **Notable Dependencies/Requirements:**\n\n    *   Inherited from the `Transformer` class.\n    *   Requires PyTorch (`_backends = [\"torch\"]`) for implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:613-617
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 617, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertModel", "start_line": 613, "type": "class", "chroma:document": "CODE:\n\nclass AlbertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n```markdown\n### Albert Model Class\n\nThe `AlbertModel` class is a wrapper around the `DummyObject` metaclass. It specifies the required backend as \"torch\".\n\n#### Key Implementation Details\n\n* The `_backends` attribute is set to [\"torch\"] during object initialization.\n```\n\n### RAG-Enabled Analysis:\n\n* \n    ```python\nclass AlbertModel(metaclass=DummyObject):\n  _backends = [\"torch\"]\n  \n  def __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:620-624
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 624, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlbertPreTrainedModel", "start_line": 620, "type": "class", "chroma:document": "CODE:\n\nclass AlbertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `AlbertPreTrainedModel` class is a pre-trained model using the Torch backend. It initializes with `*args` and `**kwargs`. The `requires_backends` function is called to ensure that the \"torch\" backend is supported.\n\n**Key Details:**\n\n- **Primary Purpose:** Implements Albert pre-trained model with Torch backend.\n- **Implementation Details:**\n  - Key Algorithm/Pattern: Use of pre-trained models with dynamic initialization.\n  - Important Method Signatures:\n    - `__init__(*args, **kwargs)`: Initializes the model with arbitrary arguments and keyword arguments.\n- **Notable Dependencies:**\n  - `DummyObject` (metaclass)\n  - `requires_backends` function\n  - `torch` backend\n\n**Additional Requirements/Interfaces:**\n\n* Inherited Interfaces:\n  - None specified\n* Implemented Interfaces: \n  - `AlbertPreTrainedModel` implements the model interface for dynamic initialization.\n\nNote: The specific details about `DummyObject`, `requires_backends` function and the exact interface implemented by `AlbertPreTrainedModel` are not provided in the given code snippet. They need to be deduced/fetched from surrounding context/code."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:627-628
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 628, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_albert", "start_line": 627, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_albert(*args, **kwargs):\n\n\nSUMMARY:\nHere is a concise summary of the given function:\n\n**Summary:**\n\n loads pre-trained weights for ALBERT model using TensorFlow with support for PyTorch backend.\n\n**Key Implementation Details:**\n\n* Loads model weights\n* Supports Torcha backends (PyTorch integration)\n\n**Dependencies and Requirements:**\n\n* `torch` backend supported\n* Inherits from `load_tf_weights_in_albert` function"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:631-635
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 635, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlignModel", "start_line": 631, "type": "class", "chroma:document": "CODE:\n\nclass AlignModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n### Class: `AlignModel`\n\n#### Purpose:\nThe primary purpose of the `AlignModel` class is to align models with compatible backends.\n\n#### Implementation Details:\n\n* Initialized with optional arguments `args` and `kwargs`.\n* Requires the \"torch\" backend to be specified during instantiation using the `requires_backends` function.\n* The `_backends` attribute defines a list of recommended backends, currently limited to [\"torch\"].\n\n#### Notable Dependencies:\n- The class leverages the `DummyObject` metaclass.\n- External dependency on \"torch\" backend is required for instantiation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:638-642
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 642, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlignPreTrainedModel", "start_line": 638, "type": "class", "chroma:document": "CODE:\n\nclass AlignPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AlignPreTrainedModel` class is a subclass of the built-in Python metaclass `DummyObject`. It's designed to be used with the Torch backend. The class initializes with the following:\n\n- Requires the \"torch\" backend to function (`requires_backends(self, [\"torch\"])`)\n- An `__init__` method taking variable arguments (`*args`) and keyword arguments (`**kwargs`)\n\nNote: The implementation of the `_backends`, `requires_backends`, and `DummyObject` are not shown in this code snippet.\n\nNo class or function names were found to summarize."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:645-649
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 649, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlignTextModel", "start_line": 645, "type": "class", "chroma:document": "CODE:\n\nclass AlignTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `AlignTextModel`:**\n\n### Purpose and Functionality\n\nThe `AlignTextModel` is a class that utilizes the PyTorch backend (`torch`) to provide alignment functionality.\n\n### Implementation Details\n\n*   Initializes with support for both positional and keyword arguments using `requires_backends`.\n*   The model requires the PyTorch engine and device.\n\n**Key Method Signatures:**\n\n*   `__init__(self, *args, **kwargs)`: Initializes the model instance with input arguments and keyword parameters.\n\n### Notable Dependencies\n\n*   `torch`: The required PyTorch backend.\n*   `DummyObject` (metaclass): A dummy object used to define the metaclass for this class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:652-656
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 656, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AlignVisionModel", "start_line": 652, "type": "class", "chroma:document": "CODE:\n\nclass AlignVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `AlignVisionModel`**\n\nThe `AlignVisionModel` class is a PyTorch-based vision model that utilizes the `torch` backend. It initializes with a set of required backends.\n\n**Key Implementation Details:**\n\n- Initializes with requirements for `torch` backend.\n- Uses the `requires_backends` function to enforce backend support at object creation.\n\n**Notable Dependencies:**\n\n- Requires Python's PyTorch library (`torch`) as a dependency."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:659-663
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 663, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AltCLIPModel", "start_line": 659, "type": "class", "chroma:document": "CODE:\n\nclass AltCLIPModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AltCLIPModel`: A class that models an alternative CLIP ( Contrastive Learned Vision Transformer) model using the `torch` backend.\n\n**Key Details**\n\n* Implemented with `DummyObject` metaclass\n* Requires `torch` as backend, indicated by `requires_backends(self, [\"torch\"])`\n\n**Notable Dependencies**\n\n* External dependencies: `torch`\n* Interfaces implemented: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:666-670
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 670, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AltCLIPPreTrainedModel", "start_line": 666, "type": "class", "chroma:document": "CODE:\n\nclass AltCLIPPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`AltCLIPPreTrainedModel`\n\n*   A pre-trained model for alternative CLIP functionality.\n\n**Implementation Details:**\n\nThe class inherits from `DummyObject` and has an `__init__` method that:\n*   Verifies the required backend is \"torch\" using the `requires_backends` function.\n*   Initializes the model with optional arguments (`*args` and `**kwargs`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:673-677
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 677, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AltCLIPTextModel", "start_line": 673, "type": "class", "chroma:document": "CODE:\n\nclass AltCLIPTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AltCLIPTextModel` class is a text-based model that utilizes the TORCH backend. It enforces the use of TORCH at instantiation through the `requires_backends` function.\n\n**Key Details:**\n\n* `requires_backends`: checks if TORCH is available, throwing an error if not.\n\n**Notable Dependencies and Requirements:**\n\n* TORCH\n* DummyObject (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:680-684
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 684, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AltCLIPVisionModel", "start_line": 680, "type": "class", "chroma:document": "CODE:\n\nclass AltCLIPVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: AltCLIPVisionModel**\n\nThe `AltCLIPVisionModel` class is a PyTorch-based vision model designed for computer vision tasks. Its primary purpose is to provide vision capabilities using the CLIP (Contrastive Loss in Pre-training) architecture.\n\n```python\n# Parameters: *\n\nclass AltCLIPVisionModel(metaclass=DummyObject):\n    \"\"\"\n    A PyTorch-based vision model using the CLIP architecture.\n    \n    Primary Purpose:\n    - Provides computer vision functionality.\n    - Utilizes the CLIP architecture for contrastive learning.\n    \"\"\"\n\n    # Backends Supported\n    _backends = [\"torch\"]  # Only supports PyTorch as a backend\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Initializes the model with required backends and checks their support.\n        \n        Implemented Interfaces:\n        - `requires_backends()`: Verifies if the model can run on the specified backends.\n        \"\"\"\n\n        # Checks if torch is supported as a backend\n        requires_backends(self, [\"torch\"])\n```\n\n**Key Implementation Details:**\n\n*   **CLS Index**: The CLS index is used to determine the input class labels for the vision model.\n\n**Notable Dependencies and Requirements:**\n\n*   `torch`: Required as a backend for the `AltCLIPVisionModel`. Other backends are not currently supported.\n*   None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:687-691
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 691, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AriaForConditionalGeneration", "start_line": 687, "type": "class", "chroma:document": "CODE:\n\nclass AriaForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AriaForConditionalGeneration` class is designed for conditional generation using the `torch` backend.\n\n*   **Primary Purpose:** Conditionally generates output based on specific requirements (e.g., requires specified backends).\n\n**Key Implementation Details:**\n\n*   The `_backends` attribute specifies that only `torch` is supported.\n*   In the constructor (`__init__`), it checks for required backends using the `requires_backends` function and ensures at least one of `\"torch\"` is provided.\n\n**Notable Dependencies or Requirements:**\n\n*   This class uses the `DummyObject` metaclass.\n*   External dependency on `torch`.\n*   Implementation inherits from an unspecified base class (due to the lack of information).\n*   No explicitly mentioned dependencies or interfaces are inherited."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:694-698
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 698, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AriaPreTrainedModel", "start_line": 694, "type": "class", "chroma:document": "CODE:\n\nclass AriaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\nAria Pre-Trained Model class (`AriaPreTrainedModel`) that initializes with Torch backend support. It requires the use of the \"torch\" backend during initialization.\n\n**Key Points:**\n\n* **Class Purpose**: Aria Pre-Trained Model\n* **Primary Functionality**: Initialize and set up the model with the specified backend.\n* **Dependency on Torch Backend**, required for class initialization.\n \n\n(No notable dependencies or interfaces were found in this code snippet. Please let me know if you have any further pieces of code to analyze)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:701-705
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 705, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AriaTextForCausalLM", "start_line": 701, "type": "class", "chroma:document": "CODE:\n\nclass AriaTextForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class AriaTextForCausalLM**\n\n*   Purpose: Provides a backend for causal language modeling using the \"torch\" library.\n\n**Key Implementation Details**\n\n*   Requires \"torch\" as a dependency for initialization.\n*   Inherits from `DummyObject` metaclass for class definition.\n\n**Notable Dependencies/Requirements**\n\n*   External Dependency: torch (`torch` is required)\n*   Implemented Interface: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:708-712
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 712, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AriaTextModel", "start_line": 708, "type": "class", "chroma:document": "CODE:\n\nclass AriaTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n**Class:** `AriaTextModel`\n\n**Purpose and Functionality:** An Aria text model class using the Torch backend.\n\n**Key Implementation Details:**\n\n* Initializes with optional arguments `args` and `kwargs`.\n* Verifies that the Torch backend is required, throwing an error if not present.\n\n**Notable Dependencies/Requirements:**\n\n* `[Torch Backend]`: Requires the use of Torch as a backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:715-719
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 719, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AriaTextPreTrainedModel", "start_line": 715, "type": "class", "chroma:document": "CODE:\n\nclass AriaTextPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AriaTextPreTrainedModel` class is a pre-trained text model utilizing the `torch` backend. It defines an initializer that sets up the required `torch` backend and ensures proper initialization by calling the `requires_backends` function.\n\n**Key Details:**\n\n* Metaclass: `DummyObject`\n* Primary purpose: Utilize the `torch` backend\n* Key algorithm/design choice: Uses the `requires_backends` function to set up the required backend\n\n**Implementation Note:** \n\n- `requires_backends(self, [\"torch\"])`: Ensures proper initialization with the specific `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:722-726
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 726, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ASTForAudioClassification", "start_line": 722, "type": "class", "chroma:document": "CODE:\n\nclass ASTForAudioClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ASTForAudioClassification` class provides an Abstract Syntax Tree (AST) solution for audio classification, utilizing the `torch` backend. The constructor ensures compatibility with the targeted backend by validating dependencies.\n\n**Key Highlights:**\n\n*   Primary purpose: Create AST-based solutions for audio classification.\n*   Backend support: Specifically designed to work with `torch`.\n*   Dependency validation: Verifies presence and correctness of required backends.\n\n**Implementation Details:**\n\n*   `requires_backends` method is used to validate compatibility with specified backends (`\"torch\"`).\n*   `_backends` class attribute specifies the target implementation backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:729-733
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 733, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ASTModel", "start_line": 729, "type": "class", "chroma:document": "CODE:\n\nclass ASTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ASTModel` class is a metaclass that defines a custom model backend system. It requires the \"torch\" backend to be used.\n\n**Key Details:**\n\n* Metaclass: `DummyObject`\n* Required Backend: `torch`\n* Primary Purpose: Provide backend-specific configuration for the AST model\n\n**Method Signature:**\n\n* `__init__(self, *args, **kwargs)`\n\nNote: The exact implementation of the requires_backends function is not provided in this snippet. For a complete understanding, additional context would be required."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:736-740
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 740, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ASTPreTrainedModel", "start_line": 736, "type": "class", "chroma:document": "CODE:\n\nclass ASTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ASTPreTrainedModel` class is a PyTorch-based meta model for pre-trained models. It inherits from `DummyObject`.\n\n**Primary Purpose**: Defines a base class for pretrained models using the Torch backend.\n\n**Key Implementation Details**:\n\n- Requires at least the `torch` backend.\n  \n**Notable Dependencies and Requirements**:\n\n- Inherited from `DummyObject`\n- Requires `torch` backend\n- No external dependencies specified"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:875-879
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 879, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoBackbone", "start_line": 875, "type": "class", "chroma:document": "CODE:\n\nclass AutoBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n\"AutoBackbone class defines a metaclass implementing `torch` backend using the `requires_backends` check.\"\n\n**Key Details:**\n\n* Metaclass: `DummyObject`\n* Backend checked for: `torch`\n* Initialization method (`__init__`) requires compatibility with `torch` backend.\n\n**Notes:**\n\n* Extensive dependencies not mentioned.\n* Parent class/interface not specified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:882-886
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 886, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModel", "start_line": 882, "type": "class", "chroma:document": "CODE:\n\nclass AutoModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `AutoModel`**\n\nThe main purpose of this class is to provide an interface for automatic model selection based on backend support.\n\n### Main Purpose and Functionality\n\nThis class, `AutoModel`, utilizes the `DummyObject` metaclass and defines its functionality behind requiring a specific backend implementation (`torch`) as a prerequisite.\n\n### Implementation Details\n\n* The `__init__` method enforces that the backend must be either \"torch\" via the `requires_backends` function call.\n* No additional methods are implemented within this class; it serves purely for the purpose of enforcing dependencies on its derived or child classes.\n\n### Notable Dependencies or Requirements\n\n* Backend implementations: Must support \"torch\" and derive from `AutoModel`\n* External dependency:\n  * `_DummyObject`: A metaclass used to define the `AutoModel`.\n\n### Code Snippet with Context\n```python\nclass AutoModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\nFor further context and details, please refer to the parent metaclass (`_DummyObject`) or additional requirements."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:889-893
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 893, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForAudioClassification", "start_line": 889, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForAudioClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `AutoModelForAudioClassification` class is an audio classification model implemented using the `torch` backend from PyTorch. It validates that the `torch` backend is required at initialization.\n\n**Key Details**\n\n* **Backend**: Requires `torch` as a dependency\n* **Implementation**: Initializes with `*args` and `**kwargs`, validating backend requirements\n* **Class**: Derived from `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:896-900
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 900, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForAudioFrameClassification", "start_line": 896, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForAudioFrameClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThis class `AutoModelForAudioFrameClassification` inherits from the `DummyObject` metaclass and specifies it as compatible with only the `torch` backend.\n\n* **Primary purpose:** This class is designed for audio frame classification tasks, leveraging models supported by the `torch` backend.\n* **Key implementation details:**\n\t+ The model requires at least the `torch` backend to function (`requires_backends` method).\n* **Notable dependencies or requirements:**\n\t+ `torch` backend compatibility\n\t+ Inheritance from `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:903-907
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 907, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForAudioXVector", "start_line": 903, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForAudioXVector(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForAudioXVector`: An audio-specific PyTorch-based auto-model class.\n\n**Primary Purpose:** This model leverages the `torch` backend, enabling audio-specific functionality.\n\n**Implementation Details:**\n\n* Verifies required backends in the `__init__` method.\n\t+ Requires `torch` as a supported backend for this model.\n\n**Notable Dependencies/Requirements:**\nNo external dependencies are specified. The `DummyObject` metaclass is used internally within the class definition."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:910-914
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 914, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForCausalLM", "start_line": 910, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForCausalLM` is a class that represents a causal language model using the `torch` backend. \n\n**Key Implementation Details:**\n\n- Requires `torch` as a dependency for instantiation.\n\n**Notable Dependencies and Requirements:**\n\n- `_backends = [\"torch\"]`\n- `requires_backends(self, [\"torch\"])`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:917-921
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 921, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForCTC", "start_line": 917, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForCTC(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AutoModelForCTC`\n\n**Purpose and Functionality**: A PyTorch-based auto-model class for CTC (Connected Time-Delayed) speech recognition.\n\n**Main Purpose**: Automatically determines the underlying backend for CTC models using `requires_backends`.\n\n**Key Implementation Details**:\n\n* Uses `torchtorch` as the default backend.\n* Verifies that `torch` is required by the `requires_backends` function.\n\n**Notable Dependencies and Requirements**:\n \n* `torch`: Required for the underlying CTC model implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:924-928
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 928, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForDepthEstimation", "start_line": 924, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForDepthEstimation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForDepthEstimation` class is an auto-suggested model for depth estimation tasks. It utilizes the `torch` backend.\n\n*   Purpose: Suggests and initializes an appropriate model based on deep learning frameworks like PyTorch (`torch`).\n*   Key implementation details:\n    *   Verifies that the provided framework supports the required backend (`requires_backends` function with [\"torch\"]).\n*   Notable dependencies:\n    +   `torch`: deep learning framework used.\n    +   `DummyObject` (metaclass): initializes class-based attributes.\n\n**Key Insights:**\n\n- The model is auto-suggested based on a specific backend, in this case, PyTorch (`torch`).\n- The existence of the specified backend ensures that the required functionalities are supported for depth estimation tasks."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:931-935
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 935, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForDocumentQuestionAnswering", "start_line": 931, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForDocumentQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForDocumentQuestionAnswering` is a PyTorch-based model class that inherits from `DummyObject`. It primarily serves as a template for document question answering tasks.\n\n**Key Takeaways:**\n\n* Primary purpose: Serves as a template for document question answering tasks using PyTorch.\n* Implementation details:\n\t+ Requires the \"torch\" backend to be supported by the model (specifically, requires_backends)\n* Dependencies and requirements:\n\t+ External dependency on PyTorch (`torch`)\n\n**Class Structure Overview:**\n```markdown\n|---------------|\n|  metaclass    |\n|  (= DummyObject) |\n|---------------|\n\n|---------------|\n|  backend      |\n|  (= \"torch\" |) |\n|---------------|\n\n|---------------|\n|  requires_backends|\n|  (= takes     |\n|   keyword-only |\n|   arguments)     |\n|---------------|\n```\nNote: The actual class structure might not be as explicitly represented, but the above summary provides a concise representation of its key characteristics."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:938-942
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 942, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForImageClassification", "start_line": 938, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForImageClassification` is an Image Classification model class that uses the `torch` backend. It inherits from a dummy metaclass and initializes with a requirement for the `torch` backend.\n\n### Key Details:\n\n* Primary purpose: Use of `torch` backend for image classification\n* Implementation details:\n\t+ Initialization requires `torch` backend (checked using `requires_backends` method)\n* Notable dependencies:\n\t+ `torch`: backend dependency for image classification"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:945-949
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 949, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForImageSegmentation", "start_line": 945, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForImageSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForImageSegmentation` is an autolabeled model class that automatically selects the `torch` backend. It initializes with no specific requirements for the `requires_backends` function.\n\n**Key Details:**\n\n*   **Metaclass:** `DummyObject`\n*   **Backend Requirements:** `self.requires_backends([\"torch\"])`\n*   **Inherited Interfaces:** No explicitly defined interfaces.\n*   **Implemented Backend Dependencies:** Only `torch` is listed in `_backends`.\n*   **Implementation Signature:** `__init__(*args, **kwargs)`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:952-956
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 956, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForImageTextToText", "start_line": 952, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForImageTextToText(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`AutoModelForImageTextToText` Class:\n\n*   Primary purpose: An auto-model for image-text-to-text translation tasks.\n*   Functionality: Utilizes the `torch` backend.\n\nKey Details:\n-   Uses `requires_backends` method to enforce use of \"torch\" as required backend.\n-   Implemented with metaclass `DummyObject`.\n\nInterfaces:\n-   Inherited from `BaseAutoModelForImageTextToText`.\n \n\nNotable Dependencies:\n-   `torch`: Required backend.\n  Additional information:\n\nThe class inherits from `BaseAutoModelForImageTextToText`, further details of which are not provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:959-963
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 963, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForImageToImage", "start_line": 959, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForImageToImage(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoModelForImageToImage Class Summary**\n\n### Main Purpose and Functionality\nThe `AutoModelForImageToImage` class is a PyTorch-based autoencoder model designed for image-to-image translation tasks. Its primary purpose is to recognize patterns in images and translate them into corresponding outputs.\n\n### Key Implementation Details\n\n* Initializes with two mandatory backend requirements: \"torch\" (using the `requires_backends` function).\n* Utilizes the `DummyObject` metaclass as its implementation framework.\n\n### Notable Dependencies or Requirements\nThis class requires:\n- PyTorch (`\"torch\"` backend)\n\nNo external dependencies are explicitly mentioned; however, it implicitly relies on the standard Python library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:966-970
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 970, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForInstanceSegmentation", "start_line": 966, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForInstanceSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AutoModelForInstanceSegmentation`\n\nA PyTorch-based instance segmentation model.\n\n**Key Features**\n\n* Supports `torch` backend\n\n**Implementation Details**\n\n* Requires the `torch` backend for initialization\n* Uses `requires_backends` function to validate backend support\n\n**Notable Dependencies and Requirements**\n\n* `torch`: Required backend for instantiation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:973-977
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 977, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForKeypointDetection", "start_line": 973, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForKeypointDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AutoModelForKeypointDetection`\n\nA PyTorch-based class for keypoint detection, utilizing the `torch` backend.\n\n**Key Implementation Details**\n\n* Initializes model with custom `requires_backends` method, enforcing usage of `torch` backend.\n* No explicit model definition or architecture details provided.\n\n**Notable Dependencies and Requirements**\n\n* External dependency: `torch`\n* Inherited/implemented interfaces: `DummyObject` (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:980-984
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 984, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForMaskedImageModeling", "start_line": 980, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForMaskedImageModeling(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForMaskedImageModeling`: A pre-trained model class using the \"torch\" backend.\n\n*   **Primary purpose:** Provides a pre-trained mask image modeling approach.\n*   **Key implementation details:**\n    *   Initializes with `requires_backends` method, requiring only `\"torch\"` backend.\n*   **Notable dependencies/requirements:** The model depends on the `\"torch\"` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:987-991
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 991, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForMaskedLM", "start_line": 987, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoModelForMaskedLM Class Summary**\n\n**Purpose:** A customizable masked language model implementation using the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes with `*args` and `**kwargs`\n* Requires the `torch` backend, ensuring compatibility.\n\n**Notable Dependencies/Requirements:**\n\n* `torch`: The only supported backend.\n* Inherited interface: Not specified; assumes compatibility with other backends.\n\nThis class is designed for customizing masked language models using the PyTorch library. Although it doesn't provide specific implementation details in this snippet, it sets up a foundation for model customization and compatibility with different backends."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:994-998
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 998, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForMaskGeneration", "start_line": 994, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForMaskGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForMaskGeneration` class uses the `DummyObject` metaclass and is designed to work with the \"torch\" backend. The constructor takes variable arguments and checks for the required \"torch\" backend using the `requires_backends` function, ensuring it's present before initialization.\n\n**Key Details:**\n\n- Purpose: Creates an instance of a model for mask generation.\n- Backend Requirements: Only supports the \"torch\" backend using a metaclass enforced backend requirement functionality.\n- Implementation:\n\n  Args `*args` and `**kwargs`\n  - Calls `requires_backends` with the key [\"torch\"] to verify compatibility\n\n**Notable Dependencies:**\n\n- None explicitly listed\n- Inherently relies on:\n  - `DummyObject` metaclass for backend enforcement"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1001-1005
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1005, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForMultipleChoice", "start_line": 1001, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForMultipleChoice` class is a PyTorch-based model implementation using the `DummyObject` metaclass. It initializes with the `\"torch\"` backend and requires it for runtime.\n\n**Key Implementation Details:**\n\n* Initializes with a list of required backends (`[\"torch\"]`)\n* Verifies the specified backend is available using the `requires_backends` function\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch library\n* `DummyObject` metaclass (from PyTorch)\n* `_requires_backends` function (not shown in this snippet)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1008-1012
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1012, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForNextSentencePrediction", "start_line": 1008, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`AutoModelForNextSentencePrediction`\n\nA pre-trained model for next sentence prediction tasks (e.g., sentiment analysis), utilizing the `torch` backend.\n\n**Key Details:**\n\n* Initializes with support for `torch` backend through the `requires_backends` method.\n* Designed to work with text classification or similar tasks where predicting the next sentence is relevant.\n\n**Notable Dependencies:**\n\n* `torch`: Required backend for model execution."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1015-1019
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1019, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForObjectDetection", "start_line": 1015, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n### `AutoModelForObjectDetection` class\n\n```markdown\n**Purpose:** \nAn auto-model object for object detection tasks.\n\n**Metadata:**\n- Metaclass used: `DummyObject`\n- Supported backend: `torch`\n\n**Initialization:**\n- Checks if at least one required backend (`torch`) is available.\n  \n    ```\nclass AutoModelForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n### Key Implementation Details:\n- Verifies that the `torch` backend is required for initialization.\n  \n### Notable Dependencies or Requirements:\n- `requires_backends` function (not shown in code snippet)\n   - Takes an instance and a list of required backends as arguments."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1022-1026
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1026, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForPreTraining", "start_line": 1022, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AutoModelForPreTraining`\n\n**Main Purpose and Functionality**\nThis class is a pre-trained model for Hugging Face's AutoML framework. It uses the `torch` backend (`self._backends = [\"torch\"]`) to provide pre-trained weights.\n\n**Key Implementation Details**\n\n* Requires the `torch` backend in its initialization (`requires_backends(self, [\"torch\"])`)\n* Inherently utilizes a dummy metaclass implementation (`metaclass=DummyObject`)\n\n**Notable Dependencies or Requirements**\n\n* External dependency: PyTorch ( referenced by `self._backends = [\"torch\"]`)\n* Related class/function: `dummy_object` class (specific implementation details not provided)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1029-1033
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1033, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForQuestionAnswering", "start_line": 1029, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`AutoModelForQuestionAnswering` class with metadata:\n\n### Main Purpose and Functionality\nA PyTorch-based model for question answering.\n\n### Key Implementation Details\n- Initializes `self` using `__init__` method with variable arguments (`*args`) and keyword arguments (`**kwargs`).\n- Requires the \"torch\" backend.\n\n### Notable Dependencies or Requirements\n- Requires \"torch\" as a dependency."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1036-1040
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1040, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForSemanticSegmentation", "start_line": 1036, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForSemanticSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForSemanticSegmentation` is a class that automatically selects the `torch` backend for semantic segmentation tasks. It initializes with a list of required backends, which are then validated using the `requires_backends` function.\n\n**Key Details:**\n\n* Primary purpose: Automatically select the `torch` backend for semantic segmentation tasks.\n* Notable method:\n\t+ `__init__`: Initializes the object with the required `torch` backend.\n* Design Choice:\n\t+ Uses a metaclass (`DummyObject`) to enforce the use of the `torch` backend.\n* Dependencies/Requirements:\n\t+ Required backends: torch (validated using `requires_backends`)\n\t+ Inherited/interface: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1043-1047
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1047, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForSeq2SeqLM", "start_line": 1043, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForSeq2SeqLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `AutoModelForSeq2SeqLM`**\n\n**Purpose and Functionality**\n================================\n\nThe `AutoModelForSeq2SeqLM` class is an auto-regressing encoder-decoder model designed for sequence-to-sequence language modeling tasks, utilizing the Torch backend.\n\n**Key Implementation Details**\n==========================\n\n*   Inherited from `metaclass=DummyObject`, indicating a custom metaclass.\n*   Requires PyTorch as a dependency (`self requiresh backends(self, [\"torch\"])`).\n\n**Notable Dependencies or Requirements**\n=====================================\n\n*   Torch (`torch`)\n*   Custom metaclass (`DummyObject`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1050-1054
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1054, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForSequenceClassification", "start_line": 1050, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`AutoModelForSequenceClassification`\n\n**Main Purpose and Functionality:**\nThis class is a pre-trained model for sequence classification tasks, utilizing the `torch` backend.\n\n**Key Implementation Details:**\n\n* Requires `torch` backend (instantiated by `requires_backends`).\n* `__init__` initializes the object with arbitrary arguments (`args`, `kwargs`).\n\n**Notable Dependencies or Requirements:**\n- `DummyObject`: Metaclass used to define the class.\n- `requires_backends`: Function to ensure required backends are installed.\n- `torch`: External dependency, backend used by the model.\n\n**Summary:** \n`AutoModelForSequenceClassification` is a pre-trained sequence classification model using `torch`. It relies on the `torch` backend and allows for flexible initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1057-1061
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1061, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForSpeechSeq2Seq", "start_line": 1057, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForSpeechSeq2Seq(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`AutoModelForSpeechSeq2Seq`\n==========================\n\nA PyTorch-based speech sequence-to-sequence (seq2seq) model autoencoder.\n\n**Primary Purpose**\nAutomatically selects the best available backend (\"torch\") for the model.\n\n### Key Implementation Details\n\n*   Uses `requires_backends` method to validate backend availability.\n*   Employs meta-class `DummyObject` as its class definition.\n\n### Notable Dependencies and Requirements\n\n*   PyTorch (`torch`) - Required backend for this autoencoder."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1064-1068
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1068, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForTableQuestionAnswering", "start_line": 1064, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForTableQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n### Class Overview:\n\n`AutoModelForTableQuestionAnswering`\n\n**Purpose and Functionality:**\n\nA PyTorch-based auto-model for table question answering tasks.\n\n**Key Implementation Details:**\n\n* `__init__`: Initializes the model using the `torch` backend.\n\t+ Requires `torch` as a required backend.\n* `_backends`: List of supported backends, specifically \"torch\".\n\n### Notable Dependencies/Requirements:\n\n* `dummy_object` (metaclass)\n* `torch` (required backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1071-1075
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1075, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForTextEncoding", "start_line": 1071, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForTextEncoding(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoModelForTextEncoding Class Summary**\n\n**Purpose and Functionality**\nThe `AutoModelForTextEncoding` class is an auto-scaffolded model wrapper designed for text encoding tasks using the `.torch` backend.\n\n**Key Implementation Details**\n\n* Requires the `.torch` backend at initialization.\n* Uses a metaclass (`DummyObject`) to enable auto-generation scaffolding code.\n\n**Notable Dependencies and Requirements**\n\n* `.torch`: Required backend for text encoding tasks."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1078-1082
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1082, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForTextToSpectrogram", "start_line": 1078, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForTextToSpectrogram(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`AutoModelForTextToSpectrogram` is an auto-similarization model for converting text to spectrograms. The model uses Torch backend.\n\n**Key Implementation Details**\n\n* Extends ` DummyObject` metaclass\n* Has a single backend supported: Torch\n* Initializes with a requirements check on the supported backend\n\n**Notable Dependencies and Requirements**\n\n* Torcha backend (referenced as \"torch\")"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1085-1089
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1089, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForTextToWaveform", "start_line": 1085, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForTextToWaveform(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoModelForTextToWaveform Class Summary**\n\n*   **Purpose**: An auto model class for text-to-wavergroup conversion using the `torch` backend.\n*   **Initialization**: Initializes the model with support for the `torch` backend, which requires specific dependencies to be installed (`requires_backends` function call).\n*   **Key Implementation Details**:\n    *   Uses a specific design pattern: `metaclass=DummyObject`\n    *   Has a primary method for initialization and backend configuration\n*   **Notable Dependencies or Requirements**:\n    *   Specific backend (`torch`)\n    *   A custom function `requires_backends` (not shown in the provided code snippet)\n        -   Function signature is not explicitly shown, but it implies dependency installation checks\n\nThis summary should be readable in a Resource Allocation Group (RAG) system."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1092-1096
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1096, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForTokenClassification", "start_line": 1092, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForTokenClassification`\n\n`AutoModelForTokenClassification` is an auto-detected token classification model utilizing the `torch` backend.\n\n**Key Features:**\n\n* `requires_backends(\"torch\")`: Verifies that the `torch` backend is available for deployment.\n* `metaclass=DummyObject`: Imposes a dummy metaclass (`DummyObject`) on the class for registration purposes.\n\n**Notable Dependencies:**\n\n* `torch`\n\n**Implementation Details:**\nThe model's architecture and implementation details are automatically determined using the specified backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1099-1103
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1103, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForUniversalSegmentation", "start_line": 1099, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForUniversalSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`AutoModelForUniversalSegmentation` is an auto-detector model for universal segmentation tasks, utilizing the `torch` backend. It initializes the model with an auto-detection mechanism that ensures compatibility with the specified backends.\n\n**Key Details:**\n\n* **Primary Purpose:** Auto-generate model configuration based on detected backends.\n* **Implementation:** Utilizes `requires_backends` function to validate supported backends (`torch`) during model initialization.\n* **Notable Dependencies:**\n\t+ `torch`: Backend library for universal segmentation tasks.\n\t+ `DummyObject`: Metaclass used for class definition.\n\nNote: Specific implementation details, such as the exact requirements and validation logic of the `requires_backends` function, are not included in this summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1106-1110
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1110, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForVideoClassification", "start_line": 1106, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForVideoClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForVideoClassification` class is a deep learning model designed for video classification tasks, leveraging the PyTorch backend. It initializes with various arguments using the `*args` and `**kwargs` syntax.\n\n**Key Implementation Details:**\n\n- Initializes with a specific required backend (`torch`)\n- Utilizes `requires_backends` function to validate backend compatibility\n\n**Notable Dependencies and Requirements:**\n\n- `DummyObject`: metaclass\n- `torch`: primary backend dependency"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1113-1117
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1117, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForVision2Seq", "start_line": 1113, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForVision2Seq(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForVision2Seq` class is a type of PyTorch model that inherits from `DummyObject`. It is designed for computer vision tasks using dual-sequenced input.\n\n**Main Purpose and Functionality:** Implements Vision Transformer (ViT) architecture for sequence-to-sequence tasks.\n\n**Key Implementation Details:**\n\n* Requires PyTorch backend (`torch`).\n* Uses a specific type of PyTorch model implementation (`DummyObject`).\n\n**Notable Dependencies or Requirements:**\n\n* torch\n* DummyObject (PyTorch metaclass)\n\nNote: The exact ViT architecture is not explicitly described in this snippet, but it is likely based on the Vision Transformer (ViT) algorithm."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1120-1124
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForVisualQuestionAnswering", "start_line": 1120, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForVisualQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`AutoModelForVisualQuestionAnswering` class:\n\n* Primary purpose: [Visual Question Answering] model for automatic generation of answers to visual questions.\n* Implementation:\n\t+ Requires `torch` backend (`requires_backends(self, [\"torch\"])`).\n\t+ Uses `DummyObject` metaclass.\n\n**Key details**\n\n* `_backends` attribute stores required backends: `[torch]`.\n* `__init__` method initializes the model with generic arguments using `requires_backends`.\n\n**Dependencies and requirements**\n\n* Related classes/functions: ` DummyObject`, `requires_backends`\n* External dependencies:\n\t+ [torch]"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1127-1131
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForZeroShotImageClassification", "start_line": 1127, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForZeroShotImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class AUTO_MODEL_FOR_ZERO_SHOT_IMAGE CLASSIFICATION**\n\nThis class is an auto-model for zero-shot image classification using the `\"torch\"` backend.\n\n### Key Implementation Details\n\n*   Requires `torchnet` backend\n*   Uses `provide` decorator to ensure required backends are available during initialization\n\n### Dependencies and Requirements\n\n*   `torchnet`\n\nNote: The `DummyObject` metaclass is used, but its purpose is unknown as it's not a standard Python metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1134-1138
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1138, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelForZeroShotObjectDetection", "start_line": 1134, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelForZeroShotObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoModelForZeroShotObjectDetection` class is a zero-shot object detection model that relies on the PyTorch backend. It initializes with a requirements check for the \"torch\" backend.\n\n**Key Details:**\n\n- Primary purpose: Implementing Zero-Shot Object Detection using AutoML\n- Key implementation detail: Relies on the PyTorch backend (`torch`)\n\n**Implementation Dependencies:**\n\n- External dependencies: `torch`\n- Required classes/interfaces: Not explicitly mentioned, but inherits from `DummyObject`\n\nNote: This summary is concise and focuses on the core details of the class, while omitting unnecessary information."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1141-1145
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1145, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoModelWithLMHead", "start_line": 1141, "type": "class", "chroma:document": "CODE:\n\nclass AutoModelWithLMHead(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`AutoModelWithLMHead` class:\n\n*   Main Purpose: A PyTorch-based auto model with pre-configured LM (Language Model) head.\n*   Key Implementations:\n    - Utilizes the `Provide` decorator, though its purpose is unclear without the full definition.\n*   Dependencies/Requirements:\n    - Requires `torch` backend; uses `DummyObject` metaclass.\n\n**Summary in RAG format**\n \n### Class Description\n#### [ AutoModelWithLMHead ]\n\nAn auto model with pre-configured LM head, utilizing PyTorch backend.\n\n\n### Key Components\n\n####  `__init__`\n\n-   Initializes instance, ensuring correct backend usage (`torch`).\n\n### Dependencies/Requirements\n\n-   `[torch]`: Required backend implementation.\n#### `[DummyObject]`: Metaclass used for class definition."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1148-1152
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1152, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoformerForPrediction", "start_line": 1148, "type": "class", "chroma:document": "CODE:\n\nclass AutoformerForPrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `AutoformerForPrediction` class is an implementation of the Autoformer model, leveraging the PyTorch backend. Its primary purpose is to perform prediction tasks using optimized autoformer models.\n\n**Key Implementation Details:**\n\n* The instance requires at least one of the following backends (`torch`) for execution.\n* Initializes the class using a variable number of positional and keyword arguments (`args` and `kwargs`). \n\n**Notable Dependencies:**\n\n* Relies on `Provides` function from an external library (`dummy_object_lib` or similar, exact function not defined).\n\nNote that without the full codebase or additional information about `DummyObject` and its relation to external libraries, this summary provides a concise representation of what is publicly available."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1155-1159
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1159, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoformerModel", "start_line": 1155, "type": "class", "chroma:document": "CODE:\n\nclass AutoformerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoformerModel Class Summary**\n\n### Purpose and Functionality\n\nThe `AutoformerModel` class is an auto-former implementation with model initialization that requires the \"torch\" backend. It is designed to work with the specified backend engine.\n\n### Key Implementation Details\n\n* The class uses the `DummyObject` metaclass.\n* The `__init__` method:\n\t+ Requires the `\"torch\"` backend using the `requires_backends` function.\n\n### Notable Dependencies and Requirements\n\n* Backend: `torch`\n* Metaclass: `DummyObject`\n\nNote that no external dependencies other than the specified backend are listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1162-1166
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1166, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AutoformerPreTrainedModel", "start_line": 1162, "type": "class", "chroma:document": "CODE:\n\nclass AutoformerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**AutoformerPreTrainedModel Class Summary**\n\n**Main Purpose and Functionality:**\nThe `AutoformerPreTrainedModel` class is a PyTorch-based autoformer model architecture, inheriting from the `DummyObject` metaclass.\n\n**Key Implementation Details:**\n\n* Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`)\n* Requires the PyTorch backend (`torch`)\n\n**Notable Dependencies or Requirements:**\n\n* ` DummyObject` metaclass (not a standard Python class)\n* PyTorch library (`torch`)\n \n\nNote: The `requires_backends` function is not shown in the snippet, but it is assumed to be part of the model's implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1169-1173
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1173, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BambaForCausalLM", "start_line": 1169, "type": "class", "chroma:document": "CODE:\n\nclass BambaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nClass `BambaForCausalLM` is an implementation of a model class using the Meta-Transformers (`DummyObject`) metaclass. It has been designed to work with the `torch` backend.\n\n**Key Insights:**\n\n* The `_backends` attribute specifies that this model class uses `torch` as the primary backend.\n* The `__init__` method initializes the instance and requires the `torch` backend to be present (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1176-1180
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1180, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BambaModel", "start_line": 1176, "type": "class", "chroma:document": "CODE:\n\nclass BambaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `BambaModel` class is an implementation of a machine learning model that utilizes the `torch` backend for computation. It requires the presence of the `torch` backend during initialization.\n\n```python\nclass BambaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        # Verifies if torch is available as a required dependency\n        requires_backends(self, [\"torch\"])\n```\n\n**Analysis:**\n\n*   The `BambaModel` class inherits from `DummyObject`, indicating that it may be part of a larger framework or abstraction layer.\n*   The `_backends` attribute specifies the primary backend used by this model implementation.\n*   The `requires_backends` function is called during initialization, ensuring that the necessary backend is available before proceeding.\n\n**Key Details:**\n\n*   Required Dependency: `torch`\n*   Backend Verification (`_requires_backends` function)\n*   Model Class Hierarchy (`BambaModel -> DummyObject`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1183-1187
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1187, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BambaPreTrainedModel", "start_line": 1183, "type": "class", "chroma:document": "CODE:\n\nclass BambaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n### Class Overview\n\n`BambaPreTrainedModel`: A class implementing a pre-trained model using the `torch` backend.\n\n### Main Purpose and Functionality\n\nThis class serves as a base model for pre-trained models, requiring support for the `torch` backend. It provides an initialization method that checks if the required backend is supported.\n\n### Key Implementation Details\n\n* Initialization (`__init__` method): Verifies that the `torch` backend is available using `requires_backends`.\n\t+ Method Signature: `__init__(self, *args, **kwargs)`\n\t+ Implemented Interface: `DummyObject` (metaclass)\n\n### Notable Dependencies or Requirements\n\n* Backend: `_backends = [\"torch\"]`\n* Required Backends Checking Function: `requires_backends(self, [\"torch\"])`\n\n### Overall Structure\n\nThis class provides a basic structure for pre-trained models, ensuring compatibility with the specified backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1190-1194
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1194, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkCausalModel", "start_line": 1190, "type": "class", "chroma:document": "CODE:\n\nclass BarkCausalModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\nBarkCausalModel\n================\n\n### Main Purpose and Functionality\n\n* Instantiates the BarkCausalModel class using the `__init__` method.\n\n### Key Implementation Details\n\n* Requires one of the following backends to be set: `\"torch\"` (using the `requires_backends` method with `[\"torch\"]`).\n\n### Notable Dependencies or Requirements\n\n* The model uses the `torch` backend.\n* The `DummyObject` metaclass is used for instantiation.\n\nNo additional dependencies are noted."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1197-1201
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1201, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkCoarseModel", "start_line": 1197, "type": "class", "chroma:document": "CODE:\n\nclass BarkCoarseModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThis `BarkCoarseModel` class is designed using a dummy object metaclass. It specifically requires the `\"torch\"` backend for initialization.\n\n**Key Details:**\n\n*   **Metaclass:** `DummyObject`\n*   **Backend:** `\"torch\"`\n*   **Method Signature:** `__init__(self, \\*args, \\*\\*kwargs)`\n\n**Notable Dependencies/Requirements:**\n\n*   - External Dependency: `torch` (specifically `\"torch\"` backend)\n\nThis summary provides a clean and concise overview of the class's functionality and requirements."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1204-1208
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1208, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkFineModel", "start_line": 1204, "type": "class", "chroma:document": "CODE:\n\nclass BarkFineModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BarkFineModel` class utilizes the ` DummyObject` metaclass and specifies `_backends` as `\"torch\"`. It has an `__init__` method that checks if the required backend `torch` is available using the `requires_backends` function.\n\n\n**Key Details:**\n\n- Uses `DummyObject` metaclass\n- Requires backend `\"torch\"`\n- Checks for available backend in `__init__` method\n\n**Dependencies/Requirements:**\n\n* `torch` (external dependency)\n* `_DummyObject` class (inherited or implemented)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1211-1215
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1215, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkModel", "start_line": 1211, "type": "class", "chroma:document": "CODE:\n\nclass BarkModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n*Class: `BarkModel`*\n \n*   Purpose: Initialize `BarkModel` instance using required backend: Torch (`torch`).\n                \n*   Implementation Details:* Instantiates and requires the usage of the Torch backend for the model."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1218-1222
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1222, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkPreTrainedModel", "start_line": 1218, "type": "class", "chroma:document": "CODE:\n\nclass BarkPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BarkPreTrainedModel` class is a pre-trained model base class that depends on the `torch` backend. It initializes with optional arguments and enforces the use of `torch` as the required backend.\n\n**Key Details:**\n\n* Backend dependency: `torch`\n* Primary purpose: Pre-trained model base class\n* Method signature: `__init__(*args, **kwargs)`\n\nNote: No notable implementation details are provided in this snippet. The class is likely to contain pre-trained model logic and algorithms which are not shown here."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1225-1229
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1229, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BarkSemanticModel", "start_line": 1225, "type": "class", "chroma:document": "CODE:\n\nclass BarkSemanticModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BarkSemanticModel Class Summary**\n\n```markdown\n# Purpose:\nThe BarkSemanticModel class is an instance of the DummyObject metaclass, \nspecifying the underlying backend as \"torch\".\n\n# Key Implementation:\n- The `__init__` method requires \"torch\" backend and performs validation.\n\n# Notable Dependencies/Requirements:\n- Requires the 'Provide' function from a dependency (not specified in code snippet)\n```\n\nNote: Without information on what the 'Provide' function is, I've left it as is. Depending on further context, this could be clarified or rephrased to better reflect its purpose."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1232-1236
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1236, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartForCausalLM", "start_line": 1232, "type": "class", "chroma:document": "CODE:\n\nclass BartForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BartForCausalLM` is a PyTorch-based class that implements a causal language model using the BART architecture. Its primary purpose is to process sequential data and generate predictions based on a given input sequence.\n\n**Key Implementation Details:**\n\n* Implemented with `torch` backend, requiring it for initialization.\n* `_backends` list specifies supported backend, in this case, only \"torch\".\n\n**Notable Dependencies and Requirements:**\n\n* Reuses `requires_backends` function from parent class.\n* Inherits no other classes or interfaces.\n* Does not have any external dependencies mentioned.\n\nNote: The provided code chunk is incomplete as it does not specify the implementation of the `DummyObject`, `requires_backends`, or the actual functionality of the `BartForCausalLM` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1239-1243
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1243, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartForConditionalGeneration", "start_line": 1239, "type": "class", "chroma:document": "CODE:\n\nclass BartForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BartForConditionalGeneration` class is a conditional generation model implemented using the PyTorch backend. Its purpose is to support conditional generation tasks.\n\n**Key Details:**\n\n* Primary purpose: Conditional generation task\n* Key implementation:\n\t+ Requires PyTorch as the required backend (`requires_backends(self, [\"torch\"])`)\n* Notable dependencies:\n\t+ Inherited `DummyObject` metaclass\n\t+ External dependency on PyTorch library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1246-1250
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1250, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartForQuestionAnswering", "start_line": 1246, "type": "class", "chroma:document": "CODE:\n\nclass BartForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BartForQuestionAnswering` class is a custom neural network model implementation of the BART (Buffered Translator at Scale) architecture for question answering tasks. It uses Torch as its backend.\n\n**Key Details:**\n\n* Primary purpose: Implementing a BART-based question answering model.\n* Implementation details:\n\t+ Utilizes the ` DummyObject` metaclass.\n\t+ Requires TensorFlow and CUDA acceleration (`requires_backends` function checks for availability of \"torch\" torch.torch.backends.cuda automatic mixed precision engine).\n* Dependencies:\n\t+ Torch (as its backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1253-1257
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1257, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartForSequenceClassification", "start_line": 1253, "type": "class", "chroma:document": "CODE:\n\nclass BartForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`BartForSequenceClassification` (metaclass: `DummyObject`)\n\n* Primary purpose: Sequence classification using the BART model\n* Implemented with Torch backend (`_backends = [\"torch\"]`)\n* Initializes with the \"torch\" backend requirement\n\n**Key Implementation Details**\n\n* Requires \"torch\" backend for initialization (`requires_backends(self, [\"torch\"])`)\n\n**Notable Dependencies/Requirements**\n\n* `DummyObject` metaclass\n* External dependency: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1260-1264
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1264, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartModel", "start_line": 1260, "type": "class", "chroma:document": "CODE:\n\nclass BartModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BartModel Class Summary**\n\n**Main Purpose and Functionality**\nThe `BartModel` class is an implementation of a transformer-based language model using the PyTorch backend.\n\n### Key Implementation Details\n\n* Initializes the model with any additional arguments (`args` and `kwargs`) that require the `\"torch\"` backend.\n* Requires the presence of the `\"torch\"` backend for instance initialization.\n\n### Notable Dependencies or Requirements\n\n* `requires_backends`: Function to enforce required backends; used with `\"torch\"`.\n* PyTorch backend (not explicitly imported, but implied as a requirement)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1267-1271
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1271, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartPreTrainedModel", "start_line": 1267, "type": "class", "chroma:document": "CODE:\n\nclass BartPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BartPreTrainedModel` is a class that inherits from `DummyObject`. It's a pre-trained model for the BART language model, requiring the `torch` backend.\n\n**Key Details:**\n\n* Primary purpose: Pre-trained BART model\n* Key implementation detail: Requires `torch` backend (implemented with `requires_backends(self, [\"torch\"])`)\n* Notable dependency:\n\t+ `torch`: Required backend for this model\n\n(Note: This summary is concise and focused on the essential details, excluding unnecessary information.)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1274-1278
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1278, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BartPretrainedModel", "start_line": 1274, "type": "class", "chroma:document": "CODE:\n\nclass BartPretrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Bart Pretrained Model Class Summary**\n\n### Main Purpose and Functionality\nThe `BartPretrainedModel` class is a meta-class that defines a pre-trained model architecture, utilizing the `torch` backend.\n\n### Key Implementation Details\n\n*   Initializes the model using `self` as an argument.\n*   Calls the `requires_backends` function to ensure the `torch` backend is supported.\n\n    ```python\n    # Method Signature: requires_backends(self, backends)\n```\n\n### Notable Dependencies or Requirements\n\n*   The `torch` library is required for this model.\n*   Inherited Interface: The \"dummy\" metaclass `DummyObject` ensures that the class is properly registered with its designated backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1281-1285
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1285, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "PretrainedBartModel", "start_line": 1281, "type": "class", "chroma:document": "CODE:\n\nclass PretrainedBartModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n### Pretrained BART Model Class\n\nThe `PretrainedBartModel` class represents a pre-trained BART (Bidirectional Transformer Attention from Transformers) model. Its purpose is to initialize a BART model instance, ensuring the required backend ('torch') is available.\n\n### Key Details:\n\n*   **Backend:** Utilizes the 'torch' backend.\n*   **Initialization:** Verifies that 'torch' is among the required backends during initialization.\n\n### Notable Dependencies/Requirements:\n\n*   `requires_backends`: A utility function to check for specific backends.\n*   `DummyObject` (metaclass): The class's metaclass, which doesn't add any specific functionality in this case.\n*   [Transformers library](https://huggingface.co/models ): This is the library used by BART architecture."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1288-1292
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1292, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitBackbone", "start_line": 1288, "type": "class", "chroma:document": "CODE:\n\nclass BeitBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BeitBackbone Class Summary**\n\n*   **Purpose:** The `BeitBackbone` class is an implementation of the backbone network for the Beit framework. It ensures that at least one backend (currently 'torch') is specified during initialization.\n*   **Key Details:**\n    *   Uses a dummy metaclass (`DummyObject`) to define its structure.\n    *   Initializes the object with `self` and accepts variable arguments (`args`) and keyword arguments (`kwargs`).\n*   **Implementation:** Requires one of 'torch' as a backend using the `requires_backends` function.\n\nNotable Dependencies:\n- Torchaotrain (backend class)\n- DummyObject (metaclass)\n\n External Dependencies: \nNone specified"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1295-1299
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1299, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitForImageClassification", "start_line": 1295, "type": "class", "chroma:document": "CODE:\n\nclass BeitForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BeitBaseClass` for Image Classification using Torch Backend.\n\n*   Main purpose and functionality: Provides an instance of the `BeitForImageClassification` class, which can be used for image classification tasks using the Torch backend.\n*   Key implementation details:\n    *   Requires Torch as a backend.\n    *   Initializes with required arguments using the `requires_backends` method.\n*   Notable dependencies or requirements:\n    *   Torch: A machine learning library commonly used in deep learning models.\n\n**Detailed Breakdown:**\n\n### Class Definition\n\n`class BeitForImageClassification(metaclass=DummyObject):`\n\nThis line defines a new class, `BeitForImageClassification`, which inherits from the `DummyObject` metaclass. The name `Beit` and its implementation are not shown in this code snippet.\n\n### Initialize Method\n\n`.def __init__(self, *args, **kwargs):`\n\nThe `__init__` method is a special method that is automatically called when an object of the class is created. It initializes the object with the provided arguments (`*args` and `**kwargs`) and performs additional setup or validation.\n\n### `requires_backends` Method Call\n\n`.requires_backends(self, [\"torch\"]);`\n\nThis line calls the `requires_backends` method on the current instance of `BeitForImageClassification`, passing a list of required backends (`[\"torch\"]`). The purpose of this method is not shown in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1302-1306
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1306, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitForMaskedImageModeling", "start_line": 1302, "type": "class", "chroma:document": "CODE:\n\nclass BeitForMaskedImageModeling(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: BeitForMaskedImageModeling**\n\n*Purpose:* \nThis class is designed for masked image modeling with the `torch` backend.\n\n*Key Implementation Details:*\n\n* Requires the `torch` backend to be installed (`backend_requires=[\"torch\"]`).\n* Initializes the object with no default arguments (`__init__()`) which calls `requires_backends()` to ensure the necessary backends are available.\n\n**Notable Dependencies/Requirements:**\n\n* Backend name: `torch`\n* Interfaces/Inherited Classes:\n    None\n\n**Method Signatures and Interfaces:**\n\n* No public methods defined."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1309-1313
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1313, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitForSemanticSegmentation", "start_line": 1309, "type": "class", "chroma:document": "CODE:\n\nclass BeitForSemanticSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BeitForSemanticSegmentation`**\n\n*   **Purpose:** This class provides a semantic segmentation model using the `Beit` architecture with the PyTorch backend.\n\n```python\nclass BeitForSemanticSegmentation(metaclass=DummyObject):\n    \"\"\"Asemantic segmentation model using the Beitarchitecture\"\"\"\n    \n    # Primary Backend\n    _backends = [\"torch\"]\n```\n\n**Implementation Details:**\n\n*   The model requires the `torch` backend for execution, indicating that this class utilizes PyTorch as its primary backend.\n*   \n        requires_backends(self, [\"torch\"]):\n\nThe `requires_backends` method checks if the required backends are available. \n\nKey Methods/Variables:\n- `__init__`: Initializes the object with optional arguments and backend requirements check.\n\nNotable Dependencies:\n\n-   `DummyObject`: A metaclass used for defining the class structure.\n-   `requires_backends`: A function checked during object instantiation to ensure specified Python backends are installed and available."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1316-1320
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1320, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitModel", "start_line": 1316, "type": "class", "chroma:document": "CODE:\n\nclass BeitModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`BeitModel` is a PyTorch-based class with a custom metaclass `DummyObject`. Its primary purpose is to manage a list of required backends (`[\"torch\"]`) for initialization.\n\n**Key Implementation Details**\n\n* Initializes the object using a variable number of positional and keyword arguments.\n* Calls the `requires_backends` function, passing in the class itself and a list of required backends (`[\"torch\"]`). This ensures that the specified backend is available at instantiation.\n\n**Notable Dependencies or Requirements**\n\n* Python\n* PyTorch (`torch`)\n* A custom metaclass (`DummyObject`) for class definition customization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1323-1327
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1327, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BeitPreTrainedModel", "start_line": 1323, "type": "class", "chroma:document": "CODE:\n\nclass BeitPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: BeitPreTrainedModel**\n\n### Purpose and Functionality\n\nThe `BeitPreTrainedModel` is a Python class that inherits from `DummyObject`, utilizing the PyTorch backend (`\"torch\"`). It defines an initializer method (`__init__`) to configure the model, ensuring compatibility with the specified backend.\n\n### Key Implementation Details\n\n*   Requires PyTorch as a dependency.\n*   Initializes the model using `self` as the argument reference.\n\n### Notable Dependencies or Requirements\n\n*   `DummyObject`: The base class used for this model.\n*   PyTorch: A machine learning library required for compatibility."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1330-1334
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1334, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForMaskedLM", "start_line": 1330, "type": "class", "chroma:document": "CODE:\n\nclass BertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BertForMaskedLM`**\n\nThe `BertForMaskedLM` class is an implementation of the BERT pre-trained language model as a masked language modeling (MLM) task. The primary purpose is to predict missing words in a sentence.\n\n### Main Purpose and Functionality\n\n* **Description:** This class builds upon the Hugging Face Transformers library, utilizing PyTorch as its backend.\n* **Primary Function:** Provides an interface for applying pre-trained BERT models to masked language modeling tasks.\n\n### Key Implementation Details\n\n* **Method Signature:** `__init__(*args, **kwargs)`: Initializes the model, requiring the presence of the `torch` backend (PyTorch 1.9+).\n\t+ Requirements: `_requires_backends(\"torch\")`\n* **Metaclass:** Utilizes `DummyObject` as its metaclass.\n\n### Notable Dependencies or Requirements\n\n* **Backend:** `torch` (required for PyTorch 1.9+)\n* **Interface Inheritance:** Implements the `TransformerModel` interface from Hugging Face's Transformers library.\n\t+ Referenced interfaces: `TransformerModel`\n* **External Dependency:** Built upon the Hugging Face Transformers library, referencing its pre-trained BERT models and MLM architecture."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1337-1341
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1341, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForMultipleChoice", "start_line": 1337, "type": "class", "chroma:document": "CODE:\n\nclass BertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nClass `BertForMultipleChoice` is a variant of the BERT model optimized for multiple-choice classification tasks. It utilizes the `torch` backend.\n\n**Key Details:**\n\n* Primary purpose: Multiple choice classification using BERT\n* Notable dependencies:\n\t+ `requires_backends(self, [\"torch\"])`: Ensures the use of the PyTorch backend.\n\t+ Inherited interfaces: None mentioned, but implicitly inherits from parents that support multiple-choice classification.\n\nNo external libraries are explicitly listed as dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1344-1348
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1348, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForNextSentencePrediction", "start_line": 1344, "type": "class", "chroma:document": "CODE:\n\nclass BertForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BertForNextSentencePrediction` is a PyTorch implementation of the BERT model for next sentence prediction tasks. It uses the `torch` backend.\n\n**Key Implementation Details:**\n\n- Initializes the model with PyTorch as the required backend.\n- No specific algorithm or pattern details are provided in this code snippet.\n\n**Notable Dependencies/Requirements:**\n\n- `torch`\n- Inherited interface/superclass: `DummyObject` (via metaclass)\n \n\nThis summary provides a concise overview of the key aspects of the class, highlighting its intended purpose and dependencies. The RAG system can use this information to facilitate understanding and navigation within the codebase."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1351-1355
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1355, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForPreTraining", "start_line": 1351, "type": "class", "chroma:document": "CODE:\n\nclass BertForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BertForPreTraining Class Summary**\n\n**Overview**\nBertForPreTraining is a transformer-based pre-training model implemented in the BERT (Bidirectional Encoder Representations from Transformers) architecture.\n\n**Key Functionality**\n\n* Initializes the BERTForPreTraining instance with support for the `torch` backend.\n* Verifies that the `torch` backend is required for this class.\n\n**Notable Implementation Details**\n\n* The `__init__` method takes variable arguments (`*args`) and keyword arguments (`**kwargs`).\n* The `requires_backends` function is used to validate the supported backends, confirming that only `torch` is allowed.\n\n**Dependencies and Requirements**\n\n* Related classes: `DummyObject`\n* External dependencies:\n\t+ `torch`: PyTorch library\n* Interfaces inherited/implemented: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1358-1362
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1362, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForQuestionAnswering", "start_line": 1358, "type": "class", "chroma:document": "CODE:\n\nclass BertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BertForQuestionAnswering` class is a custom model implementation for Question Answering tasks using the BERT algorithm.\n\n*   **Purpose:** The main purpose of this class is to provide a Q&A model leveraging the BERT algorithm, which is designed for natural language processing tasks.\n*   **Key Implementation Details:**\n    *   The `__init__` method initializes the object and checks if the required backend, torch, meets the prerequisites.\n*   **Notable Dependencies or Requirements:**\n    *   The class relies on the `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1365-1369
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1369, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForSequenceClassification", "start_line": 1365, "type": "class", "chroma:document": "CODE:\n\nclass BertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`BertForSequenceClassification`\n\n*A PyTorch-based sequence classification model built on the BERT framework.*\n\n**Primary Purpose:**\nImplementing a pre-trained language understanding model for downstream natural language processing tasks.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Initializes with arguments using `__init__`.\n\n**Notable Dependencies and Requirements:**\n\n* `requires_backends()` function with dependency on \"torch\".\n* Inherited from the `DummyObject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1372-1376
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1376, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertForTokenClassification", "start_line": 1372, "type": "class", "chroma:document": "CODE:\n\nclass BertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`BertForTokenClassification`\n\n**Primary Purpose:**\nA PyTorch-based implementation of the BERT model for token classification tasks.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend, which is enforced in the constructor.\n\n**Notable Dependencies and Requirements:**\n\n* `_backends`: List of required backends (`[\"torch\"]`).\n* Inherited from `DummyObject`.\n \n\nNote: The code references a dummy `DummyObject` metaclass, but its purpose and implementation are not detailed here to maintain conciseness."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1379-1383
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1383, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertLMHeadModel", "start_line": 1379, "type": "class", "chroma:document": "CODE:\n\nclass BertLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BertLMHeadModel` class is a PyTorch-based model that serves as the architecture for transforming BERT (Bidirectional Encoder Representations from Transformers) into a language modeling head. It requires only the \"torch\" backend.\n\n* **Purpose**: Language Modeling Head for BERT Architecture\n* **Key Implementation Details**:\n\t+ Uses PyTorch and relies on `torch` backend\n\t+ Utilizes `requires_backends` decorator to enforce specific backend requirements\n* **Notable Dependencies/Requirements**:\n\t+ No external dependencies listed, but relies on PyTorch with \"torch\" backend support."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1386-1390
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1390, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertModel", "start_line": 1386, "type": "class", "chroma:document": "CODE:\n\nclass BertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BertModel Class Summary**\n\n*   **Class Purpose:** The `BertModel` class is an implementation of the BERT model using the \"torch\" backend.\n\n*   **Implementation Details:**\n    *   Initializes self with arguments and ensures that the \"torch\" backend is required.\n        -   **Required Backend**: torch\n    *   No methods are defined in this snippet; it appears to be an abstract base class or a setup function, prompting further inspection of inherited classes.\n\n*   **Noteable Dependencies/Requirements:**\n    -   The BERT model itself.\n    -   The `torch` backend library\n    -   The `DummyObject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1393-1397
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1397, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertPreTrainedModel", "start_line": 1393, "type": "class", "chroma:document": "CODE:\n\nclass BertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`BertPreTrainedModel`: A base class for pre-trained BERT models using the `torch` backend.\n\n**Main Purpose and Functionality**\nThe primary purpose of this class is to serve as a foundation for pre-trained BERT models, providing compatibility with various backends, in this case, `torch`.\n\n**Implementation Details**\n\n* Uses the `DummyObject` metaclass.\n* Initializes with `args` and `kwargs`, ensuring that the `torch` backend is available (`requires_backends` checks for required backends).\n\n**Notable Dependencies/Requirements**\n- `Requires torch backend`\n- Inherited from `metaclass=DummyObject`\n\nNote: The exact text is preserved to maintain accuracy."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1400-1401
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1401, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_bert", "start_line": 1400, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_bert(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nFunction `load_tf_weights_in_bert` loads TensorFlow weights for BERT model, requiring PyTorch backend.\n\n**Key Details:**\n\n* Primary purpose: Load pre-trained BERT weights in PyTorch environment\n* Algorithm/Pattern: Uses `load_tf_weights_in_` function as a dependency for loading weights with specific backend (torch)\n* Key Method Signature/Interface:\n\t+ `load_tf_weights_in_bert`\n\t+ Requires PyTorch backend (`requires_backends`) \n\n**Notable Dependencies and Requirements:**\n\n* External Dependency: torch\n* Related Class: load_tf_weights_in_"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1404-1408
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1408, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertGenerationDecoder", "start_line": 1404, "type": "class", "chroma:document": "CODE:\n\nclass BertGenerationDecoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n```markdown\n**BertGenerationDecoder Class**\n\nMain purpose: Decoder for BERT-based generation tasks.\n\n* Initializes the decoder with the `torch` backend, requiring its support.\n\n  **Key Features and Implementation Details:**\n  +  Implements the `metaclass=DummyObject` to enforce required backends.\n  +  Checks for the presence of the `torch` backend using `requires_backends`.\n```\n\n**Notable Dependencies/Requirements:**\n\n*   `torch`\n*   `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1411-1415
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1415, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertGenerationEncoder", "start_line": 1411, "type": "class", "chroma:document": "CODE:\n\nclass BertGenerationEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: BertGenerationEncoder**\n\nThe `BertGenerationEncoder` class is an implementation of a BERT-based encoder, specializing in generation tasks. Its primary purpose is to provide a front-end interface for interacting with the chosen backend (`torch`). \n\n**Key Implementation Details:**\n\n* Uses the `torch` backbone\n* Requires backend compatibility at instantiation\n\n**Notable Dependencies:**\n\n* `torch`: primary backend dependency"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1418-1422
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1422, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BertGenerationPreTrainedModel", "start_line": 1418, "type": "class", "chroma:document": "CODE:\n\nclass BertGenerationPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThe `BertGenerationPreTrainedModel` class is a PyTorch-based pre-trained model for BERT generation tasks. It inherits from the `DummyObject` metaclass.\n\n**Key Details:**\n\n*   Primary Purpose: Pre-training for BERT generation tasks\n*   Implementation: Uses the Torch backend (`self._backends = [\"torch\"]`)\n*   Key Function Signature:\n    *   `requires_backends(self, [\"torch\"])`: Verifies that the torch backend is available\n\n**Dependencies and Requirements:**\n\n*   External Dependencies:\n    +   Torch library\n*   Inherited/Implemented Interfaces: None found in this code snippet"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1425-1426
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1426, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_bert_generation", "start_line": 1425, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_bert_generation(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`load_tf_weights_in_bert_generation`: Loads TF weights in BERT generation mode. \n\n**Implementation Details:**\n\n*   Requires backend support from `load_tf_weights_in_bert_generation` with TF backend.\n*   Supports loading of TF weights.\n\n**Notable Dependencies/Requirements:**\n\n*   Torch backend (`torch`)\n\n**Analysis:** The function is designed to load pre-trained TensorFlow (TF) weights specifically for BERT generation. It leverages the `load_tf_weights_in_bert_generation` base function, which implies a custom implementation of weight loading mechanisms tailored for BERT."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1429-1433
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1433, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForCausalLM", "start_line": 1429, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BigBirdForCausalLM` model class:\n\n*   Purpose: Utilizes the `torch` backend for causal language modeling.\n\n*   Key Implementation Details:\n    *   Requires `torch` backend initialization.\n    *   Initializes with `args` and `kwargs`.\n\n*   Notable Dependencies/Requirements:\n    *   `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1436-1440
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1440, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForMaskedLM", "start_line": 1436, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BigBirdForMaskedLM` class is a PyTorch-based neural network model for language modeling tasks, built on top of the BigBird architecture. The primary purpose is to utilize masked language modeling for pre-training and fine-tuning.\n\n**Key Implementation Details:**\n\n* Utilizes the \"torch\" backend.\n* Requires specific hardware and software dependencies (not explicitly listed).\n\n**Note:** The `DummyObject` metaclass is used, but its implementation is not provided in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1443-1447
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1447, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForMultipleChoice", "start_line": 1443, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n### Background Object for Multiple Choice Tasks (`BigBirdForMultipleChoice`)\n\nA PyTorch backend wrapper for the Big Bird model, designed for multiple choice tasks.\n\n#### Key Functionality:\n\n- Initializes with support for `torch` backend using the `requires_backends` function.\n- Enforces backend compatibility via meta-class application.\n\n#### Notable Details:\n\n- Uses a dummy object as its metaclass (`DummyObject`).\n- Implements requirement for `torch` backend using `requires_backends`.\n\n#### Dependencies:\n\n- Relies on PyTorch as the primary backend (`backend: \"torch\"`).\n\n**Example Use Case:**\n```python\n# Initialize BigBirdForMultipleChoice with required torch backend\ntask = BigBirdForMultipleChoice()\n```\nNote that exact implementation details regarding the `BigBird` model and input data processing are not provided in this summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1450-1454
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1454, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForPreTraining", "start_line": 1450, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`BigBirdForPreTraining` is a class utilizing the `torch` backend. It inherits a `DummyObject` metaclass and requires the `torch` backend for initialization.\n\n\n**Key Implementation Details**\n\n*   Initializes with `*args` and `**kwargs`.\n*   Requires the `torch` backend using `requires_backends`.\n\n**Notable Dependencies/Requirements**\n\n*   Utilizes the `torch` backend.\n*   Inherit from `DummyObject` metaclass.\n\nNote: The provided code snippet lacks information about how the class is used, its primary purpose, or any important methods. To create a more comprehensive summary, additional context would be necessary. However, based on the partial information available, this concise summary provides an overview of the class's functionality and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1457-1461
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1461, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForQuestionAnswering", "start_line": 1457, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BigBirdForQuestionAnswering` class is a custom implementation of the BERT model for question answering tasks, utilizing the PyTorch backend. The constructor (`__init__`) initializes the instance and enforces the use of the \"torch\" backend through the `requires_backends` function.\n\n**Key Details:**\n\n* Primary purpose: Question Answering task with BERT-based architecture.\n* Crucial method signature: None explicitly mentioned, but implied by the use of `requires_backends`.\n* Notable dependencies:\n\t+ PyTorch: specified as a required backend.\n\t+ `requires_backends` function (not shown in the code snippet): used to enforce dependency requirements.\n\n**Inherited/Implemented Interfaces:**\n\nNone explicitly stated in the provided code snippet. However, BERT models typically inherit from pre-trained architectures and may implement specific interfaces for fine-tuning or adapting to new tasks."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1464-1468
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1468, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForSequenceClassification", "start_line": 1464, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `BigBirdForSequenceClassification` class uses the PyTorch backend and requires it to be installed. The class is likely used for sequence classification tasks using the BigBird model.\n\n**Key Implementation Details**\n\n* Uses PyTorch backend (`torch`)\n* Requires PyTorch to be installed for proper functionality\n\n**Dependences/Requirements**\n\n* `requires_backends` function\n* External dependency: PyTorch library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1471-1475
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1475, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdForTokenClassification", "start_line": 1471, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\nBigBirdForTokenClassification:\n  Purpose: Hugging Face model for token classification tasks using the BigBird tokenizer and PyTorch backend.\n  \n  Key Requirements:\n  - Backend: Torch\n  \n  Initialization:\n  - Requires torch backend to be installed (`requires_backends(\"torch\")`)\n```\n\n**Code Breakdown:**\n\n*   Set `metaclass` to ensure proper inheritance.\n*   Initialize class _backends with \"torch\".\n*   Define the constructor method ( \\_init \\_) for initialization.\n*   Apply the requires\\_backends decorator function with torch backend.\n\nNote: The above explanation only includes a high-level overview of what the code does."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1478-1482
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1482, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdModel", "start_line": 1478, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BigBirdModel Class Summary**\n\n**Purpose and Functionality:**\nThe `BigBirdModel` class is a meta-class that inherits from `DummyObject`. It defines a single backend implementation using the PyTorch library.\n\n**Key Implementation Details:**\n\n* Initialize with optional arguments (`*args`, `**kwargs`)\n* Requires Torcha backend implementation ([`requires_backends()` function])\n\t+ Expects specific backend (\"torch\") to be registered\n\n**Notable Dependencies and Requirements:**\n\n* [PyTorch](https://pytorch.org/) backend library\n* The `_backends` attribute is set to [\"torch\"] to configure the required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1485-1489
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1489, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPreTrainedModel", "start_line": 1485, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BigBirdPreTrainedModel` class is a BERT-based model implementation with the Torch backend. It initializes with optional arguments and enforces the use of the \"torch\" backend.\n\n**Key Details:**\n\n* Metaclass: `DummyObject`\n* Backend support: [\"torch\"]\n* Required backends enforce during initialization\n* Model type: BERT-based\n\n**Notable Implementation:**\n```python\ndef __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n```\nThis method validates the backend support at model initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1492-1493
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1493, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_big_bird", "start_line": 1492, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_big_bird(*args, **kwargs):\n\n\nSUMMARY:\n**Console Summary**\n\n**Function Name:** `load_tf_weights_in_big_bird`\n\n**Main Purpose and Functionality:**\nDefines a function to load TensorFlow weights using the BigBird backend with support for PyTorch.\n\n**Key Implementation Details:**\n\n*   **Algorithm/Pattern:** Not specified, implying it uses an existing loading mechanism.\n*   **Important Method Signatures and Interfaces:**\n    *   `requires_backends`: Takes in the function itself and a list of required backends, specifically \"torch\", indicating that PyTorch support is necessary.\n\n**Notable Dependencies or Requirements:**\n\n*   External Dependency:\n    *   PyTorch\n*   Inherited/Implemented Interfaces:\n    *   None mentioned"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1496-1500
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1500, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusForCausalLM", "start_line": 1496, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: BigBirdPegasusForCausalLM**\n\n**Purpose and Functionality:**\nThe `BigBirdPegasusForCausalLM` class is a specialized transformer model (`metaclass=DummyObject`) designed for causal language modeling tasks. It leverages the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes with optional arguments (`args`, `kwargs`)\n* Requires the `torch` backend for execution, which is ensured by the `requires_backends` decorator\n* Implies the use of a transformer architecture, possibly variant of the BigBird-Transformer or Pegasus-Transformer\n\n**Notable Dependencies and Requirements:**\n\n* `torch`: a deep learning framework required for model execution\n* `DummyObject` metaclass (specific implementation details not provided)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1503-1507
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1507, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusForConditionalGeneration", "start_line": 1503, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BigBirdPegasusForConditionalGeneration` class is a conditional generation model using the `BigBird` and `Pegasus-T5` architectures. It is designed to be used with the PyTorch backend.\n\n**Key Details:**\n\n* Model architecture: BigBird + Pegasus-T5\n* Backend: PyTorch (`torch`)\n* Requires:\n\t+ Torchaotrain (implied by `requires_backends` method)\n\nThis class appears to be part of a larger library for building and training large language models, likely used for text generation tasks."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1510-1514
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1514, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusForQuestionAnswering", "start_line": 1510, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BigBirdPegasusForQuestionAnswering`**\n\n### Main Purpose and Functionality\nCustom transformer-based class for question answering tasks, utilizing the `torch` backend.\n\n### Key Implementation Details\n- Initializes with optional arguments (`*args`, `**kwargs`)\n- Requires `torch` as the recommended backend\n\n### Notable Dependencies or Requirements\n- `dummy_object` (metaclass)\n- `torch` (backend requirement)\n\n### Code Snippet:\n```python\nclass BigBirdPegasusForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1517-1521
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1521, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusForSequenceClassification", "start_line": 1517, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BigBirdPegasusForSequenceClassification` is a PyTorch-based model class for sequence classification tasks. The primary purpose of this class is to handle `torch` backends.\n\n**Key Implementation Details:**\n\n* Initializes with support for the specified `torch backend`.\n* Verifies that the required `torch backend` meets the prerequisites.\n\n**Notable Dependencies and Requirements:**\n\n* Uses the `torch` library as its underlying backend.\n* Requires ` torch` as a prerequisite backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1524-1528
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1528, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusModel", "start_line": 1524, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `BigBirdPegasusModel` class is a PyTorch-based machine learning model. Its primary purpose is to initialize with the required backend (`torch`) using the `requires_backends` function.\n\n**Key Implementation Details:**\n\n* Initializes the model instance\n* Verifies that the `torch` backend is required\n\n**Notable Dependencies and Requirements:**\n\n* `torch`: The backend library required for this model.\n* `DummyObject`: The metaclass used to define the `BigBirdPegasusModel` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1531-1535
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1535, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BigBirdPegasusPreTrainedModel", "start_line": 1531, "type": "class", "chroma:document": "CODE:\n\nclass BigBirdPegasusPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BigBirdPegasusPreTrainedModel` class:\n \n*   **Main Purpose and Functionality:** BERT/BPEGAS model implementation for Torch backend.\n*   **Key Implementation Details:**\n    *   Supports training with `torch` backend.\n    *   Initializes with variable arguments (`*args`, `**kwargs`) which implicitly require `torch` backend support in this method.\n*   **Notable Dependencies or Requirements:**\n    *   Relsies a dependency on the `torch` library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1538-1542
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1542, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BioGptForCausalLM", "start_line": 1538, "type": "class", "chroma:document": "CODE:\n\nclass BioGptForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BioGptForCausalLM Class Summary**\n\n* **Purpose:** A PyTorch-based causal language model for bioinformatics applications.\n* **Functionality:**\n\t+ Supports multiple backends using the `requires_backends` decorator.\n\t+ Initializes with support for the \"torch\" backend.\n* **Key Implementation:**\n\t+ Uses a metaclass (`DummyObject`) to define default attributes.\n\t+ Defines an `__init__` method to set up the backend requirements.\n* **Dependencies:**\n\t+ Requires PyTorch as a backend ( Torch is listed in `_backends`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1545-1549
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1549, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BioGptForSequenceClassification", "start_line": 1545, "type": "class", "chroma:document": "CODE:\n\nclass BioGptForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BioGptForSequenceClassification` class is a custom neural network model meta-class that inherits from the `DummyObject` metaclass. It requires the torch backend for execution.\n\n**Key Implementation Details:**\n\n* The `__init__` method initializes the class and checks if the required torch backend is available.\n* The `requires_backends` function is used to validate the backend requirements (in this case, \"torch\").\n\n**Notable Dependencies/Requirements:**\n\n* Torch backend (`\"torch\"`)\n* `DummyObject` metaclass\n* `requires_backends` function"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1552-1556
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1556, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BioGptForTokenClassification", "start_line": 1552, "type": "class", "chroma:document": "CODE:\n\nclass BioGptForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `BioGptForTokenClassification` class is a PyTorch-based sequence model. Its primary purpose is to perform token classification tasks.\n\n**Key Implementation Details**\n\n*   Initializes with the `\"torch\"` backend using the `requires_backends` function.\n*   Uses a dummy metaclass (`DummyObject`) to enable class definition.\n\n**Notable Dependencies and Requirements**\n\n*   Requires the `\"torch\"` backend for execution."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1559-1563
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1563, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BioGptModel", "start_line": 1559, "type": "class", "chroma:document": "CODE:\n\nclass BioGptModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\nBioGptModel class:\n  - A class implementing a BioGPT model, supported by the \"torch\" backend.\n  - Requires \"torch\" as its primary supported backend.\n\nKey details:\n- Backends: [\"torch\"]\n- Methods/Attributes:\n  - requires_backends(arg): Verifies if a model is compatible with specified backends.\n```\n\nNote: The exact method signature `requires_backends(self, [\"torch\"])` does not have any additional information in the provided code snippet. Depending on the actual implementation, it might be useful to include more context (e.g., its purpose or interface), but based on this partial class definition, further details cannot be determined.\n\nAdditional analysis: The exact method signature `requires_backends(self, [\"torch\"])` is likely a custom verification function that checks for compatibility with specified backends. However, without further information about the `DummyObject`, `BioGptModel' purpose, or usage, it's challenging to provide more context on this specific implementation detail."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1566-1570
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1570, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BioGptPreTrainedModel", "start_line": 1566, "type": "class", "chroma:document": "CODE:\n\nclass BioGptPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BioGptPreTrainedModel`:**\n\nA PyTorch-based pre-trained model for BioGPT applications.\n\n**Key Details:**\n\n* Initializes the model with support for only \"torch\" backend.\n* Enforces backend compatibility using `requires_backends` method.\n\n**Implementation:**\n\n* Uses a custom metaclass (`DummyObject`) to define the class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1573-1577
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1577, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BitBackbone", "start_line": 1573, "type": "class", "chroma:document": "CODE:\n\nclass BitBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise and focused summary of the given code chunk:\n\n**Class Summary: `BitBackbone`**\n\n**Purpose:** A class representing a data processing pipeline with backend support for PyTorch.\n\n**Key Features:**\n- Enforces backend requirements using the `requires_backends` method.\n- Suggests `torch` as its supported backend.\n \n**Implementation Details:**\n\n- The `_backends` class attribute stores the supported backends, which includes `\"torch\"`.\n\n**Dependencies/Requirements:**\n- Specifically requires PyTorch (`torch`) as a backend. \n\nNo additional dependencies are mentioned in this snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1580-1584
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1584, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BitForImageClassification", "start_line": 1580, "type": "class", "chroma:document": "CODE:\n\nclass BitForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BitForImageClassification` class is an image classification model utilizing the `torch` backend. Its primary purpose is to provide a framework for training on bit representations.\n\n**Key Implementation Details:**\n\n*   Requires `torch` backend and verifies its presence using the `requires_backends` function.\n*   Uses a metaclass (`DummyObject`) to define class-level variables.\n\n**Notable Dependencies/Requirements:**\n\n*   Imports `torch` as a required backend for this class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1587-1591
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1591, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BitModel", "start_line": 1587, "type": "class", "chroma:document": "CODE:\n\nclass BitModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`BitModel` class employing `torch` backend.\n \n* Primary purpose:  Defines a model using the PyTorch backend.\n* Key implementation:\n\t+ Initializes the model with `torch` backend requirements.\n* Notable dependencies:\n\t+ Uses PyTorch (`torch`) as its backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1594-1598
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1598, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BitPreTrainedModel", "start_line": 1594, "type": "class", "chroma:document": "CODE:\n\nclass BitPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BitPreTrainedModel`: A PyTorch-based class that uses the `torch` backend.\n\n**Key Details:**\n\n* `_backends`: Set to `[\"torch\"]`, indicating the primary supported backend.\n* `__init__`: Initializes the model with optional arguments (`args` and `kwargs`). Requires the `torch` backend to be available (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1601-1605
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1605, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotForCausalLM", "start_line": 1601, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BlenderbotForCausalLM` class inherits from a custom metaclass (`DummyObject`) and is specifically designed for use with the Torch backend. The constructor (`__init__`) requires the `torch` backend, ensuring compatibility before initialization.\n\n**Key Implementation Details:**\n\n* `requires_backends(self, [\"torch\"])`: Verifies that the required `torch` backend is present.\n* Backend configuration (_backend = [\"torch\"]): Specifies compatibility with PyTorch.\n\n**Notable Dependencies/Requirements:**\n\n* `DummyObject`: Custom metaclass used for this class definition.\n* `torch`: Required PyTorch backend for compatibility."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1608-1612
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1612, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotForConditionalGeneration", "start_line": 1608, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`BlenderbotForConditionalGeneration`\n\n**Purpose and Functionality:**\nA conditional generation model implemented using PyTorch (`torch` backend).\n\n**Key Implementation Details:**\n\n*   Initializes the model with support for `torch` backend.\n*   Checks if the required `torch` backend is available.\n\n**Notable Dependencies and Requirements:**\n\n*   Utilizes `torch` as a backend (library requirement).\n            "}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1615-1619
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1619, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotModel", "start_line": 1615, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise and focused summary of the code chunk:\n\n**Class:** `BlenderbotModel`\n**Purpose:** A generic model class for Blenderbot, which requires PyTorch as a backend.\n\n**Key Details:**\n\n- **Initialization**: The `__init__` method initializes the object with optional arguments.\n- **Backend Requirement**: The `-requires_backends()` method checks if PyTorch is installed, enforcing its use (`\"torch\"`).\n\n**Notable Dependencies:**\n- `dummy_object` module ( inferred as `DummyObject` metaclass)\n- `torch` library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1622-1626
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1626, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotPreTrainedModel", "start_line": 1622, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Blenderbot Pre-Trained Model**\n\n*   **Purpose:** An implementation of the Blenderbot pre-trained model for natural language processing tasks.\n*   **Primary Functionality:** This class enables the creation of instances that can be used to interact with or provide output from the pre-trained model.\n*   **Implementation Details:**\n    *   Utilizes the `torch` backend, as specified by `_backends = [\"torch\"]`.\n    *   The constructor (`__init__`) requires `requires_backends(self, ...)` validation, which checks if the necessary backend (in this case, `torch`) is present.\n\n**Notable Dependencies and Requirements:**\n*   Utilizes `torch` as the required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1629-1633
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1633, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotSmallForCausalLM", "start_line": 1629, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotSmallForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n*-Class Name:* `BlenderbotSmallForCausalLM`\n*-Purpose:* A variation of the Blenderbot model for causal language modeling tasks.\n*Primary Functionality:* Fine-tuned version of Blenderbot model using PyTorch backend.\n\n**Key Implementation Details:**\n\n* Requires PyTorch backend (`torch`) for initialization.\n\n**Notable Dependencies and Requirements:**\n\n* `DummyObject` metaclass\n* External Dependency:\n\t+ PyTorch (backend) \n\nNote: No specific methods or interfaces mentioned, only the primary purpose and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1636-1640
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1640, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotSmallForConditionalGeneration", "start_line": 1636, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotSmallForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BlenderbotSmallForConditionalGeneration` is a class that utilizes the `torch` backend. Its primary purpose is to enable conditional generation.\n\n*   **Key Functionality:** `requires_backends` method checks for availability of the required backend (`torch`) before initialization.\n*   **Notable Implementation Details:** Uses `DummyObject` metaclass, which is not further discussed in this snippet.\n*   **External Dependencies:**\n    *   `torch`\n    *   `DummyObject` (metaclass)\n\nNote that the actual functionality and implementation details beyond this summary would require additional context."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1643-1647
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1647, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotSmallModel", "start_line": 1643, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotSmallModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a `BlenderbotSmallModel` class that inherits from `DummyObject`. It uses the `torch` backend.\n\n### Key Points\n\n* **Purpose:** The main purpose of this class is to provide a small model for Blenderbot conversations using the `torch` backend.\n* **Implementation:**\n\t+ Uses the `requies.backends` function to ensure the `torch` backend is available at runtime.\n\t+ Does not provide any internal implementation details.\n* **Dependencies:**\n\t+ The `torch` library is required.\n\n### Interface and Methods\n\nNone explicitly defined in this code snippet. Assumes usage of `requies.backends`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1650-1654
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1654, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlenderbotSmallPreTrainedModel", "start_line": 1650, "type": "class", "chroma:document": "CODE:\n\nclass BlenderbotSmallPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BlenderBot Small Pre-Trained Model**\n=====================================\n\n**Main Purpose and Functionality**\n\nThe `BlenderbotSmallPreTrainedModel` is a pre-trained language model based on Blenderbot, utilizing the PyTorch backend.\n\n**Key Implementation Details**\n\n* Initializes with `torch` backend using `requires_backends`.\n\n**Notable Dependencies and Requirements**\n\n* `DummyObject`: metaclass used for initialization.\n* `requires_backends`: function to specify required backends.\n* External dependency: PyTorch library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1657-1661
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1661, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipForConditionalGeneration", "start_line": 1657, "type": "class", "chroma:document": "CODE:\n\nclass BlipForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\nThe `BlipForConditionalGeneration` class is a PyTorch backend for conditional generation tasks. It inherits from the `DummyObject` metaclass and initializes with support for Torch as its primary backend.\n\n**Key Implementation Details:**\n\n- Initializes with mandatory support for Torch (torch)\n- Requires specific backends upon instantiation\n\n**Notable Dependencies/Requirements:**\n\n- Torch library or runtime\n- Python 3.x environment"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1664-1668
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1668, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipForImageTextRetrieval", "start_line": 1664, "type": "class", "chroma:document": "CODE:\n\nclass BlipForImageTextRetrieval(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n* `BlipForImageTextRetrieval`: A class designed for image-text retrieval using the \"Blip\" model, leveraging the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initialization requires the `torch` backend to be available (`requires_backends(self, [\"torch\"])`)\n* The class uses a metaclass of `DummyObject`\n\n**Notable Dependencies/Requirements:**\n\n* External dependency: `torch`\n* Inherited/interface: `DummyObject` (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1671-1675
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1675, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipForQuestionAnswering", "start_line": 1671, "type": "class", "chroma:document": "CODE:\n\nclass BlipForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BlipForQuestionAnswering`**\n\n* **Purpose:** A question-answering model utilizing the Torchaudio backend.\n\n### Key Implementation Details\n\n* Requires Tensorflow (or TensorFlow Lightning) backends with at least \"torch\" support.\n\n(Note: There is no implementation details provided in the given code chunk. The `DummyObject` metaclass is not a standard Python concept and suggests customization or testing purposes.)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1678-1682
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1682, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipModel", "start_line": 1678, "type": "class", "chroma:document": "CODE:\n\nclass BlipModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BlipModel Class Summary**\n\n### Purpose and Functionality\n\nThe `BlipModel` class is designed to work with the \"torch\" backend. It initializes models by checking if the required backend is specified.\n\n```python\nclass BlipModel(metaclass=DummyObject):\n    \"\"\"\n    Model class for Blip.\n    \n    Works with torch backend.\n    \"\"\"\n\n    _backends = [\"torch\"]\n```\n\n### Key Implementation Details\n\n* Initializes models using `requires_backends` method, which checks if \"torch\" is one of the specified backends.\n* Raises an error if the required backend is not provided.\n\n```python\ndef __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n```\n\n### Dependencies and Requirements\n\nUses the torch backend.\n\n```python\n_backends = [\"torch\"]\n```\n\nNote: The `DummyObject` metaclass is not a built-in Python class. It's likely a custom implementation that enables some form of model registration or dependency management for Blip models."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1685-1689
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1689, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipPreTrainedModel", "start_line": 1685, "type": "class", "chroma:document": "CODE:\n\nclass BlipPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThis is an abstract base class `BlipPreTrainedModel` for pre-trained models using the `torch` backend. It defines the model's metadata and initializes it with compatibility checks.\n\n**Key Details:**\n\n* `metaclass=DummyObject`: Uses a dummy metaclass to register the `_backends` list.\n* `requires_backends(self, [\"torch\"])`: Initializes the model with torch backend support.\n\n**Notable Dependencies/Requirements:**\n\n* `torch`\n \n\n**Primary Purpose:** Defines an abstract base class for pre-trained models compatible with the `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1692-1696
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1696, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipTextModel", "start_line": 1692, "type": "class", "chroma:document": "CODE:\n\nclass BlipTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThe `BlipTextModel` class represents a text model using the PyTorch backend. The main functionality is initialization with required dependencies.\n\n*   Purpose: Text Model using PyTorch\n*   Primary purpose: Initialize model with required backend (`torch`)\n*   Notable details:\n    *   Initialization checks for supported backends (`requires_backends` method)\n        *   `self._backends = [\"torch\"]`\n*   Dependencies:\n    *   `torch`: Required backend\n\nThis summary aims to provide a clear and concise overview of the class, focusing on its primary functionality, algorithmics (in this case, backend checks), and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1699-1703
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1703, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BlipVisionModel", "start_line": 1699, "type": "class", "chroma:document": "CODE:\n\nclass BlipVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BlipVisionModel` class is a vision model implementation utilizing the PyTorch backend. It initializes with support for the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   **Backend Selection**: The `_backends` attribute and the `requires_backends` function enforce that only the \"torch\" backend is used.\n*   Initializer (`__init__` method) accepts positional (`args`) and keyword arguments (`kwargs`) but always requires the presence of a compatible \"torch\" backend.\n\n**Notable Dependencies and Requirements:**\n\n*   **Related Class**: `DummyObject`\n*   **External Dependency**: PyTorch (backend)\n*   No inherited or implemented interfaces noted."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1706-1710
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1710, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2ForConditionalGeneration", "start_line": 1706, "type": "class", "chroma:document": "CODE:\n\nclass Blip2ForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Blip2ForConditionalGeneration` class:\n\n*   A machine learning model implementation (main purpose).\n*   Currently supports only PyTorchbackend (`_backends = [\"torch\"]`).\n*   Initializes with required backend (`requires_backends(self, [\"torch\"])`).\n\n **Key Implementation Details:**\n\u2022 `__init__` method: Verifies that the \"torch\" backend is installed and initializes the model accordingly.\n\n **Notable Dependencies/Requirements:**\n\u2022 PyTorch library (implicitly required due to backend support)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1713-1717
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1717, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2ForImageTextRetrieval", "start_line": 1713, "type": "class", "chroma:document": "CODE:\n\nclass Blip2ForImageTextRetrieval(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Blip2 Class Summary**\n\n### Purpose and Functionality\nThe `Blip2` class is an image-text retrieval framework designed for Blip2. It utilizes the `torch` backend, making it suitable for deep learning-based tasks.\n\n### Main Implementation Details\n\n* Initializes with `args` and `kwargs`, requiring `torch` backend.\n* Calls `requires_backends` to enforce specified backends usage on instance creation.\n\n### Dependencies\n\n* Uses `torch` as its required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1720-1724
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1724, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2Model", "start_line": 1720, "type": "class", "chroma:document": "CODE:\n\nclass Blip2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Blip2Model`: A PyTorch-based machine learning model class. Provides an initialization method that enforces the use of the `torch` backend.\n\n**Key Functionality/Further Details:**\n\n- Initializes the model with support for PyTorch (`torch`) backend.\n- `requires_backends` function ensures only the specified backends are available during instance creation.\n  - Specifically, it checks if the required backend is `\"torch\"`.\n\n**Notable Dependencies/Requirements:**\n\n- The class requires PyTorch as its backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1727-1731
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1731, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2PreTrainedModel", "start_line": 1727, "type": "class", "chroma:document": "CODE:\n\nclass Blip2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `Blip2PreTrainedModel` class inherits from `DummyObject` with `\"torch\"` as its supported backend. It initializes the model using the `__init__` method, which:\n\n* Requires the \"torch\" backend using the `requires_backends` function.\n* Allows for variable arguments and keyword arguments (`*args` and `**kwargs`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1734-1738
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1738, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2QFormerModel", "start_line": 1734, "type": "class", "chroma:document": "CODE:\n\nclass Blip2QFormerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Blip2QFormerModel**\n\n*   **Main Purpose and Functionality:** Blip2QFormerModel is a class that inherits from DummyObject and implements the `__init__` method to initialize its dependencies. It uses the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   The `__init__` method requires the \"torch\" backend using the `requires_backends` function.\n*   It supports variable number of arguments (`*args`) and keyword arguments (`**kwargs`).\n\n**Notable Dependencies or Requirements:**\n\n*   Requires \"torch\" as a dependency."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1741-1745
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1745, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2TextModelWithProjection", "start_line": 1741, "type": "class", "chroma:document": "CODE:\n\nclass Blip2TextModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Blip2TextModelWithProjection`**\n\n*   **Purpose**: A text model class utilizing the \"torch\" backend for projection functionality.\n*   **Implementation Details**:\n    *   Requires the \"torch\" backend at initialization, implying a dependency on PyTorch for optimization.\n*   **Dependencies**:\n    *   `torch`: The primary backend used in this model."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1748-1752
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1752, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2VisionModel", "start_line": 1748, "type": "class", "chroma:document": "CODE:\n\nclass Blip2VisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n```\nBlip2VisionModel (\n  Primary Purpose: A class defining a machine learning model built on top of the Torch backend.\n\n Key Implementation:\n - Initializes with torch as the required backend using `requires_backends` method.\n \n Dependencies:\n - torch\n)\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1755-1759
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1759, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Blip2VisionModelWithProjection", "start_line": 1755, "type": "class", "chroma:document": "CODE:\n\nclass Blip2VisionModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `Blip2VisionModelWithProjection` class is a PyTorch-based model that utilizes the `torch` backend. The constructor initializes the model infrastructure by verifying if the required backend (`torch`) is available.\n\n**Key Details**\n\n* **Backend**: PyTorch\n* **Implemented Interface**: None (besides `DummyObject`)\n* **Dependencies**:\n\t+ `torch`\n\nNote: The `requires_backends` function is not shown in this snippet, but it seems to be a utility function that verifies if the required backends are available."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1762-1766
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1766, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomForCausalLM", "start_line": 1762, "type": "class", "chroma:document": "CODE:\n\nclass BloomForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BloomForCausalLM`**\n\nThe `BloomForCausalLM` class is an implementation of the Bloom algorithm for causal language models, utilizing PyTorch (`torch`) as its primary backend. The primary purpose is to provide efficient and probabilistic access to large multilayer networks.\n\n**Key Features:**\n\n* Utilizes PyTorch (`torch`) as its primary backend\n* Employs the Bloom algorithm for causal language models\n\n** notable dependencies/requirements:**\n \n- `PyTorch (torch)`\n \n\nNo additional required libraries or modules are listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1769-1773
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1773, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomForQuestionAnswering", "start_line": 1769, "type": "class", "chroma:document": "CODE:\n\nclass BloomForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BloomForQuestionAnswering` class is a custom implementation of a Bloom index for question-answering applications using the PyTorch backend.\n\n**Key Features and Implementation Details:**\n\n*   Uses a `DummyObject` metaclass, indicating it's a custom class.\n*   Requiring the \"torch\" backend for initialization, which implies that other backends are not supported.\n*   Requires validation of input arguments in the constructor to ensure compatibility with the intended use case.\n\n**Notable Dependencies and Requirements:**\n\n*   PyTorch (specifically, `torch` as a required backend).\n*   Custom metaclass (`DummyObject`).\n\nThis concise summary highlights the primary functionality, key implementation details, and dependent requirements of the provided class chunk."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1776-1780
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1780, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomForSequenceClassification", "start_line": 1776, "type": "class", "chroma:document": "CODE:\n\nclass BloomForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `BloomForSequenceClassification` class is a custom implementation of a Bloom filter for sequence classification tasks. It uses the `torch` backend.\n\n**Key Details:**\n\n*   Functionality: Implements a Bloom filter for sequence classification tasks.\n*   Backend: Utilizes the `torch` library.\n\n**Implementation Highlights:**\n\n*   The `_backends = [\"torch\"]` attribute specifies that this implementation relies on the `torch` backend.\n\n**Notes:**\n\n*   The class itself does not implement any specific algorithm; it seems to be a base class that requires a backend (in this case, `torch`) to be set up."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1783-1787
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1787, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomForTokenClassification", "start_line": 1783, "type": "class", "chroma:document": "CODE:\n\nclass BloomForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `BloomForTokenClassification` class is a meta-class-based implementation of Bloom filter for token classification tasks. It requires the `torch` backend.\n\n**Key Details**\n\n* Requires `torch` as the backend for this class (`_backends = [\"torch\"]`)\n* Initializes with any additional arguments (`__init__(self, *args, **kwargs)`)\n\n**No notable dependencies or requirements except for the 'torch' backend.**"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1790-1794
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1794, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomModel", "start_line": 1790, "type": "class", "chroma:document": "CODE:\n\nclass BloomModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BloomModel Class Summary**\n\n### Purpose and Functionality\nThe `BloomModel` class is an implementation of Bloom algorithms using the PyTorch backend. It checks if the required backend (torch) is present.\n\n### Key Implementation Details\n- Initializes with `self` and supports both positional and keyword arguments (`*args` and `**kwargs`)\n- Calls the `requires_backends` function, which is not shown in this code snippet\n\n### Notable Dependencies or Requirements\n- The class requires PyTorch as its backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1797-1801
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1801, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BloomPreTrainedModel", "start_line": 1797, "type": "class", "chroma:document": "CODE:\n\nclass BloomPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n* `BloomPreTrainedModel`: A PyTorch-specific Bloom model with pre-trained capabilities.\n* **Primary Purpose**: Provides a PyTorch implementation of a Bloom model using pre-trained weights.\n\n**Key Implementation Details**\n\n* Requires the `torch` backend for execution (`requires_backends` method).\n* Initializes with arbitrary arguments and keyword arguments (`__init__` method).\n\n**Notable Dependencies or Requirements**\n\n* Python library: PyTorch\n* Backend dependency: torch (required)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1804-1808
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1808, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BridgeTowerForContrastiveLearning", "start_line": 1804, "type": "class", "chroma:document": "CODE:\n\nclass BridgeTowerForContrastiveLearning(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BridgeTowerForContrastiveLearning` class is a PyTorch backend for contrasting learning tasks. It initializes with the `torch` backend and ensures compatibility.\n\n**Key Details:**\n\n- Initializes with `*args`, `**kwargs`\n-_requires the 'tor_ch' backend\n\n**Context:** This class appears to be part of a larger framework for contrastive learning tasks, specifically designed to work with PyTorch."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1811-1815
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1815, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BridgeTowerForImageAndTextRetrieval", "start_line": 1811, "type": "class", "chroma:document": "CODE:\n\nclass BridgeTowerForImageAndTextRetrieval(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n### BridgeTowerForImageAndTextRetrieval Class Overview\n\nThe `BridgeTowerForImageAndTextRetrieval` class is designed to facilitate image and text retrieval. Its primary function is to select the torch backend for the provided architecture.\n\n#### Key Implementation Details\n\n*   Initializes with a variable number of arguments (`*args`, `**kwargs`) using `__init__` method.\n*   Verifies that the required torch backend is available in the `_backends` list using the `requires_backends` function.\n*   If the required backend is not present, an exception should be raised but it does not throw any error instead provides a message\n\n#### Notable Dependencies and Requirements\n\n*   Uses the `torch` backend\n*   Inhereted from `DummyObject`\n*   Requires external dependency:\n    *   `Provides` is used to provide or raise error"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1818-1822
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1822, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BridgeTowerForMaskedLM", "start_line": 1818, "type": "class", "chroma:document": "CODE:\n\nclass BridgeTowerForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`BridgeTowerForMaskedLM`: \nClass that implements the Tower component for Masked Language Modeling. It sets up `torch` as the required backend and requires it in the constructor.\n\n* Primary purpose: Provides the Tower component for Masked LM\n* Notable implementation details:\n\t+ Requires `torch` backend\n\t+ Implemented with `DummyObject` metaclass\n\t+ Uses `requires_backends` function to check and set required backends\n* Dependencies:\n\t+ Related class: `dummy_object` (via `DummyObject` metaclass)\n\t+ External dependency: `torch`\n* Interfaces and methods:\n\t+ None explicitly referenced in this snippet"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1825-1829
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1829, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BridgeTowerModel", "start_line": 1825, "type": "class", "chroma:document": "CODE:\n\nclass BridgeTowerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BridgeTowerModel` class is a metadata model that specifies the backend requirements for the framework. It defines itself as compatible with the `\"torch\"` backend and ensures that it has been initialized with this requirement.\n\n*   **Purpose:** Define backend compatibility for the `BridgeTowerModel`\n*   **Key implementation details:**\n    *   Requires the `\"torch\"` backend to be activated.\n*   **Notable dependencies and requirements:**\n    +   `DummyObject` as metaclass\n    +   `requires_backends` function (not shown in this snippet)\n    +   `\"torch\" backend`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1832-1836
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1836, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BridgeTowerPreTrainedModel", "start_line": 1832, "type": "class", "chroma:document": "CODE:\n\nclass BridgeTowerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`BridgeTowerPreTrainedModel`: A pre-trained model class that leverages the `torch` backend.\n\n**Key Features:**\n\n- Utilizes the `torch` backend for computation (`_backends = [\"torch\"]`)\n- Initializes with variable arguments and keyword arguments using `__init__`\n- Requires the `torch` backend to be present at initialization time\n```python\nclass BridgeTowerPreTrainedModel:\n    \"\"\"Pre-trained model leveraging torch backend\"\"\"\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1839-1843
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1843, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosForTokenClassification", "start_line": 1839, "type": "class", "chroma:document": "CODE:\n\nclass BrosForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BrosForTokenClassification`**\n\n*   **Purpose:** A class for token classification tasks, leveraging backends for processing.\n*   **Functionality:** Utilizes the `torch` backend.\n\n**Implementation Details:**\n\n*   Requires the `torch` backend using the `requires_backends` method.\n*   Implemented with the `DummyObject` metaclass.\n\n**Notable Dependencies/Requirements:**\n\n*   Uses the `torch` backend as a dependency.\n*   Inherited from the empty `DummyObject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1846-1850
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1850, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosModel", "start_line": 1846, "type": "class", "chroma:document": "CODE:\n\nclass BrosModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**BrosModel Class Summary**\n\n**Purpose and Functionality:**\nThe `BrosModel` class is a PyTorch backend implementation of a data processing model, requiring the `torch` backend to be installed.\n\n**Key Implementation Details:**\n\n* Initializes with optional arguments (`*args` and `**kwargs`)\n* Verifies that the required `torch` backend is available before proceeding\n\n**Notable Dependencies/Requirements:**\n\n* `DummyObject` metaclass (implementation not shown)\n* External dependency on the `torch` backend library\n* Implementation inherits from a generic \"requires_backends\" function, but its implementation is not shown in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1853-1857
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1857, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosPreTrainedModel", "start_line": 1853, "type": "class", "chroma:document": "CODE:\n\nclass BrosPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `BrosPreTrainedModel` class is a wrapper for pre-trained models using the `\"torch\"` backend. It initializes with the specified arguments and checks if the required backend is present (`\"torch\"`).\n\n**Key Implementation Details:**\n\n*   `__init__`: Initializes the model, requiring the `\"torch\"` backend.\n*   `requires_backends`: Verifies that the `\"torch\"` backend is available.\n\n**Notable Dependencies or Requirements:**\n\n*   External dependency: `DummyObject` (metaclass)\n*   Required backend: `\"torch\"`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1860-1864
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1864, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosProcessor", "start_line": 1860, "type": "class", "chroma:document": "CODE:\n\nclass BrosProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `BrosProcessor`**\n\n*   Purpose: An image processing class utilizing the Torch backend.\n\n**Key Functionality:**\n\nThe `__init__` method initializes the processor with required dependencies. Notably:\n\n*   **Requires backends**: Verifies that the required \"torch\" backend is available before proceeding.\n*   **Class variable**: `_backends` stores a list of supported backends, currently limited to [\"torch\"].\n\n**Implementation Details**\n\n*   No specific algorithms or patterns are highlighted in this snippet, as it appears to be a simple initialization method. \n\n**Notable Dependencies/Requirements:**\n\n*   `requires_backends(self, backend)`\n    ```python\nclass requires_backends:\n    @staticmethod\n    def __call__(self, backend):\n        pass  # implementation not shown in the given code chunk\n```\n\nTo make it work, you need to implement or find an existing way to verify that a specified backend is available on your system. \n\n**External Dependencies:**\n\n*   `DummyObject` (metaclass)\n*   The `requires_backends` class or equivalent functionality.\n\nNote: As some implementation details, such as the `DummyObject` metaclass and the `requires_backends` class, are not provided in this code chunk, they cannot be further analyzed. \n\nThis summary is based on the few lines of code that were provided in the question snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1867-1871
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1871, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosSpadeEEForTokenClassification", "start_line": 1867, "type": "class", "chroma:document": "CODE:\n\nclass BrosSpadeEEForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`BrosSpadeEEForTokenClassification`\n\n**Main Purpose and Functionality:**\nA PyTorch-based token classification model utilizing the BrosSpadeEE algorithm.\n\n**Key Implementation Details:**\n\n* `_backends = [\"torch\"]`: Uses the PyTorch backend.\n* `__init__(self, *args, **kwargs)`:\n\t+ Requires the \"torch\" backend (`requires_backends(self, [\"torch\"])`).\n\t+ Initializes the model with optional arguments (`*args` and `**kwargs`).\n\n**Notable Dependencies or Requirements:**\n\n* Related classes/functions are expected to be part of the PyTorch library.\n* No external dependencies mentioned.\n\nNote: The exact details of the BrosSpadeEE algorithm are not provided, but it is assumed to be a custom implementation integrated into this model."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1874-1878
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1878, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "BrosSpadeELForTokenClassification", "start_line": 1874, "type": "class", "chroma:document": "CODE:\n\nclass BrosSpadeELForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n### Class Summary:\n\n`BrosSpadeELForTokenClassification`: A deep learning model for token classification tasks.\n\n### MainPurposeAndFunctionality:\nThis class represents a PyTorch (`\"torch\"`) implemention of the BrosSpadeEL transformer-based architecture for token classification, inheriting from `DummyObject` with `metaclass`.\n\n### KeyImplementationDetails:\n\n- Initializes the model using `__init__`, enforcing PyTorch backend usage via the `requires_backends` function.\n  - Method Signature: `def __init__(self, *args, **kwargs):`\n  \n- Uses the `BrosSpadeEL` transformer architecture.\n\n### NotableDependenciesAndRequirements:\n- Requires PyTorch (`\"torch\"`).\n\n### Inherited Interfaces:\n- Implements `DummyObject` with `metaclass`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1881-1885
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1885, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForCausalLM", "start_line": 1881, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CamembertForCausalLM` is a custom transformer model class (`metaclass=DummyObject`) that leverages the `torch` backend. Its primary purpose is to implement a causal language model (LLM) variant using Camembert architecture.\n\n**Key Implementation Details:**\n\n* Uses the `requires_backends` function to ensure support for only the `\"torch\"` backend.\n* Enforces a specific backend requirement upon initialization.\n\n**Notable Dependencies and Requirements:**\n\n* Expects the `torch` library as its primary backend (`_backends = [\"torch\"]`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1888-1892
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1892, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForMaskedLM", "start_line": 1888, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `CamembertForMaskedLM` class represents a model for masked language modeling. It is initialized with various arguments and relies on the \"torch\" backend.\n\n**Key Details**\n\n* Initializes with `args` and `kwargs`\n* Requires the \"torch\" backend\n* Model is stored in `_backends`\n\n**Notable Dependencies/Requirements**\n\n* `requires_backends` function ( referenced class/function )\n* External dependency: Torch library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1895-1899
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1899, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForMultipleChoice", "start_line": 1895, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**CamembertForMultipleChoice Class Summary**\n\n*Purpose:* A Camembert model variant optimized for multiple-choice problems.\n \n*Functionality:* Initializes the model instance with required backends.\n\n*Key Implementation Details:*\n \n - Utilizes `torch` backend (only).\n\n *Required Backends:*\n\n  - [torch](related class/functions)\n\n *Class Interface:*\n\n  - inherits from `DummyObject`\n  \n *Notable Requirements:*\n\n None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1902-1906
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1906, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForQuestionAnswering", "start_line": 1902, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n```python\nThis `CamembertForQuestionAnswering` class is a model for question answering tasks. It utilizes the Camembert backend and inherits from `DummyObject`.\n\n**Key Implementation Details:**\n\n* Initializes with `*args` and `**kwargs`\n* Requires the use of \"torch\" as a backend\n\n**Notable Dependencies/Requirements:**\n\n* `DummyObject` (metaclass)\n* `torch` (backend library)\n```\n\nLet me know if you'd like me to expand or modify anything in this summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1909-1913
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1913, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForSequenceClassification", "start_line": 1909, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`CamembertForSequenceClassification` class:\n\n*   A PyTorch neural network model for sequence classification tasks.\n*   Utilizes the Torch backend.\n*   Requires the `torch` backend for initialization.\n\n**Detailed Breakdown:**\n\n### Class Purpose\n\nThe primary purpose of this class is to create a model for sequence classification using the Camembert model architecture, compatible with the PyTorch backend.\n\n### Key Implementation Details\n\n*   The model uses the Torch backend.\n*   The `__init__` method initializes the model instance and requires the `torch` backend.\n\n### Notable Dependencies or Requirements\n\n*   External dependency: `torch` (specifically, the Torch backend)\n*   Inherited Requirement: `requires_backends(self, [\"torch\"])` mechanism ensures compatibility with the chosen backends."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1916-1920
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1920, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertForTokenClassification", "start_line": 1916, "type": "class", "chroma:document": "CODE:\n\nclass CamembertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: CamembertForTokenClassification**\n\n*   **Main Purpose and Functionality:** A token classification model implemented using PyTorch, specifically the Camembert model.\n*   **Key Implementation Details:**\n    *   Uses the `torch` backend.\n    *   Requires the `requires_backends` function to specify supported backends for the model initialization.\n*   **Notable Dependencies and Requirements:**\n    *   Inherited from `DummyObject`.\n    *   Uses `PyTorch (torch)` as a dependency. \n\nNote: The class seems to be using a metaclass (`DummyObject`) which is not provided in this snippet, but I have mentioned the related implementation detail assuming its existence for the purpose of providing accurate context."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1923-1927
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1927, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertModel", "start_line": 1923, "type": "class", "chroma:document": "CODE:\n\nclass CamembertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `CamembertModel` class is a custom model implementation using the PyTorch backend. It initializes the model with optional arguments and ensures that at least `torch` is specified as a required backend.\n\n**Key Details:**\n\n* **Purpose:** Implement custom PyTorch model.\n* **Implementation:** Initializes model with `__init__()`, requiring `torch` backend (requires_backends() method).\n* **Notable Dependencies:**\n\t+ PyTorch.\n\nNote: The mention of `DummyObject` as the metaclass is not part of the class implementation itself, but is relevant to the class's structure. It is implied that `DummyObject` is a custom metaclass used for this class definition."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1930-1934
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1934, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CamembertPreTrainedModel", "start_line": 1930, "type": "class", "chroma:document": "CODE:\n\nclass CamembertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n### Class Overview\n`CamembertPreTrainedModel`: A pre-trained model for the Camembert algorithm.\n\n### Primary Purpose and Functionality\nThe class inherits from `DummyObject` with two key characteristics:\n- It requires the `torch` backend.\n- It provides a basic structure for a machine learning model implementation.\n\n### Implementation Details\n\n*   **`__init__` Method:**\n    -   Requires `torch` as a backend.\n    -   Accepts variable arguments and keyword arguments (`*args` and `**kwargs`).\n\n### Notable Dependencies and Requirements\n-   Related Classes/Functions:\n    *   ` DummyObject`: The metaclass used by the class.\n    *   `requires_backends`: A function that verifies backend requirements. (Not shown in this snippet)\n-   External Dependencies:\n    *   `torch`: The required backend for model execution.\n\n**RAG System Representation**\n\n| **Category** | **Description** |\n| --- | --- |\n| **Purpose** | Pre-trained Camembert model implementation. |\n| **Functionality** | Inheriting from `DummyObject`. Requires `torch` backend. |\n| **Implementation Details** | `__init__` method with variable and keyword arguments. |\n| **Dependencies** | ` DummyObject`, `torch`. |\n\nNote: The above summary provides a clear, concise overview of the class and its key characteristics."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1937-1941
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1941, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CanineForMultipleChoice", "start_line": 1937, "type": "class", "chroma:document": "CODE:\n\nclass CanineForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CanineForMultipleChoice` class is a machine learning model that utilizes the `torch` backend. The primary purpose of the class is to enable multiple-choice classification for canine animals.\n\n**Key Implementation Details:**\n\n- Verifies backend requirements using the `requires_backends()` method.\n- Initializes with one or more backend options (in this case, `\"torch\"`).\n\n**Notable Dependencies/Requirements:**\n\n- Relies on the `torch` backend.\n  - Related classes/functions: \n    * `DummyObject` (metaclass)\n* External dependencies:\n  - `torch`\n* Interfaces:\n  - None explicitly implemented."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1944-1948
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1948, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CanineForQuestionAnswering", "start_line": 1944, "type": "class", "chroma:document": "CODE:\n\nclass CanineForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\nThe `CanineForQuestionAnswering` class serves as a frontend for the Canine model, a question-answering system powered by deep learning. \n\n### Purpose and Functionality\nPrimary purpose: Leverage the Canine model's capabilities in question- answering systems.\n\n### Implementation Details\n\n*   Utilizes PyTorch as its backend.\n*   Initializes with the `torch` backend to ensure compatibility.\n\n### Notable Dependencies and Requirements\n\n*   Related classes/functions:\n    *   `CanineModel`: The underlying model responsible for providing answers.\n    *   `DummyObject`: A metaclass used to create instances of this class, serving as a dummy object if not instantiated.\n        - Note: No relevant information can be extracted regarding this external class within the provided code snippet and its usage."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1951-1955
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1955, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CanineForSequenceClassification", "start_line": 1951, "type": "class", "chroma:document": "CODE:\n\nclass CanineForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: CanineForSequenceClassification**\n\nThe `CanineForSequenceClassification` class is a PyTorch-based model designed for sequence classification tasks. It inherits from the `DummyObject` metaclass and requires the `torch` backend.\n\n**Key Implementation Details:**\n\n* Primary purpose: Sequence classification using a canine-inspired architecture.\n* Notable method signature:\n\t+ `__init__(self, *args, **kwargs)`: Initializes the model with the required `torch` backend.\n* Important interfaces inherited from `DummyObject`:\n\t+ `requires_backends(self, backend_names)`\n* External dependencies:\n\t+ PyTorch (`torch`)\n\nNote: Additional implementation details and methods are not provided in this code snippet, so further analysis would require additional context or documentation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1958-1962
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1962, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CanineForTokenClassification", "start_line": 1958, "type": "class", "chroma:document": "CODE:\n\nclass CanineForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: CanineForTokenClassification**\n\nThis class, `CanineForTokenClassification`, is a custom token classification model using the torch backend.\n\n**Key Implementation Details:**\n\n- Utilizes a specific instance method (`requires_backends`) to specify required backends (`torch`). \n\nNo notable dependencies or interfaces are inherited or implemented. The class has a single constructor that accepts variable arguments and keyword arguments, which then calls `requires_backends` to validate the backend specification."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1965-1969
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1969, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CanineModel", "start_line": 1965, "type": "class", "chroma:document": "CODE:\n\nclass CanineModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `CanineModel`**\n\n### Purpose and Functionality\nThe `CanineModel` class is a metaclass-based implementation for a canine-related machine learning model. It requires the use of the \"torch\" backend.\n\n### Key Implementation Details\n\n* Uses the `DummyObject` metaclass.\n* Initializes with optional arguments using `*args` and `**kwargs`.\n* Enforces the use of the \"torch\" backend during instantiation using `requires_backends`.\n\n### Dependencies and Requirements\n\n* Requires the \"torch\" backend, referenced by class attribute `_backends`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1972-1976
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1976, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CaninePreTrainedModel", "start_line": 1972, "type": "class", "chroma:document": "CODE:\n\nclass CaninePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `CaninePreTrainedModel` class is a custom model class that inherits from `DummyObject`. It uses the PyTorch backend (`torch`) and has an initializer (`__init__`) method. Key features include:\n\n* Requires the PyTorch backend for initialization.\n* Use of `requires_backends` function to enforce backend requirements.\n\nNo notable dependencies or interfaces are implemented."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1979-1980
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1980, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_canine", "start_line": 1979, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_canine(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `load_tf_weights_in_canine` function loads pre-trained weights for a model. \n\n* It checks if the required backend (specifically Torch) is specified as an argument to ensure compatibility.\n* The primary purpose of this function seems to be part of a larger architecture, possibly related to deep learning framework Canine.\n\n**Key Implementation Details:**\n\n- The use of `requires_backends` decorator to check for supported backends.\n\n**Notable Dependencies or Requirements:**\n\n- Requires the Torch backend to be specified.\n- Possibly relies on the Canine framework for model handling."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1983-1987
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1987, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChameleonForConditionalGeneration", "start_line": 1983, "type": "class", "chroma:document": "CODE:\n\nclass ChameleonForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary:**\n\n*Class Name:* `ChameleonForConditionalGeneration`\n*Purpose:* A metaclass-based class that supports conditional generation using the Torch backend.\n\n**Key Details:**\n\n*   Initiator: The class requires the Torch backend to be specified at initialization (`requires_backends(self, [\"torch\"])`).\n*   Method Signatures:\n    *   `__init__`: Initializes the object with variable arguments.\n*   Dependencies:\n    *   `DummyObject`: An underlying metaclass.\n    *   `requires_backends`: A function to validate backend requirements.\n\n**Inheritance/Implementation:**\n\nNo inherited or implemented interfaces are mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1990-1994
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 1994, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChameleonModel", "start_line": 1990, "type": "class", "chroma:document": "CODE:\n\nclass ChameleonModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ChameleonModel**\n\n*   **Purpose and Functionality**: The `ChameleonModel` class is a base class for machine learning models, utilizing the `torch` backend. It enforces the use of supported backends during initialization.\n*   **Implementation Details**:\n    *   Checks if the specified `torch` backend is available using the `requires_backends` method.\n    *   Initializes the object with variable arguments (`args`) and keyword arguments (`kwargs`).\n\n**Notable Dependencies/Requirements**: Includes the `torch` backend, which must be installed to use this class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:1997-2001
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2001, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChameleonPreTrainedModel", "start_line": 1997, "type": "class", "chroma:document": "CODE:\n\nclass ChameleonPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ChameleonPreTrainedModel`\n\nThe `ChameleonPreTrainedModel` class is a pyTorch-based model that leverages the Chameleon framework for pre-training. The primary purpose of this class is to provide a flexible foundation for large language models trained on various datasets.\n\n**Key Implementation Details:**\n\n- Initializes the model with specified `args` and `kwargs`, requiring the PyTorch backend.\n- Calls the `requires_backends` function to ensure compatibility with the specified backend (`torch`).\n\n**Notable Dependencies:**\n\n* `metaclass=DummyObject`: a metaclass indicating the type of class this is.\n* `requires_backends(self, [\"torch\"])`: a function requiring the PyTorch backend.\n\nNote that no external dependencies are explicitly mentioned in this class chunk. The class seems to rely on the Chameleon framework and its dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2004-2008
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2008, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChameleonProcessor", "start_line": 2004, "type": "class", "chroma:document": "CODE:\n\nclass ChameleonProcessor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ChameleonProcessor Class Summary**\n\n### Main Purpose and Functionality\nThe `ChameleonProcessor` class is designed as a metaclass-based processor that leverages the `DummyObject` metaclass to create instances. It relies on backend support, currently only providing \"torch\" backend functionality.\n\n### Key Implementation Details\n\n*   Initializes with variable arguments (`*args` and `**kwargs`)\n*   Requires backends support for \"torch\"\n    *   Uses the `requires_backends` function to verify the specified backend is present\n        +   `DummyObject` metaclass ensures compatibility\n*   Defaults backend to [\"torch\"]\n\n### Notable Dependencies or Requirements\n\n*   Relies on `DummyObject` metaclass for creation\n*   Requires Python backend support, specifically \"torch\""}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2011-2015
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2015, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChameleonVQVAE", "start_line": 2011, "type": "class", "chroma:document": "CODE:\n\nclass ChameleonVQVAE(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ChameleonVQVAE` class is a PyTorch-based Variational Quantize-sgd Autoencoder (VQ-VAE) implementation. \n\n*   **Primary Purpose:** This VQ-VAE model requires the PyTorch backend for execution.\n*   **Key Implementation Details:**\n    *   Initializes with a general set of arguments (`args` and `kwargs`).\n        -   Verifies the availability of the required PyTorch backend using `requires_backends`.\n    *   Uses the PyTorch backend to process the input data.\n*   **Notable Dependencies and Requirements:**\n    *   Requires the PyTorch library (backend).\n      -   Class/interface: `torch`\n      -   External Dependency: PyTorch \n\n**Code Chunk Example:**\n\n```python\nclass ChameleonVQVAE(metaclass=DummyObject):\n    \"\"\"\n    This is a summary of this Python Script class. \n    Use code snippet as reference not entire script. \"\"\"\n\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n**Notes:**"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2018-2022
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2022, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChineseCLIPModel", "start_line": 2018, "type": "class", "chroma:document": "CODE:\n\nclass ChineseCLIPModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `ChineseCLIPModel` class is an implementation of a deep learning model using the PyTorch backend. It has initialization arguments accepted via `__init__`, which also checks if the required `torch` backend is available.\n\n**Detailed Analysis:**\n\n* **Primary Purpose:** Implementation of a PyTorch-based Chinese CLIP (Contrastive Language-Image Pre-training) model.\n* **Key Implementation Details:**\n\t+ Uses PyTorch as the backing framework (`_backends = [\"torch\"]`).\n\t+ Checks for availability of `torch` backend in the initialization process (`requires_backends(self, [\"torch\"])`).\n* **Notable Dependencies or Requirements:**\n\t+ External dependency on PyTorch.\n\t+ Interface required by `__init__` method: `requires_backends`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2025-2029
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2029, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChineseCLIPPreTrainedModel", "start_line": 2025, "type": "class", "chroma:document": "CODE:\n\nclass ChineseCLIPPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ChineseCLIPPreTrainedModel`: A PyTorch-based pre-trained model for text processing.\n\n**Key Features and Functionality:**\n\n* Utilizes the `torch` backend.\n* Performs initialization with support for `args` and `kwargs`.\n\n**Notable Implementation Details:**\n\n* Calls `requires_backends` method to ensure `torch` is supported.\n\n**Dependencies and Requirements:**\n\n* Requires `torch` as a backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2032-2036
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2036, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChineseCLIPTextModel", "start_line": 2032, "type": "class", "chroma:document": "CODE:\n\nclass ChineseCLIPTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ChineseCLIPTextModel`**\n\n### Purpose and Functionality\n\nThe `ChineseCLIPTextModel` class is a text processing model that interacts with the `torch` backend. Its primary purpose is to provide an interface for handling text input data.\n\n### Implementation Details\n\n* Initializes models with support for multiple backends using the `requires_backends` function, ensuring compatibility with `torch`.\n* Note: The underlying model implementation is not provided in this code snippet.\n\n### Dependencies and Requirements\n\n* Requires the `torch` backend for execution.\n \n**Code References:** `DummyObject`, `requires_backends`, `__init__`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2039-2043
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2043, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ChineseCLIPVisionModel", "start_line": 2039, "type": "class", "chroma:document": "CODE:\n\nclass ChineseCLIPVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ChineseCLIPVisionModel` is a class that inherits from `DummyObject`. It specifies the supported backend (`torch`) for the model. The `__init__` method ensures the correct backend is provided during instantiation.\n\n**Key Details:**\n\n* Primary purpose: Implementation of a Chinese-Clip vision model\n* Key implementation:\n\t+ Requires the `torch` backend for initialization\n* Notable dependencies:\n\t+ `RequiresBackends` class, referenced internally\n\t+ External dependency: `torch`\n* Implemented interfaces: None (inherited from `DummyObject`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2046-2050
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2050, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapAudioModel", "start_line": 2046, "type": "class", "chroma:document": "CODE:\n\nclass ClapAudioModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ClapAudioModel` class is an audio model that initializes with the `torch` backend. It enforces compatibility by calling the `requires_backends` function to validate the specified backend.\n\n**Core Details:**\n\n- **Class Purpose:** An audio model that supports multiple backends, with initial support for `torch`.\n- **Primary Functionality:** Initialization and backend validation.\n- **Implementation Method Signature:** `__init__(self, *args, **kwargs)`\n\n**Notable Implementation:**\n- Uses the `_backends` class attribute to specify supported backends (`[\"torch\"]`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2053-2057
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2057, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapAudioModelWithProjection", "start_line": 2053, "type": "class", "chroma:document": "CODE:\n\nclass ClapAudioModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n** Concise Summary: **\n### Class: `ClapAudioModelWithProjection`\n#### Purpose and Functionality\nThe `ClapAudioModelWithProjection` class is a PyTorch backend-specific model that inherits from `DummyObject`. \n\n#### Main Properties:\n- `_backends`: List of supported backends, currently set to only \"torch\".\n\n### Key Implementation Details:\n\n- **Initialization**: The `__init__` method checks if the required backend \"torch\" is available before proceeding with initialization.\n\n### Notable Dependencies and Requirements:\n- Requires PyTorch as a backed (referenced by `[torck]`) in the `requires_backends` check."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2060-2064
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2064, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapFeatureExtractor", "start_line": 2060, "type": "class", "chroma:document": "CODE:\n\nclass ClapFeatureExtractor(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*Purpose:* `ClapFeatureExtractor` is a class that extracts features from input data using specific backends.\n \n**Key Implementation Details:**\n\n* Requires at least the `torch` backend to function (`requires_backends(self, [\"torch\"])`).\n \n**Notable Dependencies and Requirements:**\n\n* The ` DummyObject` metaclass is used."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2067-2071
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2071, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapModel", "start_line": 2067, "type": "class", "chroma:document": "CODE:\n\nclass ClapModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ClapModel` class utilizes the `requires_backends` function to ensure the use of the \"torch\" backend, which is set by default. The class's constructor (`__init__`) accepts variable arguments `*args` and keyword arguments `**kwargs`.\n\n**Key Implementation Details:**\n\n- Primary purpose: Initializes model with specified backend.\n- Notable Design Choice: Uses `requires_backends` function to enforce backend selection.\n\n**Dependences and Requirements:**\n\n- Related class: `dummy_object` (referenced by the metaclass `DummyObject`)\n- External dependencies: None noted within this code snippet; however, \"torch\" is assumed as a required library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2074-2078
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2078, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapPreTrainedModel", "start_line": 2074, "type": "class", "chroma:document": "CODE:\n\nclass ClapPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThe `ClapPreTrainedModel` class is a custom, trainable model that uses the Torch backend. The constructor (`__init__()`) checks if the required backend (\"torch\") is available.\n\n**Key Details**\n\n* **Purpose**: Custom, pre-trained model\n* **Backend**: Torch\n* **Constructor Method Signatures**:\n\t+ `__init__(*args, **kwargs)`\n* **Noteable Dependencies**:\n\t+ Requires the \"torch\" backend\n* **Inherited/Implemented Interfaces**: None\n\n**No notable external dependencies or interfaces are mentioned in this code chunk.**"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2081-2085
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2085, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapTextModel", "start_line": 2081, "type": "class", "chroma:document": "CODE:\n\nclass ClapTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`ClapTextModel` is a data model class using the `torch` backend. It requires the `torch` backend for initialization. \n\n**Key Details**\n\n- **Backend:** `torch`\n- **Primary purpose:** Data model\n- **Initialization requirements:** `requires_backends` method checks if `torch` backend is available\n\nNote: The `DummyObject` metaclass and `Provide` function are not included in the summary as they seem to be utility classes/functions specific to the system or framework this code is using, and their exact purpose is unclear without more context."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2088-2092
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2092, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClapTextModelWithProjection", "start_line": 2088, "type": "class", "chroma:document": "CODE:\n\nclass ClapTextModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ClapTextModelWithProjection`\n\n*   **Purpose:** A text model with projection functionality, requiring PyTorch backend.\n\n**Key Implementation Details:**\n\n*   `__init__`: Initializes the model, ensuring `requires_backends` is called with the \"torch\" backend.\n    \n    Signature: `def __init__(self, *args, **kwargs):`\n    \n*   `requires_backends`: Verifies that suitable backends are available for the model.\n    \n    Signature (inherited from parent class): `def requires_backends(self, required_backends, name):`\n\n**Notable Dependencies and Requirements:**\n\n*   Requires PyTorch backend (`_backends = [\"torch\"]`) \n*   Inherently uses `requires_backends` function from a parent class"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2095-2099
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2099, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPForImageClassification", "start_line": 2095, "type": "class", "chroma:document": "CODE:\n\nclass CLIPForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `CLIPForImageClassification` class is an implementation of the CLIP (Contrastive Language-Image Pre-training) model for image classification tasks. It inherits from a metaclass `DummyObject`.\n\n**Key Details**\n\n* The class only supports the \"torch\" backend.\n* In the constructor, it checks if both forward-mode and backward-mode are supported on all required backends using `requires_backends(self, [\"torch\"])`.\n \n**Note**: The provided code snippet lacks the implementation of the model's methods (e.g., `forward` or `backward`) due to what appears to be a missing detail.\n\nNo notable dependencies are listed in this snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2102-2106
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2106, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPModel", "start_line": 2102, "type": "class", "chroma:document": "CODE:\n\nclass CLIPModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CLIPModel`: A PyTorch-based class for the CLIP (Contrastive Language-Image Pretraining) model. It initializes with required backend \"torch\" and performs validation checking.\n\n**Key Details:**\n \n* Class is parameterized using `requires_backends`.\n* Requires backend \"torch\".\n* Uses `DummyObject` metaclass.\n\n**Dependences/Requirements:**\n\n* torch (backend)\n* DummyObject (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2109-2113
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2113, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPPreTrainedModel", "start_line": 2109, "type": "class", "chroma:document": "CODE:\n\nclass CLIPPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CLIPPreTrainedModel` class is a PyTorch-specific model, inheriting from a dummy metaclass. It initializes itself with no required arguments and specifies that it relies on the `torch` backend for functionality.\n\n**Key Implementation Details:**\n\n- Requires PyTorch as a dependency.\n- Inherited from `DummyObject`.\n\n**Notable Dependency/Interface:** `requires_backends(self, [\"torch\"])`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2116-2120
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2120, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPTextModel", "start_line": 2116, "type": "class", "chroma:document": "CODE:\n\nclass CLIPTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CLIPTextModel` class is a text model that utilizes the PyTorch backend. It initializes with various arguments and requires the \"torch\" backend to be installed for proper functionality.\n\n**Key Details:**\n\n- Primary purpose: Initialize text model with required argument and backend check\n- Notable implementation:\n    - Uses `DummyObject` metaclass (implementation not shown)\n    - Exercises `requires_backends` function to verify presence of \"torch\" backend\n\n**Notable Dependencies/Requirements:**\n\n- PyTorch library (`backends = [\"torch\"]`)\n- ` DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2123-2127
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2127, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPTextModelWithProjection", "start_line": 2123, "type": "class", "chroma:document": "CODE:\n\nclass CLIPTextModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CLIPTextModelWithProjection` class is a PyTorch-based model that utilizes the CLIP (Contrastive Learning of Representations) architecture. It consists of a text component and an image component with projection.\n\n**Key Details:**\n\n* `requires_backends(self, [\"torch\"])`: Verifies presence of required backend (\"torch\") during instantiation.\n* **Notable Dependencies:** \n  * CLIP architecture\n  * PyTorch library\n\n**Interfaces/ Inheritance:**\n \n* Implements CLIP-related interfaces or inherits from a base class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2130-2134
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2134, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPVisionModel", "start_line": 2130, "type": "class", "chroma:document": "CODE:\n\nclass CLIPVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**CLIPVisionModel Class Summary**\n\n**Main Purpose and Functionality:**\nThe `CLIPVisionModel` class is an implementation of the CLIP (Contrastive Language-Image Pre-training) model using the PyTorch backend.\n\n**Key Implementation Details:**\n\n* The class requires the PyTorch backend to function (`requires_backends(self, [\"torch\"])`).\n \n\n**Notable Dependencies or Requirements:**\n\n* PyTorch\n* `DummyObject` metaclass (part of PyTorch's modeling framework)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2137-2141
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2141, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPVisionModelWithProjection", "start_line": 2137, "type": "class", "chroma:document": "CODE:\n\nclass CLIPVisionModelWithProjection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`CLIPVisionModelWithProjection` is a PyTorch-based vision model that leverages the CLIP framework. The class ensures availability of the required `torch` backend.\n\n**Key Details**\n\n* Main Functionality: Utilizes the CLIP framework for vision tasks\n* Important Method Signature: `__init__()` which requires the presence of the specified `torch` backend\n\n**Notable Dependencies and Requirements**\n\n* External Dependency: `torch`\n* Inherited/Implemented Interfaces:\n    None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2144-2148
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2148, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPSegForImageSegmentation", "start_line": 2144, "type": "class", "chroma:document": "CODE:\n\nclass CLIPSegForImageSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary**\n\nThe `CLIPSegForImageSegmentation` class is a custom model implementation using the PyTorch backend. \n\n- **Main Purpose and Functionality**: This class defines a segmentation model built on top of CLIP (Contrastive Language-Image Pre-training) architecture.\n  - Primary purpose: Segment images using a pre-trained language model.\n\n**Implementation Details**\n\n- **Key Algorithm/Pattern**: CLIP-based segmentation using the `requires_backends` method to assert PyTorch compatibility.\n \n- **Key Method Signatures and Interfaces**\n  - `__init__`: The constructor that initializes the model with optional arguments (`*args` and `**kwargs`) and verifies PyTorch support.\n\n- **Notable Dependencies**\n\n  - Related class: `DummyObject` ( metaclass )\n  \n  - External dependency: `torch` (required backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2151-2155
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2155, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPSegModel", "start_line": 2151, "type": "class", "chroma:document": "CODE:\n\nclass CLIPSegModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n```python\nCLIPSegModelSummary:\n\n* Main Purpose: A PyTorch-based CLIP segmentation model implementation using the DummyObject metaclass.\n* Key Implementation Details:\n\t+ Initializes with support for torch backend using `requires_backends` method.\n\t+ Requires specified Backend (\"torch\") to be available at instantiation.\n```\n\n**Key Takeaways**\n\n- Class name and metaclass are set to `CLIPSegModel` with `DummyObject` metaclass.\n- The `_backends` list is initialized with \"torch\" as the supported backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2158-2162
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2162, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPSegPreTrainedModel", "start_line": 2158, "type": "class", "chroma:document": "CODE:\n\nclass CLIPSegPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThe `CLIPSegPreTrainedModel` class is a custom PyTorch model (backend: \"torch\") that inherits from `DummyObject`. The `__init__` method initializes the model with arbitrary arguments (`*args, **kwargs`) and requires the PyTorch backend.\n\n**Detailed Breakdown**\n\n- **Class Inheritance**: `CLIPSegPreTrainedModel` is a subclass of `DummyObject`.\n- **Backend Requirement**: The model requires the PyTorch backend (`\"torch\"`).\n- **Initialization Method**: The `__init__` method initializes the model and is implemented using the `requires_backends` function, which ensures that the required backend is present.\n\n**Key Points**\n\n* Backend: \"torch\"\n* Class Inheritance: from DummyObject\n* Initialization Requirements: requires backends [\"torch\"]\n- Note: The actual implementation details of the `DummyObject` class are not provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2165-2169
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2169, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPSegTextModel", "start_line": 2165, "type": "class", "chroma:document": "CODE:\n\nclass CLIPSegTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CLIPSegTextModel` class is a machine learning model implemented using the `DummyObject` metaclass. It requires the `torch` backend to function and initializes it.\n\n**Key Details:**\n\n*   **Initialization:** The model takes variable arguments (`*args`) and keyword arguments (`**kwargs`) in its constructor.\n*   **Backend Requirement:** The `requires_backends` method is used with the `\"torch\"` backend to ensure the model can run on this backend.\n*   **Class Properties:**\n    *   `_backends`: A class property that lists the required backend(s), which are `\"torch\"`.\n\n**Notable Dependencies/Requirements:**\n\n*   `dummyobject` (DummyObject metaclass)\n*   `torch` (required backend)\n*   `requires_backends` method"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2172-2176
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2176, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CLIPSegVisionModel", "start_line": 2172, "type": "class", "chroma:document": "CODE:\n\nclass CLIPSegVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary: CLIP Segmentation Model Class**\n\n**Main Purpose and Functionality:**\nThe `CLIPSegVisionModel` class is a custom PyTorch-based model implementation for segmentation tasks. Its primary purpose is to provide a foundation for training models on image segmentation data.\n\n**Key Implementation Details:**\n\n*   **Backend Specification:** The model is designed to work with the PyTorch backend, as specified by the `requires_backends` method.\n*   **Initialization Method:** The `__init__` method initializes the model instance and enforces the use of the \"torch\" backend.\n\n**Notable Dependencies/Requirements:**\n\n*   `DummyObject` (metaclass): A dummy class used for metaclasses in Python 3.8+.\n*   `requires_backends` (function): A function that checks if a specified model requires one or more backends to be supported.\n\nHere is the concise code summary rewritten from scratch: \n\n### CLIP Segmentation Model Class\n\n**Primary purpose**: Custom PyTorch-based class for segmentation tasks.\n\n```python\nclass CLIPSegVisionModel:\n    \"\"\"\n    A custom PyTorch-based model implementation for image segmentation.\n    \n    Attributes:\n        backend (str): The required backend for the model. Default is \"torch\".\n    \"\"\"\n    backend = \"torch\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Initialize the model instance and enforce the use of 'torch' backend.\"\"\"\n        self.requires_backends([\"torch\"])\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2179-2183
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2183, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpDecoder", "start_line": 2179, "type": "class", "chroma:document": "CODE:\n\nclass ClvpDecoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ClvpDecoder`**\n\n* **Purpose**: A custom decoder class for parsing CLVP (Compression-Lossless Variable-precision Image Pixel Network) data using the Torch backend.\n\n* **Key Implementation Details**:\n\t+ Requires support for the \"torch\" backend during initialization.\n\t+ Uses method `requires_backends(self, [\"torch\"])` to enforce this requirement.\n\n* **Noteable Dependencies/Requirements**:\n\t+ Inherited from metaclass `DummyObject`.\n\t+ Relies on Torch as a required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2186-2190
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2190, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpEncoder", "start_line": 2186, "type": "class", "chroma:document": "CODE:\n\nclass ClvpEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ClvpEncoder`\n\n**Purpose and Functionality:** A class that encodes data using multiple backends.\n\n*   Primary purpose: To initialize the encoder object with required backend(s).\n*   Main functionality: Verifies the presence of supported backend(s) using the `requires_backends` method before instantiation.\n\n**Key Implementation Details:**\n\n*   Uses a metaclass (`DummyObject`) to define class attributes.\n*   Initializes the object with optional arguments and keyword-only arguments.\n\n**Notable Dependencies or Requirements:**\n\n*   Reuses a function named `requirementsBackends (line not shown)` for backend validation.\n\nThe code snippet provides an initial implementation, but it lacks core encoding logic and specific backend implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2193-2197
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2197, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpForCausalLM", "start_line": 2193, "type": "class", "chroma:document": "CODE:\n\nclass ClvpForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ClvpForCausalLM` is a PyTorch backend-aware class for causal language modeling. Its primary purpose is to integrate with a larger framework.\n\n**Key Implementation Details:**\n\n- Initializes with `torch` backend requirement\n- Uses `requires_backends` function from parent module\n\n**Notable Dependencies and Requirements:**\n\n- `torchacl`: dependency on Acumen library for math operations\n- `_DummyObject`: metaclass used to define the class"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2200-2204
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2204, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpModel", "start_line": 2200, "type": "class", "chroma:document": "CODE:\n\nclass ClvpModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nClass `ClvpModel` (with metaclass `DummyObject`) representing a PyTorch-dependent model.\n\n**Key Implementation Details:**\n\n* Initializes the model with optional args and kwargs using the `_check_backends` method.\n\n**Dependencies/Requirements:**\n\n* Requires PyTorch backend support (`_backends = [\"torch\"]`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2207-2211
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2211, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpModelForConditionalGeneration", "start_line": 2207, "type": "class", "chroma:document": "CODE:\n\nclass ClvpModelForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThe `ClvpModelForConditionalGeneration` class is a machine learning model using the Torch backend. It initializes with support for the \"torch\" backend and enforces the use of this backend during instantiation.\n\n**Key Details:**\n\n* **Primary Purpose:** Conditional generation model using Torch.\n* **Implementation:** Initializes model with Torch backend, enforcing its usage.\n* **Notable Dependencies:**\n\t+ Torch\n* **Initializers:**\n\t+ `requires_backends(self, [\"torch\"])`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2214-2218
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2218, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ClvpPreTrainedModel", "start_line": 2214, "type": "class", "chroma:document": "CODE:\n\nclass ClvpPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\nThis is a base class `ClvpPreTrainedModel` with a metaclass `DummyObject`. It defines the following:\n\n* Primary purpose: Provides a model framework for Pre-trained models using the Torch backend.\n* Key implementation details:\n\t+ The `requires_backends` function is used to specify that this model requires the `torch` backend.\n\t+ The class uses a dummy metaclass (`DummyObject`) without providing any further information about its behavior.\n* Notable dependencies:\n\t+ `torch`: a PyTorch backend dependency\n* No external interfaces are implemented. \n\nAlternatively, if you'd like to see it in Markdown format with sections:\n\n**Class Summary**\n===============\n\n### Primary Purpose\n\nProvides a model framework for Pre-trained models using the Torch backend.\n\n### Key Implementation Details\n\nThe class requires the `torch` backend and uses a dummy metaclass (`DummyObject`) without providing further information about its behavior.\n\n### Notable Dependencies\n\n* `torch`: a PyTorch backend dependency"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2221-2225
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2225, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CodeGenForCausalLM", "start_line": 2221, "type": "class", "chroma:document": "CODE:\n\nclass CodeGenForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`CodeGenForCausalLM`\n\nA meta-class `CodeGenForCausalLM` for generating code for causal language models. It inherits from the `DummyObject` metaclass.\n\n**Key Features:**\n\n* Supports only one backend:torch\n* Requires torch backend\n\n**Implementation Details:**\n\n* Initializes with args and kwargs\n\n**Notable Dependencies:**\n\n* torch (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2228-2232
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2232, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CodeGenModel", "start_line": 2228, "type": "class", "chroma:document": "CODE:\n\nclass CodeGenModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`CodeGenModel` class is a metadata-driven model for generating code for specific backends. It initializes with the torch backend by default.\n\n### Key Points:\n\n*   `requires_backends`: Verifies that at least `[\"torch\"]` backend is provided during initialization\n*   `__init__()`: Initializes the model without any additional configuration\n\n### Implementation Details:\n\n*   The class utilizes the `DummyObject` metaclass, likely intended to mimic a specific design pattern.\n*   No specific methods or attributes are implemented aside from the constructor.\n\n### Notable Dependencies/Requirements:\n\n*   `requires_backends()`: A function (not shown in this snippet) that validates required backends for model initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2235-2239
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2239, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CodeGenPreTrainedModel", "start_line": 2235, "type": "class", "chroma:document": "CODE:\n\nclass CodeGenPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n===============\n\n`CodeGenPreTrainedModel`\n\n### Main Purpose and Functionality\n\n`CodeGenPreTrainedModel` is a class that inherits from `DummyObject`, serving as the base model for code generation with pre-trained models.\n\n### Key Implementation Details\n\n* Requires the `torch` backend for execution.\n* Initializes model instance using `__init__` method.\n\n### Notable Dependencies and Requirements\n\n* `torch`: Required backend for model execution.\n\n**Summary Notes**\n\n* `DummyObject` serves as a metaclass, not explicitly stated in summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2242-2246
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2246, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CohereForCausalLM", "start_line": 2242, "type": "class", "chroma:document": "CODE:\n\nclass CohereForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `CohereForCausalLM`**\n\nThe `CohereForCausalLM` class is a PyTorch-based implementation of a natural language processing model using the CoHere library. It serves as a front-end for causal language models.\n\n**Key Implementation Details:**\n\n* Requires PyTorch backend (`requires_backends(self, [\"torch\"])`)\n* Utilizes the ` DummyObject` metaclass\n\n**Notable Dependencies and Requirements:**\n\n* CoHere library\n* PyTorch"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2249-2253
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2253, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CohereModel", "start_line": 2249, "type": "class", "chroma:document": "CODE:\n\nclass CohereModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CohereModel` class utilizes the `DummyObject` metaclass and is designed to accommodate the \"torch\" backend. The class initializes with variable arguments `*args` and keyword arguments `**kwargs`, ensuring that the \"torch\" backend is required through the `requires_backends` function.\n\n**Key Insights:**\n\n- **Backend Requirement:** The class relies on the \"torch\" backend, as specified by `_backends`.\n- **Initialization Method:** The `__init__` method initializes the instance with variable `*args` and `**kwargs`, followed by the `requires_backends` function.\n- **Dependency:** No explicit dependency mention, but inferred to be the \"torch\" library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2256-2260
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2260, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CoherePreTrainedModel", "start_line": 2256, "type": "class", "chroma:document": "CODE:\n\nclass CoherePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CoherePreTrainedModel`: A lightweight class implementing a pre-trained model using the `torch` backend.\n\n**Key Implementation Details:**\n\n* Expects the `torch` backend to be supported (`requires_backends` check)\n* Initializes with arbitrary arguments and keyword arguments using variable-length args and kwargs\n\n**Dependencies/Requirements:**\n\n* `torch`: The primary backend required for this model\n* Inherited from: No explicit inheritance, potentially inheriting from a base class not shown"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2263-2267
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2267, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Cohere2ForCausalLM", "start_line": 2263, "type": "class", "chroma:document": "CODE:\n\nclass Cohere2ForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Cohere2ForCausalLM**\n\n*   **Purpose and Functionality:** This class is a PyTorch backend for the `Cohere2` model, designed to process causal language models.\n\n    The primary purpose of this class is to provide input compatibility with the `torch` backend for integrating the `Cohere2` model in deep learning pipelines.\n*   **Key Implementation Details:**\n\n    *   The class initializes a `_backends` list, which specifies that only the `torch` backend is supported (`_backends = [\"torch\"]`).\n    *   The `__init__` method checks if the required backend is installed using the `requires_backends` function and ensures it's available before proceeding.\n*   **Notable Dependencies or Requirements:**\n\n    *   The class relies on the `torch` backend, which is an essential dependency for its functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2270-2274
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2274, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Cohere2Model", "start_line": 2270, "type": "class", "chroma:document": "CODE:\n\nclass Cohere2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `Cohere2Model` class is a custom model class that leverages the PyTorch backend. It requires the use of at least one \"torch\" backend instance during initialization.\n\n**Key Details:**\n\n*   Primary purpose: Custom neural network model with backend dependency.\n*   Implementation details:\n    *   Uses the `DummyObject` metaclass for class definition.\n    *   Initializes instance using the `__init__` method, passing `throws` or checks `requires_backends`.\n    *   Explicitly requires at least one \"torch\" backend instance.\n        Method Signature: `def __init__(self, *args, **kwargs)`: Installs the torch backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2277-2281
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2281, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Cohere2PreTrainedModel", "start_line": 2277, "type": "class", "chroma:document": "CODE:\n\nclass Cohere2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Cohere2PreTrainedModel` class is a PyTorch implementation of the Cohere-2 language model. It inherits from `DummyObject` and has a single backend dependency for \"torch\".\n\n```\nPurpose: Cohere-2 Language Model Implementation in PyTorch\nBackend Dependency: torch\n```\n\n**Key Implementation Details:**\n\n* Requires the `torch` backend to function.\n* Uses `requires_backends(self, [\"torch\"])` to validate the backend support.\n\n**Notable Dependencies and Requirements:**\n* Implemented interface not specified, but inherits from `DummyObject`.\n* No external dependencies mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2284-2288
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2288, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ColPaliForRetrieval", "start_line": 2284, "type": "class", "chroma:document": "CODE:\n\nclass ColPaliForRetrieval(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ColPaliForRetrieval` class:\n \n* Primary purpose: Implements a `PaliBackend` instance with PyTorch backend support (`_backends = [\"torch\"]`)\n* Requirements: Specifies that the \"torch\" backend is required at initialization.\n\n**Key Details:**\n\n* Uses `requires_backends` function to validate backend support\n* Implemented as `DummyObject` metaclass indicating no inheritance or abstract methods\n\n**Notable Dependencies:**\n\n* `torch`: required PyTorch backend implementation"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2291-2295
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2295, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ColPaliPreTrainedModel", "start_line": 2291, "type": "class", "chroma:document": "CODE:\n\nclass ColPaliPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ColPaliPreTrainedModel`\n\n*   Primary Purpose: A pre-trained model implementation built on top of the `torch` backend.\n\n**Key Implementation Details:**\n\n*   Initializes with *args and **kwargs.\n*   Requires the `torch` backend for functionality.\n\n**Notable Dependencies or Requirements:**\n\n*   The model utilizes the `torch` library as its primary backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2298-2302
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2302, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConditionalDetrForObjectDetection", "start_line": 2298, "type": "class", "chroma:document": "CODE:\n\nclass ConditionalDetrForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ConditionalDetrForObjectDetection Class Summary**\n\n**Class Purpose**: Conditional Detr for Object Detection with support for multiple backends.\n\n**Key Implementation Details**:\n\n*   Uses the `torch` backend by default.\n*   Verifies that the `torch` backend is required at instantiation.\n*   No additional functionality available in this snippet.\n\n**Notable Dependencies/Requirements**:\n\n*   Built on top of `DemandingBackend`, with `torch` being one of the supported backends.\n*   No external dependencies mentioned.\n\n**Note**: The `DummyObject` metaclass is used, but its purpose is unclear without more context."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2305-2309
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2309, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConditionalDetrForSegmentation", "start_line": 2305, "type": "class", "chroma:document": "CODE:\n\nclass ConditionalDetrForSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ConditionalDetrForSegmentation`\n\nA specialized variant of the DETR model for segmentation tasks, utilizing the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes with optional arguments (`*args`, `**kwargs`)\n* Requires the `torch` backend to be available (backends are stored in `_backends`)\n* Calls `requires_backends` function to verify and prepare required backends\n\n**Notable Dependencies/Requirements:**\n\n* Backend: `torch`\n* Inherited Interfaces: Not specified"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2312-2316
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2316, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConditionalDetrModel", "start_line": 2312, "type": "class", "chroma:document": "CODE:\n\nclass ConditionalDetrModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ConditionalDetrModel`: A PyTorch-based model class that enforces the use of \"torch\" backend for runtime checks. The model's initialization ensures compatibility with the specified backend.\n\n**Details:**\n\n* **Purpose**: Conditional Detr Model implementation\n* **Implementation details**:\n\t+ Backend support only allows `torch`\n\t+ `__init__` method verifies backend requirements using `requires_backends` function\n* **Notable dependencies and requirements**:\n\t+ `DummyObject` metaclass\n\t+ `requires_backends` function (not shown in this snippet)\n\t+ `torch` as the primary supported backend"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2319-2323
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2323, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConditionalDetrPreTrainedModel", "start_line": 2319, "type": "class", "chroma:document": "CODE:\n\nclass ConditionalDetrPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Conditional Detr Pre-Trained Model**\n\n*   **Main Purpose:** This class represents a conditionally detached pre-trained model for the DETR (DEtection TranSlation) architecture.\n*   **Primary Purpose:** The model's primary purpose is to provide a detachable representation of a pre-trained model, allowing for conditional detachment and manipulation.\n*   **Key Implementation Details:**\n    *   The class inherits from `DummyObject` using the `metaclass` parameter.\n    *   It requires the `torch` backend in the constructor call (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2326-2330
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2330, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertForMaskedLM", "start_line": 2326, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ConvBertForMaskedLM` is a custom PyTorch model class (`metaclass=DummyObject`) designed for masked language modeling. It initializes with `torch` as the primary backend.\n\n**Key Details:**\n\n* Primary purpose: Perform masked language modeling tasks using a modified BERT architecture.\n* Notable dependencies:\n\t+ Torch library for backend implementation\n\t+ Custom `DummyObject` metaclass\n\n**Implementation Focus:** The class' initialization ensures that it requires the `torch` backend, setting up the foundation for its masked language modeling functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2333-2337
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2337, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertForMultipleChoice", "start_line": 2333, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ConvBertForMultipleChoice` class is a custom PyTorch model implementation of the BERT model optimized for multiple-choice tasks. It inherits from a dummy object and specifically targets the \"torch\" backend.\n\n**Key Details:**\n\n*   **Model Type:** `ConvBertForMultipleChoice`\n*   **Backend:** \"torch\"\n*   **Primary Functionality:** Multiple-choice task optimization of the BERT model\n*   **Method Signatures/Interfaces:**\n    *   `Requires backends(self, [\"torch\"])`: Verifies that the required backend is available.\n*   **Notable Dependencies:**\n    +   Inherited from `DummyObject`\n    +   Targets \"torch\" backend\n\nLet me know if you want any further clarification!"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2340-2344
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2344, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertForQuestionAnswering", "start_line": 2340, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ConBertForQuestionAnswering Class Summary**\n\n### Purpose and Functionality\nThe `ConvBertForQuestionAnswering` class is a custom transformer model built on top of BERT, specifically designed for question-answering applications.\n\n### Key Implementation Details\n\n* Utilizes the `torch` backend.\n* Integrates `dummy_object` metaclass.\n* Requires `torch` as a dependency during initialization.\n\n### Notable Dependencies\n\n* `torch`: primary backend framework.\n\n**Note:** This summary focuses on providing a concise and accurate overview of the class, highlighting its purpose, key implementation details, and notable dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2347-2351
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2351, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertForSequenceClassification", "start_line": 2347, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ConvBertForSequenceClassification`**\n\n*   **Main Purpose and Functionality:** This class implements a sequence classification model built on top of BERT (Bidirectional Encoder Representations from Transformers). It utilizes the PyTorch backend.\n\n**Key Implementation Details:**\n\n*   Uses the `torch` backend for execution.\n*   The `_backends` attribute specifies that this model requires the `torch` backend to be instantiated.\n\n**Notable Dependencies and Requirements:**\n\n*   Requires the `torch` library for execution.\n*   Inherently uses the `DummyObject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2354-2358
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2358, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertForTokenClassification", "start_line": 2354, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ConvBertForTokenClassification` is a PyTorch-based token classification model class, utilizing ConvBert as the underlying model. The primary purpose of this class is to facilitate efficient token classification tasks.\n\n\n**Key Implementation Details:**\n\n* Requires `torch` backend installation.\n\n**Notable Dependencies/Interfaces:**\n\n* Uses `requires_backends` function from `DummyObject` metaclass with a dependency on `torch`.\n* Implements an instance initialization method (`__init__`) with variable and keyword arguments."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2361-2365
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2365, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertModel", "start_line": 2361, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ConvBertModel`**\n\n### Purpose and Functionality\nThe `ConvBertModel` class is a PyTorch-based convolutional neural network (CNN) model that utilizes the BERT architecture.\n\n### Key Implementation Details\n\n*   Requires the \"torch\" backend to be installed.\n*   Initiates with an optional list of arguments (`args`) and keyword arguments (`kwargs`).\n\n### Notable Dependencies or Requirements\n\n*   References the `DummyObject` metaclass, which is not included in this code snippet but suggests a potential dependency or override for default class settings.\n*   Inherently requires the \"torch\" backend to function.\n\nThis summary focuses on the most relevant details of the provided class code chunk."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2368-2372
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2372, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvBertPreTrainedModel", "start_line": 2368, "type": "class", "chroma:document": "CODE:\n\nclass ConvBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ConvBertPreTrainedModel**\n\n* **Purpose:** A PyTorch-based implementation of the pre-trained BERT model for convolutional layers.\n\n* **Main Functionality:**\n\t+ Initializes the model with support for the \"torch\" backend.\n\t+ Verifies that the required backend is available.\n\nKey Details:\n\n* Uses `DummyObject` as a metaclass.\n* Requires the \"torch\" backend for initialization.\n\nDependencies:\n\t+ Related backends: torch\n\t+ External dependencies:\n\t\t- PyTorch library\n\nInterfaces/Methods:\n\t- `requires_backends(self, ['torch'])`: Verifies required backend availability."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2375-2376
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2376, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_convbert", "start_line": 2375, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_convbert(*args, **kwargs):\n\n\nSUMMARY:\n**Function Summary: `load_tf_weights_in_convbert`**\n\n Loads TensorFlow weights compatible with ConVertB.\n\n**Primary Purpose:** Loads and loads pre-trained model weights for use in ConVert models.\n\n**Key Implementation Details:**\n\n* Expects input arguments using variable positional and keyword arguments.\n* Requires compatibility with both ConVert and PyTorch backends.\n* No specific algorithm or pattern is explicitly mentioned, but it's likely utilizing TensorFlow's weight loading mechanisms.\n\n**Notable Dependencies/Requirements:**\n\n* `load_tf_weights_in_convbert` (itsself) as the main entry point for load_tf_weights\n* PyTorch backend compatibility (with \"torch\" being a required backend)\n* No external dependencies are mentioned, implying this function depends on core TensorFlow functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2379-2383
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2383, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextBackbone", "start_line": 2379, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is the `ConvNextBackbone` class definition, part of a PyTorch-based neural network architecture.\n\n*   Primary purpose: A backbone component designed for next-generation convolutional neural networks.\n*   Method signature and implementation details:\n    *   `__init__`: Initializer method requiring the `\"torch\"` backend to be detected.\n        *   It calls the `requires_backends` function with [\"torch\"] argument, indicating that this class relies on PyTorch as its primary backend.\n\nKey dependencies:\n*   The class uses the `DummyObject` metaclass and relies on the `torch` library for backend detection.\n\nNote: The class is not defined in full within this code snippet; it is likely part of a larger project or module implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2386-2390
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2390, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextForImageClassification", "start_line": 2386, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ConvNextForImageClassification` is an image classification model using the TensorFlow and PyTorch backends. The class uses `DummyObject` as its metaclass.\n\n**Key Details:**\n\n* Primary Purpose: Image Classification\n* Backends Used: TensorFlow, PyTorch (specifically Torch backend)\n* Requirements: The `requires_backends` method is called with a list of required backends (`[\"torch\"]`) to initialize the class instance.\n \n\nNote: Since this code snippet appears to be incomplete and doesn't provide concrete implementation details, the analysis focused on extracting general information about the class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2393-2397
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2397, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextModel", "start_line": 2393, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ConvNextModel` class inherits from `DummyObject` (not a standard Python metaclass) and serves as a base model for convolutional neural networks. It requires the PyTorch backend.\n\n**Main Purpose and Functionality:**\n```python\nclass ConvNextModel:\n    \"\"\"\n    A base model for convolutional neural networks.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Initializes the model with supported backends.\n\n        Args:\n            args: Variable number of arguments (not used)\n            kwargs: Keyword arguments (not used)\n        \"\"\"\n```\n\n**Key Implementation Details:**\n- Uses `torch` as a required backend.\n- No specific algorithms or patterns are highlighted, but it's a base model for convolutional neural networks.\n\n**Notable Dependencies or Requirements:**\n- PyTorch (`\"torch\"`)\n\n(Note that the summary does not include any unnecessary details and highlights the main purpose and functionality of the class.)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2400-2404
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2404, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextPreTrainedModel", "start_line": 2400, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`ConvNextPreTrainedModel` is a PyTorch-based convolutional neural network (CNN) model architecture implemented as a class. It inherits from the `DummyObject` metaclass.\n\n**Key Details**\n\n* Primary purpose: To establish a CNN model with support for PyTorch backend\n* Key implementation details:\n\t+ Requires PyTorch backend to be installed (`requires_backends(self, [\"torch\"])`)\n* Notable dependencies/requirements:\n\t+ PyTorch library (`@pytorch/fb core common.utils` contains ` DummyObject metaclass)\n\t+ No external dependencies listed\n\n**Summary Conclusion**\n\nThe provided code snippet introduces a custom CNN model class that relies on the PyTorch backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2407-2411
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2411, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextV2Backbone", "start_line": 2407, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextV2Backbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n```python\nConvNextV2Backbone class implements a backbone for computer vision models using Torch as the primary backend.\n\n**Key Features:**\n\n*   Primary purpose: Defines a customizable ConvNextV2 backbone architecture with support for multiple backends.\n*   Implementation details:\n    *   Uses Torch backend (torch) by default (`_backends = [\"torch\"]`).\n    *   Initializes the class instance using `__init__` method, which ensures requirement of Torch backend is fulfilled (`requires_backends(self, [\"torch\"])`).\n```\n**Notable Dependencies:**\n\n*   Torch (`torch`)\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2414-2418
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2418, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextV2ForImageClassification", "start_line": 2414, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextV2ForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ConvNextV2ForImageClassification` class implements a neural network model for image classification using the Torch backend.\n\n**Key Details:**\n\n* Primary purpose: Image classification\n* Algorithm/Pattern: ConvNext architecture\n* Notable Implementation:\n\t+ Requires Torch backend (using `requires_backends`)\n\t+ Uses `torch` as primary backend\n\n**Dependencies and Requirements:**\n\n* Torch library (explicitly stated as a required backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2421-2425
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2425, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextV2Model", "start_line": 2421, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextV2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ConvNextV2Model` class is an implementation of the ConvNetV2 architecture. It serves as a base model for various convolutional neural networks.\n\n**Key Details:**\n\n* **Initialization**: The model requires the `torch` backend to be specified during instantiation.\n* **Backend**: The model uses the _backend property set to [\"torch\"].\n* **Interfaces**: This class inherits requirements from the `DummyObject` metaclass, which likely provides a basic framework for object creation.\n\n**Notable Dependencies:**\n\n* `torch`\n* `DummyObject`\n\nThis summary highlights the main purpose and functionality of the code chunk while focusing on key implementation details such as initialization, backend usage, and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2428-2432
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2432, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ConvNextV2PreTrainedModel", "start_line": 2428, "type": "class", "chroma:document": "CODE:\n\nclass ConvNextV2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ConvNextV2PreTrainedModel**\n\n*   **Purpose and Functionality:** This class represents a pre-trained model based on the ConvNextV2 architecture. It inherits from `DummyObject` with a specific backend requirement for \"torch\".\n\n*   **Key Implementation Details:**\n    *   Uses `DummyObject` as its metaclass.\n    *   Enforces compatibility with the \"torch\" backend in its initialization.\n\n*   **Dependencies and Requirements:**\n\n    *   Backend dependency: \"torch\"\n        *   Related class/functions: `requires_backends`\n    \nNo additional external dependencies identified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2435-2439
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2439, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CpmAntForCausalLM", "start_line": 2435, "type": "class", "chroma:document": "CODE:\n\nclass CpmAntForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CpmAntForCausalLM` class is a custom implementation of the `AntForCausalLM` model using PyTorch as its backend. It initializes with a list of required backends (`\"torch\"`).\n\n**Key Details:**\n\n- **`metaclass=DummyObject`**: The class uses a custom metaclass, but its purpose is not clear without further context.\n\n- **`requires_backends(self, [\"torch\"])`**: Verifies that the PyTorch backend is present before initializing the object.\n\n**Dependences and Requirements:** \n\n- PyTorch\n\nThis summary focuses on the main functionality of the class and its key implementation details, providing an overview for developers to understand this code segment within a larger context."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2442-2446
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2446, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CpmAntModel", "start_line": 2442, "type": "class", "chroma:document": "CODE:\n\nclass CpmAntModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CpmAntModel` class is a model implementation that utilizes the `torch` backend. It enforces the presence of the `torch` backend during initialization.\n\n**Key Details:**\n\n* Primary purpose: Model implementation with backend enforcement\n* Key feature: Requires `torch` backend for initialization\n* Method Signature: `__init__(*args, **kwargs)`\n\n**Notable Dependencies/Requirements:**\n\n* Related class/functions: `DummyObject` (metaclass)\n* External dependency:\n\t+ `torch`\n* Implemented interfaces:\n\t+ None (this is a custom implementation)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2449-2453
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2453, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CpmAntPreTrainedModel", "start_line": 2449, "type": "class", "chroma:document": "CODE:\n\nclass CpmAntPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CpmAntPreTrainedModel` class is an implementation of a model that uses the Torch backend. The constructor initializes the model by checking if the required TensorBackend framework is provided.\n\n**Key Details:**\n\n*   **Model Type**: CpmAnt Pre-Trained Model\n*   **Primary Backend**: `torch`\n*   **Required Frameworks**:\n    *   `torch` (TensorBackend)\n\n**Implementation Highlights:**\n\n*   `__init__`: Initializes the model, checks for required TensorBackend (`torch`)\n*   `requires_backends`: Verifies that the Torch backend is provided\n\nNote: No external dependencies are listed as it only includes internal Python framework information."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2456-2460
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2460, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CTRLForSequenceClassification", "start_line": 2456, "type": "class", "chroma:document": "CODE:\n\nclass CTRLForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `CTRLForSequenceClassification` class is a custom control flow module that utilizes the torch backend. It inherits from `DummyObject` and initializes with the required torch backend for its operation.\n\n**Key Details:**\n\n* Primary purpose is to enable sequence classification using the specified backends.\n* Utilizes **torcht** as an internal module (not explicitly imported).\n* Requires the presence of the **torch** backend at initialization time (`requires_backends(self, [\"torch\"])`).\n\n**Additional Notes:**\n\n* No external dependencies or class/interface references mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2463-2467
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2467, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CTRLLMHeadModel", "start_line": 2463, "type": "class", "chroma:document": "CODE:\n\nclass CTRLLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nCTRLLMHeadModel is a PyTorch-based classifier head model with the following key details:\n\n*   Primary purpose: Classify input sequences using a transformer encoder and output classification probabilities.\n\n*   Implementation details:\n    *   Utilizes the `torch` backend for computation.\n    *   The `requires_backends` method ensures compatibility with the specified backend.\n\n*   Notable dependencies:\n    *   Referenced `DummyObject` metaclass from parent package.\n    *   External dependency: PyTorch library (`torch`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2470-2474
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2474, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CTRLModel", "start_line": 2470, "type": "class", "chroma:document": "CODE:\n\nclass CTRLModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `CTRLModel` class is a metaclass-defined class that serves as a base for control models using the `torch` backend. It enforces the use of the `torch` backend by requiring it in its initialization.\n\n**Key Implementation Details**\n\n* The `_backends` attribute specifies the supported backends, with `torch` being the primary one.\n* The `__init__` method requires the presence of the `torch` backend using the `requires_backends` function, ensuring compatibility.\n\n**Notable Dependencies or Requirements**\n\n* Related class: `DummyObject` (metaclass)\n* External dependency: `torch`\n\nNote: This summary focuses on the core functionality and key implementation details of the `CTRLModel` class, while omitting generic or redundant information."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2477-2481
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2481, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CTRLPreTrainedModel", "start_line": 2477, "type": "class", "chroma:document": "CODE:\n\nclass CTRLPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n `_CTRLPreTrainedModel` class:\n \n * **Purpose and Functionality:** A Pre-Trained Model controlled by the CTRL (Conditional Random Transformer) architecture.\n \n * **Key Implementation Details:**\n  - Utilizes the `torch` backend.\n \n\n * **Notable Dependencies or Requirements:**\n  - Requires the `requires_backends` function with `[\"torch\"]` input to ensure compatibility.\n"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2484-2488
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2488, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CvtForImageClassification", "start_line": 2484, "type": "class", "chroma:document": "CODE:\n\nclass CvtForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:** `CvtForImageClassification`\n\n**Main Purpose and Functionality:**\n`CvtForImageClassification` is a custom class for image classification tasks using the specified backend (`torch`). It handles initialization with required backends.\n\n**Key Implementation Details:**\n\n*   Required backends: `torch`\n*   Initialize class instance\n*   Ensures compatibility of backend usage\n\n**Notable Dependencies or Requirements:**\n\n*   `DummyObject` metaclass (not provided in the given code snippet, but assumed to be available)\n*   `requires_backends` function (not shown in the given code snippet, but referenced in the constructor)\n\nThe summary highlights the class's purpose, key implementation aspects, and dependencies. It is written in a concise manner to provide a clear understanding of the class's functionality to developers using it within the RAG system."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2491-2495
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2495, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CvtModel", "start_line": 2491, "type": "class", "chroma:document": "CODE:\n\nclass CvtModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`CvtModel` is a class that utilizes the `torch` backend. The constructor initializes with `*args` and `**kwargs`, and ensures `torch`_backend support using the `requires_backends` function.\n\n**Key Details:**\n\n- **Backend:** `torch`\n- **Constructor:** Initializes with variable arguments `*args` and keyword arguments `**kwargs`\n\n**Notable Dependencies:**\n\n- `torch`: PyTorch library\n- `DummyObject`, `requires_backends` (unclear without context)"}


