# 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: 2025-02-01 08:47:35
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim_no_trainer.py:375-799

metadata:
{"docstring": "", "end_line": 799, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim_no_trainer.py", "name": "main", "start_line": 375, "type": "function", "chroma:document": "This code is a template for a PyTorch-based reinforcement learning framework, likely designed to be used with the Hugging Face library for model fine-tuning and deployment. It consists of training loop that iterates over an evaluation dataset, calculating the loss and tracking metrics.\n\nHere's a concise summary:\n\n**Training Loop:**\n\n1. For each epoch:\n\t* Update the model parameters (`model.train()`).\n\t* Create a dataloader (`active_dataloader`) from the training data.\n\t* Iterate over the batches in the dataloader.\n\t\t+ Accumulate gradients using `torch.distributions`\n\t\t+ Update the model parameters with `optimizer.step()`\n\t\t+ Update the learning rate schedule with `lr_scheduler.step()`.\n2. After each epoch, evaluate the model on a validation dataset (`model.eval()`).\n3. Track metrics such as loss and training accuracy during evaluation.\n\n**Additional Components:**\n\n* Tracking evaluation metrics using Hugging Face's tracking system.\n* Saving model checkpoints at regular intervals (e.g., every `checkpointing_steps` epochs) for fine-tuning and deployment.\n\nOverall, this code provides a basic framework for training a PyTorch model on an RL or reinforcement learning task, with optional components for tracking performance and saving models."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py:169-171
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 171, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py", "name": "collate_fn", "start_line": 169, "type": "function", "chroma:document": "**Summary:**\n\n`collate_fn` is a function that collates input data (`examples`) into a standardized format. It stacks the \"pixel_values\" from each `example` and returns a dictionary with the collated values, where the key is `\"pixel_values\"`.\n\n**Key Implementation Details:**\n\n* Uses PyTorch's `torch.stack` to combine pixel values.\n* Outputs a dictionary containing a single key-value pair: `\"pixel_values\" -> stacked tensor of pixel values.\n\n**Note:**\n\nThis function appears to be part of a larger data processing pipeline, likely used in deep learning applications."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py:174-398
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 398, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py", "name": "main", "start_line": 174, "type": "function", "chroma:document": "This code is part of the Hugging Face Transformers library, implemented in Python. It provides a comprehensive framework for fine-tuning pre-trained models such as ViTMAEForPreTraining on various image classification tasks.\n\nHere's a concise summary:\n\n1.  **Initialization and Model Setup**: The script starts by importing required libraries and initializing a new instance of the model (ViTMAEForPreTraining) or loading an existing one.\n\n2.  **Data Preparation and Preprocessing**: It then sets up data preparation strategies for training and evaluation, including transformations to apply on the images in the dataset.\n\n3.  **Trainer Implementation**: A Trainer object is instantiated with the model, training arguments, and other relevant details. The trainer is responsible for orchestrating training and saving model checkpoints, metrics, and state information.\n\n4.  **Training and Evaluation**: If required by the training configuration (do\\_train setting), it trains the model until completion or a specified number of epochs. It also evaluates the trained model on a validation dataset.\n\n5.  **Logging and Hub Operations**: After training or evaluation is complete, the script logs specific metrics like model performance, saves key metrics to a file, and optionally pushes the fine-tuned model to Hugging Face's Model Hub for sharing.\n\nThe code is written in Python, utilizes the Transformers library by Hugging Face Inc., and is designed with modular, expandable functionality allowing users to easily implement their preferred data preparation strategies and training protocols."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py:401-403
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 403, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mae.py", "name": "_mp_fn", "start_line": 401, "type": "function", "chroma:document": "**Function Summary: `_mp_fn`**\n\n* **Purpose:** Invokes the `main()` function using xla_spawn (TPU) multi-processing.\n\n* **Key Implementation:** Calls external library (`xla_spawn`) to utilize TPUs for parallel execution.\n\n* **Notable Function Signature:** `_mp_fn(index)`\n\t+ `index`: Input parameter passed to the function, not explicitly used within the code snippet.\n \n* **External Dependencies:**\n\t+ `main()`\n\t+ `xla_spawn` (TPUs)\n     No explicit dependencies on internal classes/functions."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py:196-229
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 229, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py", "name": "MaskGenerator", "start_line": 196, "type": "class", "chroma:document": "**Class Summary: `MaskGenerator`**\n\n**Main Purpose and Functionality**\nThe `MaskGenerator` class generates 1D boolean masks for pretraining tasks. It creates a mask with shape `(model_patch_size**2,)` where values are either 0 or 1, indicating \"masked\" or not masked.\n\n**Key Implementation Details**\n\n* `__init__`: Initializes the generator with input size, mask patch size, model patch size, and mask ratio.\n\t+ Checks divisibility of input size by mask patch size and mask patch size by model patch size.\n\t+ Calculates token count and scaled mask count based on input size, mask patch size, and model patch size.\n* `__call__`: Generates a random mask with the specified shape.\n\t+ Creates a permutation of indices for masking tokens.\n\t+ Zero-pads and repeats the permutation to create the full mask.\n\n**Notable Dependencies and Requirements**\n\n* Imports:\n\t+ `torch` (for tensor creation)\n\t+ `np` (for NumPy arrays and functions)\n* Interfaces/Classes inheriting from this class: None\n* Inherited interfaces: None"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py:232-235
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 235, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py", "name": "collate_fn", "start_line": 232, "type": "function", "chroma:document": "**Function Summary**\n\nThe `collate_fn` function collates input data for training, combining all pixel values and mask tensors from multiple examples into a single batch.\n\n**Key Details:**\n\n* Combines `pixel_values` and `mask` tensor fields from each example using `torch.stack()`.\n* Returns a dictionary with the collated `pixel_values` as 'pixel_values' key and booleans indicating masked positions as 'bool_masked_pos' key.\n\n**Notable Dependencies/Requirements:**\n\n* `torch`: Required for tensor operations (e.g., `torch.stack()`)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py:238-478
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 478, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-pretraining/run_mim.py", "name": "main", "start_line": 238, "type": "function", "chroma:document": "Here is a concise summary of the provided code:\n\nThis is the implementation of the SimMIM model, which is a type of masked image modeling task. The script defines a dataset class for loading and preprocessing images, a data collator function to transform pairs of image patches into input tensors, and a `Trainer` class to perform training and evaluation.\n\nThe main pipeline of this code includes:\n\n1. Loading the dataset and preprocessing images using transforms.\n2. Creating a mask generator to create masks for masked image models.\n3. Defining a data collator function to transform batches of image patches into input tensors for the model.\n4. Training the model using a `Trainer` class, which handles model training, evaluation, saving, and loading checkpoints.\n\nThe script also includes support for fine-tuning pre-trained models and pushing the trained models to Hugging Face's model hub.\n\nOverall, this code provides a comprehensive implementation of masked image modeling, including data preprocessing, transformation, and training."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:59-217

metadata:
{"docstring": "", "end_line": 217, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "parse_args", "start_line": 59, "type": "function", "chroma:document": "**Function Summary**\n\nThe provided function `parse_args()` defines and parses command-line arguments for fine-tuning a pre-trained transformer model for instance segmentation using Hugging Face's transformers library.\n\n### Main Purpose and Functionality\n\nThe primary purpose of this function is to parse and return the command-line arguments configured by the user. It uses the `argparse` library to create an argument parser with various input parameters, such as model name or path, dataset name, image dimensions, batch sizes, learning rate, and schedule type.\n\n### Key Implementation Details\n\n* Notable algorithms and designs:\n\t+ The function uses a combination of predefined arguments from the transformers library, with some customizable options.\n\t+ The `SchedulerType` parameter is mapped to different learning rate schedulers.\n* Important method signatures and interfaces:\n\t+ None; the function relies on the argparse library for its structure.\n\n### Notable Dependencies or Requirements\n\n* The following external dependencies are required:\n\t+ `huggingface` library for accessing pre-trained models and transformers\n\t+ `argparse` library for parsing command-line arguments\n\t+ `os` library for creating directories\n\n### Summary Statistics\n\nThe function has 26 unique argument parameters, with default values provided for most of them. The parser performs basic sanity checks on the input configuration before returning the parsed arguments.\n\n```markdown\n# Import statements (none)\n\n# Function definition\ndef parse_args():\n    ...\n    \n    # Parsed command-line arguments\n    args = parser.parse_args()\n    ...\n\n# Parameters summary (26 unique parameters)\n- Model name/path:\n  --model_name_or_path\n- Dataset name:\n  --dataset_name\n- Image dimensions:\n  --image_height, --image_width\n- Batch sizes:\n  --per_device_train_batch_size, --per_device_eval_batch_size\n- Learning rate:\n  --learning_rate\n- Schedule type:\n  --lr_scheduler_type\n\n# External dependencies (huggingface, argparse, os)\n```"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:220-258

metadata:
{"docstring": "", "end_line": 258, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "augment_and_transform_batch", "start_line": 220, "type": "function", "chroma:document": "**Function Summary**\n\n`augment_and_transform_batch`: Transform medical images with associated masks and annotations, applying augmentations and processing transforms.\n\n**Key Functions/Classes:**\n\n* `A.Compose()`: Compose data augmentation transformations.\n* `AutoImageProcessor`.\n* `np.array()`: Convert PIL image to NumPy array.\n* `transform()`: Apply data augmentation transformation to input data.\n\n**Important Method Signatures:**\n\n* `image_processor(images, segmentation_maps, instance_id_to_semantic_id, return_tensors)`\n\n**Key Dependencies:**\n\n* `pandas` (implicit through `Mapping[str, Any]`)\n* `torch`\n* ` numpy`"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:261-268

metadata:
{"docstring": "", "end_line": 268, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "collate_fn", "start_line": 261, "type": "function", "chroma:document": "**Function Summary:**\n\n`collate_fn` is a function that collates and batches input data (`examples`) into a single dictionary. It takes three key fields from each example:\n\n- `pixel_values`\n- `class_labels`\n- `mask_labels`\n\nOptional depending on the presence of \"pixel_mask\" in the examples.\n\n**Key Implementation Details:**\n\n- Batches pixel values, class labels, and mask labels using PyTorch's `stack` function.\n- Returns a dictionary containing these batches with keys 'pixel_values', 'class_labels', 'mask_labels' and optionally 'pixel_mask'.\n\nNotable Interfaces/Dependencies:\n\n- PyTorch (`torch`)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:271-279

metadata:
{"docstring": "", "end_line": 279, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "nested_cpu", "start_line": 271, "type": "function", "chroma:document": "**Function Summary: `nested_cpu`**\n\nThe `nested_cpu` function recursively traverses the data structure passed to it, applying CPU operations on each element. It handles three types of inputs:\n\n1. **List/Tuple**: Recursively calls itself on each element and returns a new list/tuple with the results.\n2. **Dictionary (Mapping)**: Recursively calls itself on each value and returns a new dictionary with the original keys mapped to the results.\n3. **PyTorch Tensor**: Detach the tensor from the GPU (if any) and return it as a CPU tensor.\n\n**Key Implementation Details:**\n\n* Uses `isinstance` to check the type of input.\n* Returns the original input if it's not an instance of the expected types.\n* Uses list expression and dictionary comprehension for concise iteration over elements.\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch (`torch`) is a dependency."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:282-354

metadata:
{"docstring": "", "end_line": 354, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "evaluation_loop", "start_line": 282, "type": "function", "chroma:document": "**Evaluation Loop Function Summary**\n\n### Main Purpose\nThe `evaluation_loop` function evaluates the performance of a model using object detection metrics. It takes in various inputs, including a model, image processor, accelerator, dataloader, and id2label dictionary.\n\n```python\ndef evaluation_loop(model: Model, image_processor: ImageProcessor, accelerator: Accelerator,\n                    dataloader: DataLoader, id2label: dict):\n    ...\n```\n\n### Key Implementation Details\n\n* The function uses the `torch.no_grad()` context to disable gradient computation.\n* It applies model inference using the `model(**inputs)` syntax, where inputs are processed via the `accelerator`.\n* The `image_processor.post_process_instance_segmentation()` function is used to post-process output masks and labels.\n\nKey method signatures:\n\n* `model(**inputs)`\n* `image_processor.post_process_instance_segmentation(outputs, ...`\n\n### Notable Dependencies or Requirements\n\n* The following modules are used:\n\t+ `torch`\n\t+ `tqdm` (for progress bar)\n\t+ `matplotlib` (not explicitly mentioned, but assumed for plotting)\n\nInterfaces and classes:\n\n* `Accelerator`: provides methods for gathering data and tracking metrics\n* `Model`: provides the inference method\n\n### External Dependencies\n\n* The following external dependencies are used:\n\t+ `dataloader` module (for loading data)\n\t+ `id2label` dictionary (mapping class IDs to class names)\n\nNote: The summary preserves exact class and function names, provides a concise description of the main purpose, key implementation details, and notable dependencies or requirements."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:357-372

metadata:
{"docstring": "", "end_line": 372, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "setup_logging", "start_line": 357, "type": "function", "chroma:document": "**Summary:**\n\n### Functionality:\n\nThe `setup_logging` function configures the logging system for a deep learning model, setting up a basic logging format and handler. It also adapts to whether the code is running on a local machine or remote server, adjusting verbosity levels accordingly.\n\n### Main Purpose:\n\nPrimary purpose: To establish a standardized logging configuration.\n \n### Key Implementation Details:\n- Sets logging format and date format.\n- Configures stream handler for output on stdout.\n- Adjusts verbosity based on local/main process determination.\n\n### Notable Dependencies:\n`logging`, `sys`, `datasets.utils.logging`, `transformers.utils.logging`\n\nNote: accelerator interface is referenced by `Accelerator`."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:375-398

metadata:
{"docstring": "", "end_line": 398, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "handle_repository_creation", "start_line": 375, "type": "function", "chroma:document": "**Summary**\n\n`handle_repository_creation` function:\n---------------------------\n\n### Main Purpose and Functionality\n\nCreates a repository for the model and dataset based on user input. If `push_to_hub` is set, retrieves an existing repository or creates a new one; otherwise, creates a local directory.\n\n### Key Implementation Details\n\n* Retrieves repository name from `args.hub_model_id` or constructs it from `output_dir`.\n* Creates a new repository using Hugging Face API (`HfApi`) if `push_to_hub` is set.\n* If no repository exists, generates a `.gitignore` file with specific pattern matches.\n\n### Notable Dependencies and Requirements\n\n* `argparse.Namespace`: Argument parsing\n* `HuggingFaceAPI`: Repository creation service ( requires `hub_token`)\n* `Path`, `os.path.join`, `open`: File system utilities for handling directories and files."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py:401-739

metadata:
{"docstring": "", "end_line": 739, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py", "name": "main", "start_line": 401, "type": "function", "chroma:document": "**Summary: Training, Evaluation, and Model Saving Script for Object Detection Models**\n\nThis script is used to train, evaluate, and save object detection models using the Hugging Face Transformers library. The script includes the following main components:\n\n1. **Model Training**: The script initializes the model, tokenizer, and optimizer, then trains the model on the training dataset.\n2. **Evaluation Loop**: During each epoch, the script evaluates the model on a validation dataset to track metrics such as accuracy, precision, and recall.\n3. **Saving and Versioning**: The script saves the trained model at regular intervals (optional) or after all epochs are completed.\n\nThe script includes various parameters that can be adjusted to optimize training and evaluation, such as:\n\n* `output_dir`: the directory where the saved models will be stored\n* `push_to_hub`: a boolean flag indicating whether to upload the saved model to Hugging Face Hub\n* `repo_id` and `hub_token`: tokens for authenticating with Hugging Face Hub\n* `checkpointing_steps`: the frequency at which to save the model (integer, \"epoch\", or \"step\")\n\nThe script outputs various logging statements during training and evaluation, including metrics on the validation dataset. Additionally, it creates a final output directory containing the saved model and any necessary metadata files.\n\n**Example Use Case:**\n\nTo run this script, navigate to the directory containing the script and modify the `output_dir`, `push_to_hub`, and other parameters as needed. Then, execute the script using Python:\n```python\npython train.py --config_file config.json\n```\nReplace `train.py` with the actual filename of your script, and `config.json` with a configuration file that specifies any desired parameters."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:102-140

metadata:
{"docstring": "", "end_line": 140, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "augment_and_transform_batch", "start_line": 102, "type": "function", "chroma:document": "**Function Summary: `augment_and_transform_batch`**\n\nMain Purpose and Functionality:\nThis function takes in a mapping of images and annotations, applies augmentations using `A.Compose`, processes the output with an image processor (`AutoImageProcessor`), and returns a batched feature object containing pixel values, mask labels, and class labels.\n\nKey Implementation Details:\n\n* Applies augmentations to input images using `transform`.\n* Creates a mapping from instance IDs to semantic IDs based on augmented masks.\n* Processes the augmented images with the image processor (`model_inputs`).\n* Appends processed features to a batched feature object (`batch`).\n\nNotable Dependencies and Requirements:\n\n* `A.Compose` for augmentation\n* `AutoImageProcessor` for image processing\n* `Mapping[str, Any]` for input examples\n* `np.array` and `numpy` for image manipulation\n\nReturn Type: `BatchFeature`\n\nNote: The summary preserves exact class and function names, and focuses on precise technical terms."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:143-150

metadata:
{"docstring": "", "end_line": 150, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "collate_fn", "start_line": 143, "type": "function", "chroma:document": "**Collate Function Summary:**\n\nThe `collate_fn` function collates and preprocesses input data for model training. Its primary purpose is to group a list of dictionaries (`examples`) into batches, aggregating the corresponding values.\n\n**Key Implementation Details:**\n\n- Aggregates pixel values, class labels, and mask labels from individual examples using torch.stack.\n  \n**Notable Dependencies or Requirements:**\n\n- `torch`: used for stack operation on tensor data."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:159-167

metadata:
{"docstring": "", "end_line": 167, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "nested_cpu", "start_line": 159, "type": "function", "chroma:document": "**Function Summary:**\n### `nested_cpu`\n\n#### Description\nRecursively traverses the input tensors data structure, transferring data to CPU and detaching it from GPU if present.\n\n#### Implementation Details\n\n*   Returns recursively transformed data structure (list/tuple or dict) with nested tensor elements converted to CPU.\n*   Applies transformation individually to torch.Tensor element using `cpu().detach()` method.\n*   Handles primitive tensors and unknown/unsupported types by returning the input unchanged.\n\n**Key Methods and Interfaces:**\n\n*   `type(tensors)(...)`: Creates a new data structure type wrapping recursively transformed elements.\n*   `tensors.cpu().detach()`: Converts torch.Tensor to CPU, detaching it from GPU."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:170-296

metadata:
{"docstring": "", "end_line": 296, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "Evaluator", "start_line": 170, "type": "class", "chroma:document": "**Evaluator Class Summary**\n\nThe `Evaluator` class computes metrics for instance segmentation tasks. Its primary purpose is to process prediction and target batches, update metrics, and return computed values.\n\n**Key Implementation Details:**\n\n1.  **Post-processing**: The `postprocess_target_batch` method collects targets in a standardized format, while the `postprocess_prediction_batch` method post-processes predictions based on a given threshold.\n2.  **Metric Computation**: The `get_metric` method initializes the metric object, and the `__call__` method updates metrics with evaluation results using the `(metric.update()` method).\n3.  **Metric Aggregation**: Metrics are aggregated and transformed into a comprehensive dictionary containing class-specific values.\n\n**Notable Dependencies and Requirements:**\n\n1.  `ModelOutput`\n2.  `ImageProcessor` (from `AutoImageProcessor`)\n3.  `MeanAveragePrecision`\n4.  `torch` library\n\n    The dependency information indicates that the Evaluator uses specific models, image processors, and metrics to compute instance segmentation tasks.\n\n**Summary Metrics:**\n\n*   Instantiation: Class instantiation with necessary dependencies.\n*   Processing: Data transformation in both prediction and target batches.\n*   Evaluation: Computation of instance segmentation metrics.\n*   Results: Dictionary containing computed metric values."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:299-316

metadata:
{"docstring": "", "end_line": 316, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "setup_logging", "start_line": 299, "type": "function", "chroma:document": "**Function Summary: `setup_logging`**\n\n**Purpose and Functionality:**\nSets up logging configuration according to the provided `TrainingArguments`.\n\n**Implementation Details:**\n\n* Configures logging handlers with basic format string and date/time format\n* Sets verbosity level based on specified training arguments\n\t+ Enables default log level (info) if `should_log` is enabled\n\t+ Sets process-specific log level from training arguments\n* Enables explicit format and all default log handlers\n\n**Dependencies and Requirements:**\n\n* Imports `sys`, `logging`, and `transformers.utils.logging`\n* Uses external dependencies:\n\t+ `transformers`: specific logging utilities (set_verbosity, enable_default_handler, etc.)\n\t+ `logging`: basic logger functionality\n\t+ `sys`: access to system stdout"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:319-338

metadata:
{"docstring": "", "end_line": 338, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "find_last_checkpoint", "start_line": 319, "type": "function", "chroma:document": "**Summary**\n\nThis is the `find_last_checkpoint` function, which finds and returns the last completed training check-point in the specified output directory based on provided parameters.\n\n**Key Implementation Details**\n\n- Checks for existing check-point by path (`training_args.resume_from_checkpoint`) or using `get_last_checkpoint`\n- Verifies output directory existence and emptiness before raising an error\n- Logs detection of a check-point when it exists but `--resume_from_checkpoint` is not specified\n\n**Dependencies and Requirements**\n\n- `TrainingArguments` object from the Hugging Face Transformers library\n- External dependency on `os.path.isdir`, `os.listdir`, and `logger`\n- Internal function call to `get_last_checkpoint`"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py:341-475

metadata:
{"docstring": "", "end_line": 475, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/instance-segmentation/run_instance_segmentation.py", "name": "main", "start_line": 341, "type": "function", "chroma:document": "**Main Functionality**\n\nThis is the main entry point of an instance segmentation model training script. It parses command-line arguments, sets up logging and telemetry, loads pre-trained model, dataset, image processor, and augmentation pipeline. The script then uses the Hugging Face Trainer API to train and evaluate the model.\n\n**Key Implementation Details**\n\n* Uses Hugging Face Transformers library for transforming arguments\n* Utilizes AutoModelForUniversalSegmentation and AutoImageProcessor for loading pre-trained model and image processor\n* Defines custom dataset transforms using A.Compose and A.Partial for horizontal flipping, random brightness contrast, and color jittering\n* Implements custom Trainer API configuration with Evaluator compute metrics\n\n**Notable Dependencies or Requirements**\n\n* Hugging Face Transformers library (`transformers`)\n* AutoModelForUniversalSegmentation and AutoImageProcessor from Hugging Face Transformers (`~ transformers.models.universal_segmentation`)\n* A.Compose and A.Partial from Hugging Face Transformers (`~transformers.pipeline`)\n* Evaluator compute metrics plugin (`@transformers.plugins.trainer.evaluators`)\n* Trainer API plugin (`@transformers.trainers`)\n* `json` library for parsing command-line arguments\n\n**Class/Function/Variable Names**\n\n* `Arguments` and `TrainingArguments` from Hugging Face Transformers\n* `parser`, `args`, and `training_args`\n* `model`, `image_processor`, `dataset`, and `trainer`\n* `compute_metrics` using the Evaluator plugin\n* `collate_fn` for dataset collator\n\n**Interfaces**\n\n* Implements Trainer API configuration with custom plugins for model training and evaluation\n* Inheriting from `Trainer` API trainer class"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:62-94

metadata:
{"docstring": "# Copied from examples/pytorch/object-detection/run_object_detection.format_image_annotations_as_coco", "end_line": 94, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "format_image_annotations_as_coco", "start_line": 62, "type": "function", "chroma:document": "**Concise Summary**\n\n### Function Purpose\n\nThe `format_image_annotations_as_coco` function formats image annotations to the COCO (Object Detection Dataset Standard) format. It takes an image ID, category labels, areas, and bounding box coordinates as input and returns a dictionary with formatted annotations.\n\n### Key Implementation Details\n\n* Formats annotation data from different lists (`categories`, `areas`, `bboxes`) into a single cohesive list.\n* Utilizes the COCO format's expected fields: `image_id`, `category_id`, `area`, `bbox`, and `iscrowd`.\n* Converts bounding box coordinates to absolute COCO format.\n\n### Dependencies/Requirements\n\n* No external dependencies; relies on standard Python data types (str, int, float, list, tuple)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:98-117

metadata:
{"docstring": "# Copied from examples/pytorch/object-detection/run_object_detection.convert_bbox_yolo_to_pascal", "end_line": 117, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "convert_bbox_yolo_to_pascal", "start_line": 98, "type": "function", "chroma:document": "**Summary**\n\nConverts bounding boxes from YOLO format (x_center, y_center, width, height) to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.\n\n**Key Implementation Details:**\n\n* Converts center coordinates to corner coordinates using `center_to_corners_format()`\n* Scales box coordinates by image size (width and height)\n\n**Noteability:**\n\n* This function requires input bounding boxes in YOLO format (`boxes` argument)\n* Returns output bounding boxes in Pascal VOC format (`torch.Tensor`)\n\n**External Dependencies:**\n\n* None\n\n**Implementation Choose:** `center_to_corners_format()` is not shown, please provide the source implementation if you want to add it in this summary."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:121-150

metadata:
{"docstring": "# Copied from examples/pytorch/object-detection/run_object_detection.augment_and_transform_batch", "end_line": 150, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "augment_and_transform_batch", "start_line": 121, "type": "function", "chroma:document": "**Concise Summary**\n\n`augment_and_transform_batch` function:\nApplies augmentations, formats annotations in COCO format, and applies image processor transformations to input batch of object detection examples.\n\n**Key Implementation Details**\n\n* Uses `A.Compose` for applying augmentations\n* Formats annotations using `format_image_annotations_as_coco`\n* Applies `AutoImageProcessor` transformations (resizing, rescaling, normalization)\n* Returns transformed batch with optional pixel mask removal\n\n**Notable Dependencies or Requirements**\n\n* `transform`: `pytorchvision.transforms.A.Compose`\n* `image_processor`: `pytautocr.image_processor.AutoImageProcessor`\n* `format_image_annotations_as_coco` (not shown in code snippet)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:154-160

metadata:
{"docstring": "# Copied from examples/pytorch/object-detection/run_object_detection.collate_fn", "end_line": 160, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "collate_fn", "start_line": 154, "type": "function", "chroma:document": "**Function Summary**\n\n`collate_fn`: A function that collates and normalizes input batches for object detection tasks using PyTorch.\n\n**Key Functionality**\n\n* Takes a `batch` of `BatchFeature` objects as input\n* Returns a dictionary containing:\n\t+ Pixel values: stacked horizontally ( torch.Tensor )\n\t+ Labels: concatenated into a list ( List[Any] )\n\t+ Optional pixel mask: if present, stacked horizontally ( torch.Tensor )\n\n**Implementation Details**\n\n* Uses PyTorch tensor operations for efficient computation.\n* Utilizes list comprehension and dictionary creation for concise data processing.\n\n**Dependencies**\n\n* `torch` (tensor operations)\n* `batch` and `BatchFeature` objects from `pytorch` or custom library."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:163-173

metadata:
{"docstring": "", "end_line": 173, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "nested_to_cpu", "start_line": 163, "type": "function", "chroma:document": "**Function Summary: `nested_to_cpu`**\n\nThe `nested_to_cpu` function recursively moves nested tensors in objects to the CPU if they are currently on the GPU.\n\n**Main Purpose and Functionality:**\nThis function handles nested data structures of various types (`torch.Tensor`, `Mapping`, `list`, `tuple`, `np.ndarray`, `str`, `int`, `float`, `bool`) by moving any tensor-like elements that are on the GPU to the CPU, preserving their values.\n\n**Implementation Details:**\n\n*   Handles recursive handling of nested data structures using type checks (`isinstance`)\n*   Uses type-specific methods for conversion and return:\n    *   For torch.Tensor: copies the tensor to CPU using `.cpu()`\n    *   For Mapping (dict): recursively calls `nested_to_cpu` on values\n    *   For list/tuple: recursively calls `nested_to_cpu` on elements\n    *   For other scalar types, returns unchanged\n\n**Dependencies and Requirements:**\n*   Uses the PyTorch library (`torch`)\n*   Assumes presence of NumPy (`np`; not explicitly used in this snippet)\n\nNote that this summary preserves class/function/variable names exactly as per your specified requirements."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:176-224

metadata:
{"docstring": "", "end_line": 224, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "evaluation_loop", "start_line": 176, "type": "function", "chroma:document": "**Function Summary**\n\n`evaluation_loop` function evaluates the performance of an object detection model using metrics such as mean average precision (MAP), map per class, and maximum area retrieved at 100th percentile. The function takes in the following inputs:\n\n* `model`: the object detection model to be evaluated\n* `image_processor`: an AutoImageProcessor instance for converting between YOLO and Pascal VOC formats\n* `accelerator`: an Accelerator instance for parallelization\n* `dataloader`: a DataLoader instance containing training data\n* `id2label`: a dictionary mapping class IDs to their corresponding names\n\nThe function performs the following steps:\n\n1. Evaluates the model on the training dataset.\n2. Collects predicted boxes, classes, and scores from the model outputs.\n3. Converts YOLO format to Pascal VOC format for ground truth boxes.\n4. Updates the MeanAveragePrecision metric with predicted and ground truth data.\n5. Computes MAP per class and maximum area retrieved at 100th percentile.\n6. Rounds MAP values to four decimal places.\n\n**Key Implementation Details**\n\n* Uses `torch.nn.Module` as input model\n* Employs `AutoImageProcessor` for converting between YOLO and Pascal VOC formats\n* Utilizes `tqdm` for progress bar\n* Implements `with torch.no_grad():` block for zero gradient computation\n\n**Notable Dependencies or Requirements**\n\n* PyTorch, AutoImageProcessor, Accelerator, DataLoader, and `torchvision.transforms`\n* `torchviz` (not explicitly mentioned but implied through usage of `tqdm`)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:227-389

metadata:
{"docstring": "", "end_line": 389, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "parse_args", "start_line": 227, "type": "function", "chroma:document": "**Summary: Command Line Argument Parser**\n\nThis function defines a command line argument parser for training and fine-tuning object detection models using Hugging Face's Transformers library.\n\n**Main Purpose:**\nThe parser is used to gather configuration parameters for the model, dataset, and training process.\n\n**Key Implementation Details:**\n\n* Supports various hyperparameters for model architecture (e.g., `model_name_or_path`, `dataset_name`)\n* Handles training data preparation (e.g., `train_val_split`, `ignore_mismatched_sizes`)\n* Configures batch sizes, learning rates, and optimizer parameters (e.g., `per_device_train_batch_size`, `learning_rate`)\n* Offers options for checkpointing, experimentation, and tracking (e.g., `checkpointing_steps`, `report_to`)\n\n**Notable Dependencies:**\n\n* `argparse`: a built-in Python library for parsing command-line arguments\n* `huggingface transformers`: the library used for object detection and fine-tuning models\n\n**External Requirements:**\nNone mentioned in this code snippet, assume basic Python environment."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py:392-777

metadata:
{"docstring": "", "end_line": 777, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection_no_trainer.py", "name": "main", "start_line": 392, "type": "function", "chroma:document": "This code is a training loop for a deep learning model, specifically an object detection model. The code uses the Hugging Face Transformers library and its integration with PyTorch. It trains the model on a specified dataset using a custom evaluation metric.\n\nThe main components of this code are:\n\n1.  **Model Training Loop**: This section contains the core training loop where the model is trained on a given batch of data.\n2.  **Evaluation Metric**: The `evaluation_loop` function calculates the evaluation metrics for both training and test datasets.\n3.  **Checkpointing**: The model saves its state at regular intervals during training, which helps in resumes from anywhere in the training process.\n\nThe benefits of this code include:\n\n*   Scalability: This code can be used to train large models on various machine learning tasks.\n*   Flexibility: The use of PyTorch and Hugging Face Transformers makes it easy to adapt the model to different datasets and tasks.\n*   Tracking Progress: The `with_tracking` flag helps track progress during training, and it will save the final results after every full epoch.\n\nHowever, there are areas for improvement:\n\n*   **Code Duplication**: Some parts of the code can be reused or extracted into separate functions for better readability.\n*   **Variable Length Loops**: Some loops in the code are not using all elements from a list (e.g., `for i, batch in enumerate(active_dataloader) if resume_step > 0`), leading to unnecessary iterations over zero-valued data. Instead, these conditions can be handled outside the loop or inside using an `if-else` statement.\n*   **Magic Numbers**: There are magic numbers scattered throughout the code (e.g., `args.max_train_steps`, `/len(train_dataloader)` etc.). It would be better to replace them with named constants for clarity and maintainability.\n\nOverall, this is a solid piece of code that effectively trains an object detection model using Hugging Face Transformers. However, refactoring and improving readability can lead to an even more efficient and well-structured version."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py:61-93

metadata:
{"docstring": "", "end_line": 93, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py", "name": "format_image_annotations_as_coco", "start_line": 61, "type": "function", "chroma:document": "**Summary:**\n\n### `format_image_annotations_as_coco` function:\n\n* Formats a set of image annotations to the COCO format, accepting `image_id`, categories, areas, and bounding box coordinates.\n\n#### Primary Purpose:\nConverts annotated image data into the COCO (Visual Object Shapes) format.\n\n#### Key Implementation Details:\n\n* Iterates over `categories`, `areas`, and `bboxes` in parallel using `zip`.\n* Formats each annotation as a dictionary with required fields (`image_id`, `category_id`, `iscrowd`, `area`, `bbox`) according to the COCO format.\n* Appends formatted annotations to the `annotations` list.\n\n#### Dependencies:\n\n* None\n\n#### Notable Method Signatures and Interfaces:\n- None\n\n#### External Dependencies:\n- No external dependencies listed.\n\n### Returned Data Structure: \n\n* A dictionary with two keys:\n  - `\"image_id\"` (str): image id.\n  - `\"annotations\"` (list of dict): list of formatted annotations in COCO format."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py:96-115

metadata:
{"docstring": "", "end_line": 115, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py", "name": "convert_bbox_yolo_to_pascal", "start_line": 96, "type": "function", "chroma:document": "**Summary:**\n\n```python\n# Function: convert_bbox_yolo_to_pascal\n# Purpose: Convert YOLO format bounding boxes to Pascal VOC format in absolute coordinates.\n\ndef convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:\n    # Key function:\n    #   1. `center_to_corners_format`: converts center coordinates to corner form\n    #   2. Multiply by tensor containing image size values (width, height) to convert relative coordinates to absolute\n\n    # Parameters:\n    #   boxes: Bounding boxes in YOLO format (torch.Tensor)\n    #   image_size: Image size in format (height, width)\n\n    # Returns:\n    #   torch.Tensor: Converted bounding boxes in Pascal VOC format (x_min, y_min, x_max, y_max)\n```\n\n**Key Implementation Details:**\n\n* Uses `center_to_corners_format` to convert center coordinates to corner form\n* Multiplies by a tensor containing image size values to convert relative coordinates to absolute\n* Inverts box coordinates due to the YOLO format using multiplication with a tensor of width and height"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py:118-147

metadata:
{"docstring": "", "end_line": 147, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py", "name": "augment_and_transform_batch", "start_line": 118, "type": "function", "chroma:document": "**Function Summary**\n\n`augment_and_transform_batch`\n\nApplies augmentations and formats annotations in COCO format for object detection tasks.\n\n* **Purpose**: Transforms input data by applying augmentations, formatting annotations, and processing images through an image processor.\n\n**Key Implementation Details**\n\n* Applies augmentations using `A.Compose`\n* Formats annotations in COCO format using `format_image_annotations_as_coco`\n* Processes images with image processor transformations (resizing, rescaling, normalization) using `AutoImageProcessor`\n\n**Notable Dependencies**\n\n* `A.Compose` for augmentation\n* `format_image_annotations_as_coco` for annotation formatting\n* `AutoImageProcessor` for image processing\n* COCO format annotations\n\n**Method Signatures and Interfaces**\n\n* `transform`: Takes `image`, `bboxes`, and `category` as inputs and returns a transformed output\n\n\nThis summary captures the primary purpose, key implementation details, and notable dependencies of the function while preserving exact class/function/variable names."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py:150-156

metadata:
{"docstring": "", "end_line": 156, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py", "name": "collate_fn", "start_line": 150, "type": "function", "chroma:document": "**Summary**\n\nA custom collate function (`collate_fn`) that aggregates batches of `BatchFeature` objects into a single dictionary. It combines pixel values, labels, and optional pixel masks using PyTorch stacks.\n\n* **Primary purpose**: Aggregate batch data into a format suitable for training models.\n* **Key implementation details**:\n\t+ Stacks tensor-valued fields (`pixel_values`, `labels`) using PyTorch's `stack` function.\n\t+ Optionally stacks pixel mask tensors if present in the first batch element.\n* **Notable dependencies**: None.\n* **External dependencies**: None.\n\n**Detailed summary (more concise)**\n\n`collate_fn(batch: List[BatchFeature]) -> Mapping[str, Union[torch.Tensor, List[Any]]]`\n\nCombine batch data into a single dictionary with stacked tensors and lists."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py:323-518

metadata:
{"docstring": "", "end_line": 518, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/object-detection/run_object_detection.py", "name": "main", "start_line": 323, "type": "function", "chroma:document": "**Main Purpose and Functionality**\n\nThis function, `main()`, initializes the training pipeline for an object detection model using the Hugging Face Transformers library. It loads pre-trained configurations, models, and image processors, defines input data preprocessing transformations, and trains the model with the Trainer API.\n\n### Key Implementation Details\n\n1. **Data Preprocessing**:\n\t* Defines image augmentations and dataset transforms using `A.Compose` and `A.OneOf`.\n\t* Applies these transformations to training, validation, and test datasets.\n2. **Model Training**:\n\t* Initializes a Trainer object with the trained model, arguments, and datasets.\n\t* Trains the model for a specified number of epochs (if `do_train` is True).\n3. **Evaluation**:\n\t* Evaluates the model on the test dataset using the Trainer API.\n\n### Key Classes and Functions\n\n1. `Trainer`: Initializes and trains the model with pre-requisites, including data collators, metrics functions, and checkpoints.\n2. `AutoConfig`, `AutoModelForObjectDetection`, `AutoImageProcessor`: Load pre-trained configurations, models, and image processors from the Hugging Face model hub.\n3. `A.Compose` and `A.OneOf`: Define custom data augmentation and transformation pipelines.\n\n### Summary of Model Training Process\n\n1. Initialize Trainer object with pre-requisite datasets and arguments.\n2. Define data transformations for training, validation, and test datasets.\n3. Apply these transformations to the dataset.\n4. Train the model using the Trainer API and specified number of epochs (if `do_train` is True).\n5. Evaluate the model on the test dataset.\n\n### Key Variables\n\n1. `config`: Pre-trained configuration object from Hugging Face model hub.\n2. `model`: Pre-trained object detection model loaded with the configuration object.\n3. `image_processor`: Pre-trained image processor used for data augmentation and transformation.\n4. `training_args`: Training arguments, including number of epochs, evaluation metrics, and checkpoint saving.\n\n### Conclusion\n\nThis function provides an end-to-end training pipeline for object detection models using Hugging Face Transformers library. The model training process is efficient, scalable, and easy to maintain due to the powerful features provided by the library."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py:51-284

metadata:
{"docstring": "", "end_line": 284, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py", "name": "parse_args", "start_line": 51, "type": "function", "chroma:document": "**Summary**\n\nThis function defines arguments for fine-tuning a transformer model on a text classification task. The code utilizes the Hugging Face library's `argparse` to create a command-line interface (CLI) for input parameters.\n\n**Primary Purpose**\nFine-tune a pre-trained transformer model on a text classification task based on user-provided dataset and model configuration inputs.\n\n**Implementation Details**\n\n*   Command-line arguments are used to configure various aspects of the fine-tuning process, such as dataset name, model architecture, hyperparameters (e.g., learning rate, batch size), and more.\n*   The code creates a reproducible environment by setting the random seed and ensuring that the model's output directory exists for saving and pushing updates to the Model Hub.\n\n**Notable Parameters**\n\n*   `output_dir`: Path where the fine-tuned model and other artifacts will be stored.\n*   `seed`: Random seed used for reproducibility in training.\n*   `learning_rate`, `batch_size`, `gradient_accumulation_steps`: Hyperparameters controlling the optimization process.\n*   `lr_scheduler_type` : Type of learning rate scheduler applied to optimize the model's performance.\n\n**Choice of `SchedulerType`**: The available options are linear, cosine, cosine_with_restarts, polynomial, constant, and constant_with_warmup. The user can choose one of these types based on their specific needs for fine-tuning.\n\n**Gumbel Temperature Parameters**\n\n*   `max_gumbel_temperature`: Maximum temperature value applied in the contrastive task.\n*   `min_gumbel_temperature`: Minimum temperature value applied in the contrastive task.\n*   `gumbel_temperature_decay` : Decay rate of gumbel temperature during training.\n\n**Audio Features**\n\n*   `mask_time_prob`, `mask_time_length`: Parameters controlling the masking process for audio features using Gumbel softmax."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py:376-382

metadata:
{"docstring": "", "end_line": 382, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py", "name": "multiply_grads", "start_line": 376, "type": "function", "chroma:document": "**Summary:**\n\n`multiply_grads` function multiplies gradients by a constant `c`. It iterates over `params`, checking for non-null gradients. If `c` is a tensor, it's moved to the device of each parameter's gradient, and then multiplies the gradient data with `c`.\n\nKey implementation details:\n\n* Iterates over parameters using `for p in params:`\n* Checks if each parameter has a valid gradient (`p.grad is not None`)\n* Moves constant `c` to the same device as each parameter's gradient using `to(p grad.device)`\n* Multiplies each gradient data with `c` using `mul_`\n\nNotable dependencies:\n\n* External dependency: `torch` with specific methods (`is_tensor`, `mul_`)\n\n**Note:** The function assumes that `params` is a collection of PyTorch parameters and `c` is a scalar or tensor value."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py:385-393

metadata:
{"docstring": "", "end_line": 393, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py", "name": "get_grad_norm", "start_line": 385, "type": "function", "chroma:document": "**Summary**\n\n`get_grad_norm` function calculates the L2 gradient norm (Euclidean distance) of all model parameters `params`, scaled by `scale`. It computes the squared magnitude of each parameter's gradient and sums them up, then takes the square root.\n\n**Key Points:**\n\n* Computation:\n\t+ Iterates over model parameters (`p` in params)\n\t+ Checks if parameter has a computed gradient (`if p.grad is not None:`)\n\t+ Calculates the squared L2 norm of the gradient (`(param_grad / scale).norm(2)`)\n\t+ Accumulates sum of squared norms (`total_norm += param_norm.item() ** 2`)\n* Scaling and normalization:\n\t+ Scales gradients by `scale` (default=1)\n\t+ Applies `data` attribute to detach gradient from computation graph\n* Return value:\n\t+ Final total L2 norm computed as square root of accumulated sum"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py:396-797

metadata:
{"docstring": "", "end_line": 797, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py", "name": "main", "start_line": 396, "type": "function", "chroma:document": "Here's a concise summary:\n\nThis code trains a model for contrastive learning and diversity loss using PyTorch. It uses multi-processing to parallelize training and validation tasks. The training process involves:\n\n1. Initialization of the model, optimizer, and scheduler\n2. Training loop:\n\t* Forward pass: calculate losses (contrastive loss, diversity loss)\n\t* Backward pass: update parameters\n3. Logging losses and metrics at specified intervals\n4. Saving the model periodically\n5. Validation and logging results\n\nThe code uses various techniques such as:\n\n1. Multi-processing to parallelize training and validation tasks\n2. Contrastive learning for self-supervised learning\n3. Diversity loss for generating new features\n4. Gradient clipping and weight decay for regularization\n5. Learning rate scheduling using a scheduler\n\nOverall, this code is designed to train a contrastive learning model with diversity, which can be used for various applications such as image classification, object detection, and retrieval tasks."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation_no_trainer.py:70-311

metadata:
{"docstring": "# Parsing input arguments", "end_line": 311, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation_no_trainer.py", "name": "parse_args", "start_line": 70, "type": "function", "chroma:document": "The provided code chunk is for parsing command-line arguments in Python using the `argparse` library. It defines a function called `parse_args()` that returns an object containing all the parsed arguments.\n\nHere's a breakdown of what this code does:\n\n1.  **Creating a parser**: The script starts by creating a new `ArgumentParser` named `parser`.\n\n2.  **Defining arguments**: It then adds various types of arguments to the parser using methods like `add_argument()` and `parser.add_argument_type()`. These include string, integer, float, boolean, and custom data types for scheduler and model type.\n\n3.  **Parsing arguments**: After defining all the possible arguments, the script uses the `parse_args()` method to parse these arguments from the command line and store them in a variable named `args`.\n\n4.  **Sanity checks**: It performs several sanity checks on the parsed arguments, including:\n    *   Raised an error if `dataset_name`, `train_file`, or `validation_file` is not provided.\n    *   Ensured that the file extensions of `train_file` and `validation_file` are either `.csv` or `.json`.\n    *   Raised an assertion if `output_dir` is not provided when `--push_to_hub` is used.\n\n5.  **Returning parsed arguments**: Finally, the function returns the object containing all the parsed arguments (`args`).\n\n**Example Usage**\n\nTo use this function to parse command-line arguments, you can call it like so:\n\n```python\nimport argparse\n\ndef main():\n    # Parse arguments\n    args = parser.parse_args()\n\n    # Access parsed arguments as a dictionary.\n    output_dir = args.output_dir\n    num_train_epochs = args.num_train_epochs\n    learning_rate = args.learning_rate\n    weight_decay = args.weight_decay\n\n# ... (rest of your code)\n\nif __name__ == \"__main__\":\n    main()\n```\n\nYou can customize the command-line interface by passing specific arguments during the script execution, such as:\n\n```python \n--output_dir models/saved_models --num_train_epochs 3 --learning_rate 5e-5 --weight_decay 0.01\n```\n\nThe output directory, number of training epochs, learning rate, and weight decay are all provided during script execution using these command-line arguments."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation_no_trainer.py:314-785

metadata:
{"docstring": "", "end_line": 785, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation_no_trainer.py", "name": "main", "start_line": 314, "type": "function", "chroma:document": "This code is a training script for a machine learning model, specifically a transformer-based language model. The script uses the Hugging Face Transformers library and the PyTorch framework.\n\nKey features of the script include:\n\n1. **Data loading**: The script loads data from a dataset (not specified in the provided code snippet) using Hugging Face's `DataLoader`.\n2. **Model training**: The script trains the model using PyTorch's `Trainer` API, with support for automatic mixed precision training.\n3. **Evaluation metrics**: The script uses the BLEU score as an evaluation metric to measure the model's performance on a validation set.\n4. **Checkpointing and saving**: The script saves model checkpoints at regular intervals during training using Hugging Face's `Trainer` API.\n\nThe script also includes features for:\n\n* **Multiprocessing**: The script utilizes PyTorch's multi-GPU support with multiple workers to speed up training times.\n* **Hyperparameter tuning**: Although not explicitly implemented in this code snippet, the `Trainer` API allows for hyperparameter tuning through the use of a `TrainerCallback` class.\n\nOverall, this script appears to be designed for training a transformer-based language model on a specific dataset."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation.py:260-687

metadata:
{"docstring": "", "end_line": 687, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation.py", "name": "main", "start_line": 260, "type": "function", "chroma:document": "This is a code for training a translation model using the Hugging Face Transformers library. The code defines a sequence-to-sequence model, sets up the data processing pipeline, and trains the model on a provided dataset.\n\n**Key Features:**\n\n1. The code uses the `Seq2SeqTrainer` class from the transformers library to train the model.\n2. It sets up the data processing pipeline using the `tokenizer` and `processing_class`.\n3. The model is trained on the `train_dataset` with a maximum length of `max_length` and `num_beams`.\n4. Evaluation metrics (Bleu score, generation length) are computed during the training process.\n5. The code also includes options for pushing the model to Hugging Face Hub.\n\n**Key Steps:**\n\n1. Load pre-trained tokenizer\n2. Define data processing pipeline using `tokenizer` and `processing_class`\n3. Split dataset into training, validation, and prediction sets\n4. Train the model using `Seq2SeqTrainer`\n5. Evaluate the model on validation and prediction datasets\n6. Save the model and evaluate its performance using Bleu score"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation.py:690-692

metadata:
{"docstring": "", "end_line": 692, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/translation/run_translation.py", "name": "_mp_fn", "start_line": 690, "type": "function", "chroma:document": "**Summary:**\n\nThis Python function `_mp_fn` appears to be a part of a function scheduling system, possibly for parallelization or job management. It takes an `index` parameter and:\n- Calls the `main` function when executed by XLA Spawn (a TPU execution engine).\n- Primary purpose: Schedule main execution on TPUs.\n\n**Key Implementation Details:**\n\n* **Function Signature:** `_mp_fn(index)`\n* **Implementation Note:** Calls `main()` as part of task scheduling.\n\n**Dependencies and Requirements:**\n\n* **External Dependencies:**\n\t+ XLA Spawn (TPU execution engine)\n* **Inherited/Implemented Interfaces:** None mentioned"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/audio-classification/run_audio_classification.py:52-58

metadata:
{"docstring": "", "end_line": 58, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/audio-classification/run_audio_classification.py", "name": "random_subsample", "start_line": 52, "type": "function", "chroma:document": "**Function Summary: `random_subsample`**\n\n* **Purpose:** Randomly selects chunks of audio data from an input WAV file, limited to a specified maximum length.\n* **Functionality:** Samples up to `max_length` seconds at the specified sample rate (default: 16,000 Hz).\n* **Implementation Details:**\n\t+ Sampling is done using a random offset within the file bounds.\n\t+ The selected chunk size is determined by rounding the product of sample rate and `max_length`.\n* **Params:**\n\t+ `wav`: Input audio data (`np.ndarray`)\n\t+ `max_length`: Max chunk length in seconds\n\t+ `sample_rate` (optional): Audio sampling rate, defaults to 16,000 Hz.\n\n**Key Points:**\n\n* Uses `numpy` for array manipulation\n* Incorporates randomization using `randint` for offset selection"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/audio-classification/run_audio_classification.py:197-428

metadata:
{"docstring": "", "end_line": 428, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/audio-classification/run_audio_classification.py", "name": "main", "start_line": 197, "type": "function", "chroma:document": "**Audio Classification Model Training with Transformers**\n==========================================================\n\nThis script trains an audio classification model using the Hugging Face Transformers library. The model is configured for fine-tuning on a custom dataset, with options to train and evaluate the model.\n\n**Dataset Configuration**\n------------------------\n\nThe dataset consists of:\n\n* `train` dataset: A subset of size `data_args.max_train_samples` from the full training set.\n* `eval` dataset: A subset of size `data_args.max_eval_samples` from the full evaluation set.\n* The audio feature extractor is created using the `feature_extractor` processing class.\n\n**Model Configuration**\n----------------------\n\nThe model is a pre-trained `AudioModelForSequenceClassification`. The configuration is updated to match the number of labels in the dataset, with custom label mappings generated from the dataset's label names.\n\n**Training and Evaluation**\n---------------------------\n\nThe training process involves:\n\n* Computing metrics using the `compute_metrics` function.\n* Saving the model, logs, and state.\n* Training the model using the `Trainer` class.\n* Evaluating the model using the `evaluate` method.\n\n**Key Options**\n--------------\n\n* `data_args.max_train_samples`: Maximum number of samples to use for training.\n* `data_args.max_eval_samples`: Maximum number of samples to use for evaluation.\n* `training_args.do_train`: Flag to indicate whether to train the model or not.\n* `training_args.do_eval`: Flag to indicate whether to evaluate the model or not.\n* `training_args.resume_from_checkpoint`: Path to a checkpoint to resume training.\n\n**Example Use Cases**\n---------------------\n\nThis script can be used as a starting point for audio classification tasks, such as:\n\n* Sentiment analysis of speech recordings\n* Emotion detection from music\n* Voice recognition systems\n\n**Advice and Next Steps**\n-------------------------\n\n* Experiment with different dataset sizes and architectures.\n* Try adjusting the hyperparameters to optimize performance.\n* Explore other Transformers library features, such as pipeline optimization or efficient inference."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:94-101

metadata:
{"docstring": "#\n# Functions to prepare models' input\n#", "end_line": 101, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "prepare_ctrl_input", "start_line": 94, "type": "function", "chroma:document": "**Function Summary**\n\n`prepare_ctrl_input`\n================================\n\n### Main Purpose and Functionality\n\nPrepares input for CTRL (Conversational Toolkit for Reasoning and Training) by encoding the prompt text.\n\n### Key Implementation Details\n\n* Checks temperature threshold (0.7) and logs a warning if above.\n* Encodes prompt text using `tokenizer` with `add_special_tokens=False`.\n* Verifies that encoded prompt starts with a control code; logs a warning otherwise.\n\n### Notable Dependencies or Requirements\n\n* `args`: Input arguments\n* `_`: Ignored argument (required, but not used)\n* `tokenizer`: Custom tokenizer object\n* `prompt_text`: Input text to be encoded\n\n### External Dependencies\n\n* Custom tokenizer implementation (`tokenizer`)\n* logging module (`logger`)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:104-127

metadata:
{"docstring": "", "end_line": 127, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "prepare_xlm_input", "start_line": 104, "type": "function", "chroma:document": "**Summary:**\n\n`prepare_xlm_input` function prepares input for XLM (Extreme Language Model) based on provided arguments and model configurations.\n\n**Key Features:**\n\n* Sets language and retrieves corresponding ID from `lang2id` dictionary\n* Prompts user to select language if not present in available languages\n* Initializes `mask_token_id` when applicable (TODO)\n\n**Method Signatures and Interfaces:**\n\n* `prepare_xlm_input(args, model, tokenizer, prompt_text)`\n\n**Notable Dependencies and Requirements:**\n\n* `args.model_name_or_path`\n* `model.config.lang2id`\n* `tokenizer lang2id`\n\n**Primary Purpose:**\nThis function prepares input for XLM-based models by setting language and retrieving corresponding ID. It also prompts user to select language if not present in available languages."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:130-133

metadata:
{"docstring": "", "end_line": 133, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "prepare_xlnet_input", "start_line": 130, "type": "function", "chroma:document": "**Concise Summary**\n\n**Functionality:** Prepares input for XLNet model by concatenating a prefixed text with the provided `prompt_text`.\n\n**Key Details:**\n- Concatenates `prefix` with `prompt_text` if available\n- Uses default values (`args.prefix`, `args.padding_text`) in absence of explicit value\n- Default prefix is specified as `PREFIX`\n\n**Notable Dependencies/Requirements:**\n- `args`: Expected dictionary containing input parameters\n- `tokenizer`\n- `_` ( Unused argument)\n- `prompt_text`"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:136-139

metadata:
{"docstring": "", "end_line": 139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "prepare_transfoxl_input", "start_line": 136, "type": "function", "chroma:document": "**Summary:**\n\n`prepare_transfoxl_input` function:\n\n* Prepares the input for a `transfoxl` transformer-based model.\n* Concatenates a prefix (defaulting to `args.padding_text` or `PREFIX`) with the provided `prompt_text`.\n* Returns the modified prompt text.\n\n**Key implementation details:**\n\n* Uses the `prefix` and default values from the `args` dictionary (`prefix`, `padding_text`)\n* Concatenation of `prefix` and `prompt_text`\n * Constants:\n\t+ `PREFIX`: Default prefix (used as fallback)\n\n**Notable dependencies or requirements:**\n\n* `tokenizer` object (not explicitly used in this code chunk)\n* External dependency: `_` (likely a wildcard or placeholder, its purpose not clear without more context)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:150-157

metadata:
{"docstring": "", "end_line": 157, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "adjust_length_to_model", "start_line": 150, "type": "function", "chroma:document": "**Summary:**\n\n`adjust_length_to_model()` function adjusts the input `length` to ensure it's within the bounds of the maximum sequence length set by `max_sequence_length`. The function handles cases where `length` is negative or exceeds `max_sequence_length`.\n\n**Key Implementation Details:**\n\n* Returns adjusted `length` between 0 and `max_sequence_length`.\n* Handles edge cases:\n\t+ Negative `length`: sets to `MAX_LENGTH` avoiding infinite loop.\n\t+ Exceeds `max_sequence_length`: no generation larger than model size.\n\n**Notable Dependencies or Requirements:**\n\n* No external dependencies specified.\n* Uses a constant `MAX_LENGTH`."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:160-186

metadata:
{"docstring": "", "end_line": 186, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "sparse_model_config", "start_line": 160, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `sparse_model_config` function retrieves configuration settings for a sparse model, specifically `num_layers`, `num_heads`, and `num_embedding_size_per_head`. It extracts these values from the input `model_config` object, handling variable attribute names.\n\n**Key Details:**\n\n* Extracts:\n\t+ `embedding_size`: Determined by `hidden_size`, `n_embed`, or `n_embd`\n\t+ `num_head`: Determined by `num_attention_heads` or `n_head`\n* Checks for valid values and raises `ValueError` if any are invalid.\n* Calculates `num_embedding_size_per_head`\n\n**Notable Attributes/Functions:**\n\n* `model_config.hidden_size`, `model_config.n_embed`, `model_config.n_embd` (alternative names)\n* `model_config.num_attention_heads`, `model_config.n_head`\n* `model_config.n_layer`, `model_config.num_hidden_layers`\n\n**External Dependencies:**\n\nNone."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:189-215

metadata:
{"docstring": "", "end_line": 215, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "generate_past_key_values", "start_line": 189, "type": "function", "chroma:document": "**Summary: generate_past_key_values function**\n\n* **Purpose**: Generates key values and queries tensors to be used in Transformer model, specifically for multi-head attention mechanisms in different model architectures (\"bloom\" and general).\n\n* **Detailed implementation**:\n\t+ Uses `torch.empty` to create tensors.\n\t+ Sets tensor dimensions based on hyperparameters (`num_block_layers`, `batch_size`, `seq_len`, `num_attention_heads`, and `num_embedding_size_per_head`).\n\t+ Appends dimension `models.dtype` to ensure correct data type conversion.\n\t+ Moves tensors to device using `model.device`.\n\t+ Creates two separate sets of key values (self-attention queries) for each block layer, depending on the model architecture.\n\n* **Notable dependencies/requirements**:\n\t+ `torch`: The PyTorch library is required for creating and manipulating tensors.\n\t+ `sparse_model_config`: A function defining the configuration of sparse models.\n\t+ `model.config.model_type` with value \"bloom\" or otherwise, determining the Tensor shape allocation.\n\n* **Code quality**: Variable naming follows conventional Python naming conventions, but could be improved by including descriptive comments or docstrings to highlight individual algorithmic components."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:218-233

metadata:
{"docstring": "", "end_line": 233, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "prepare_jit_inputs", "start_line": 218, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `prepare_jit_inputs` function prepares inputs for a JIT (Just-In-Time) compilation process in a deep learning model. It:\n\n* Takes 3 inputs: `inputs`, `model`, and `tokenizer`.\n* Processes `inputs` into a `dummy_input` tokenized using the `tokenizer`, with attention mask created.\n* Appends padding to the beginning of the attention mask.\n* Returns the prepared `dummy_input`.\n\n**Key Details**\n\n* Tokenization using `tokenizer.batch_encode_plus`\n* Creation of dummy input tensor using `torch.cat`\n* Modification of attention mask using a special token (zero)\n\n**Dependencies/Requirements**\n\n* `inputs`, `model`, and `tokenizer` objects\n* `torch` library for tensor operations"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:236-280

metadata:
{"docstring": "", "end_line": 280, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "_ModelFallbackWrapper", "start_line": 236, "type": "class", "chroma:document": "**Class Summary: `_ModelFallbackWrapper`**\n\nThe `_ModelFallbackWrapper` class is a wrapper that provides fallback behavior for generation models. Its primary purpose is to enable cache reuse and reordering of past key values during beam search.\n\n**Key Implementation Details:**\n\n*   The class takes two parameters in its constructor: `optimized` and `default`, which are instances of classes extending `GenerationMixin`.\n*   The `__call__` method handles the core generation logic, ensuring that cache is reused when necessary and reordering past key values for correct beam search.\n*   The `prepare_inputs_for_generation` method delegates to the `_default` object's implementation, allowing the wrapper to adapt its behavior.\n\n**Notable Dependencies and Requirements:**\n\n*   The class relies on a CausalLMOutputWithPast class that is not defined in this snippet.\n*   It also uses the `torch` library for tensor operations and potentially other dependencies.\n\n**Interfaces and Inheritance:**\n\nThe `_ModelFallbackWrapper` inherits from `GenerationMixin` with additional functionality. Notably, it wraps around an instance of a parent class (`optimized` or `default`) that implements specific methods (e.g., `__call__`, `prepare_inputs_for_generation`)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py:283-442

metadata:
{"docstring": "", "end_line": 442, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation.py", "name": "main", "start_line": 283, "type": "function", "chroma:document": "**Function Summary:**\n\nThis function generates sequences of text based on input prompts. It uses the Hugging Face Transformers library and is designed to be highly customizable.\n\n**Main Purpose and Functionality:**\n\nThe primary purpose of this function is to generate sequential text based on user input. The function takes in multiple command-line arguments, including:\n\n* `--model_type`: The type of model to use (e.g., CTRL, T5)\n* `--model_name_or_path`: The path to a pre-trained model or its name\n* `--prompt`: The user prompt for the text generation\n*`--length`, `--stop_token`, `--temperature`, `--repetition_penalty`, `--k`, and `--p`: Various parameters controlling the text generation process\n\n**Key Implementation Details:**\n\nThe function uses Model Classes from Hugging Face Transformers library. Key implementation details include:\n\n* Loading pre-trained models using `model_class.from_pretrained(args.model_name_or_path)`\n* Converting user input to input IDs using the tokenizer\n* Using the model's `generate()` method to produce text sequences based on the input IDs and provided parameters\n\n**Notable Dependencies or Requirements:**\n\nThis function relies on the Hugging Face Transformers library for model loading, tokenization, and text generation.\n\nExample external dependencies:\n```\nimport torch\nfrom transformers import Model, AutoTokenizer\n```\n\nThis summary focuses on the core functionality of the code, providing a concise overview of its main purpose, key implementation details, and notable dependencies."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation_contrastive_search.py:38-131

metadata:
{"docstring": "", "end_line": 131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/text-generation/run_generation_contrastive_search.py", "name": "main", "start_line": 38, "type": "function", "chroma:document": "**Summary:**\n\nThis is the main function for generating text sequences using a pre-trained language model.\n\n**Primary Purpose:** Generate text sequences given a prompt and model configuration.\n\n**Key Implementation Details:**\n\n* Initializes an `ArgumentParser` to parse command-line arguments for:\n\t+ Model name or path\n\t+ Prompt text\n\t+ Sequence length\n\t+ Stop token\n\t+ Temperature and repetition penalty parameters\n\t+ Additional configuration options (prefix, padding text, language code)\n* Sets the random seed for initialization if specified\n* Initializes the distributed state and model tokenizer and model using Hugging Face Transformers library\n* Generates text sequences using the `generate` method of the model with the given inputs\n\n**Notable Dependencies:**\n\n* Hugging Face Transformers library (specifically, `argparse`, `AutoTokenizer`, `AutoModelForCausalLM`)\n* External dependencies:\n\t+ NVIDIA Apex for 16-bit precision (if --fp16 is used)\n\n**Key Method Signatures and Interfaces:**\n\n* `generate(model=self.model, ...)` from Hugging Face Transformers library\n* `set_seed(seed)` from a custom function (not shown in this code snippet)\n* `decode(generated_sequence, ...)`, `add_special_tokens`, `clean_up_tokenization_spaces` methods from Hugging Face Transformers library"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag_no_trainer.py:66-225

metadata:
{"docstring": "", "end_line": 225, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag_no_trainer.py", "name": "parse_args", "start_line": 66, "type": "function", "chroma:document": "**summary**\n\nThis function creates an argument parser for fine-tuning a transformer model on a multiple choice task. It supports various command-line arguments, including:\n\n* Dataset and configuration names\n* Training data file paths\n* Model parameters (e.g., learning rate, batch sizes)\n* Output directory and other experiment settings\n\n**Key details**\n\n* The `argparse.ArgumentParser` class is used to define the argument parser.\n* 33 arguments are defined, with varying data types and default values.\n\n**Notable dependencies**\n\n* `datasets` library for dataset configuration\n* `huggingface.co/models` for model selection\n* `Tokenizers` library (optional)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag_no_trainer.py:285-703

metadata:
{"docstring": "", "end_line": 703, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag_no_trainer.py", "name": "main", "start_line": 285, "type": "function", "chroma:document": "The code is a PyTorch implementation of a neural network training pipeline using the Hugging Face Transformers library and the Weights & Biases tracking system. The pipeline trains a model on a dataset using a specified hyperparameter search space.\n\nHere's a high-level overview of the pipeline:\n\n1. Data loading: The code loads the dataset into memory.\n2. Model initialization: A pre-trained transformer model is initialized with a specific configuration.\n3. Hyperparameter search: The code performs a grid search over a defined hyperparameter space to find the best combination that results in a minimum loss.\n4. Training loop: The model is trained on the dataset in a loop, with optimization and logging steps at each epoch.\n5. Evaluation: After training finishes, the model is evaluated on a validation set and the accuracy is computed.\n6. Outputting results: The final results are saved to disk, including the trained model checkpoints, tokenizer, and evaluation metrics.\n\nKey components:\n\n* `PyTorch` for neural network building and optimization\n* `Hugging Face Transformers` for pre-trained models and tokenizers\n* `Weights & Biases` for tracking pipeline runs and hyperparameter search\n* `API` for uploading results to the Hugging Face model hub\n\nThis code is likely used in a research or development environment where the goal is to train high-performance language models for tasks such as natural language processing, computer vision, etc."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag.py:224-490
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 490, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag.py", "name": "main", "start_line": 224, "type": "function", "chroma:document": "Here is a concise summary:\n\nThis code sets up a Hugging Face Trainer for fine-tuning an English question-answering model on the SWAG (Stanford Question Answering Game) dataset. It defines multiple datasets, preprocesses them using the `tokenizer` and `pre ProcessFunction`, trains the model with a suitable datollarator and computing metric, saves trained weights to Hugging Face Model Hub after completion."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag.py:493-495
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 495, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/multiple-choice/run_swag.py", "name": "_mp_fn", "start_line": 493, "type": "function", "chroma:document": "**Summary:**\n\n `_mp_fn` is an inner function that calls `main()` when utilizing XLA Spawn (TPUs), without handling alternative execution paths.\n\n**Key Details:**\n\n- Primary purpose: Execute `main()` on TPUs using XLA Spawn.\n- Method signature: `_mp_fn(index)`, no explicit parameters or return types mentioned.\n- Notable dependency:\n  - Utilizes `xla_spawn` for TPUs, a library provided by Google's XLA (Accelerated Linear Algebra) project."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py:197-211

metadata:
{"docstring": "# We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module,\n# so we jit it to be faster.", "end_line": 211, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py", "name": "Transform", "start_line": 197, "type": "class", "chroma:document": "**Summary**\n\n`Transform` is a PyTorch module for image preprocessing, which applies sequence of transformations to input image `x`. It constructs a sequential pipeline using `torch.nn.Sequential`, including:\n\n*   resizing the image via `Resize`\n*   center cropping the image via `CenterCrop`\n*   converting image data type via `ConvertImageDtype` to float\n*   normalizing image values via `Normalize`\n\nThe transform pipeline is optimized for faster processing using `jit`.\n\n**Key Implementation Details**\n\n*   Input parameter types: `image_size`, `mean`, and `std`\n*   Output type: `torch.Tensor`\n*   Notable transformations:\n    *   `Resize` with interpolation mode `BICUBIC`\n    *   `CenterCrop` for image size match\n    *   `ConvertImageDtype` to float data type\n    *   `Normalize` with provided mean and std values\n\n**Notable Dependencies**\n\n*   PyTorch (`torch`)\n*   PIL (Python Imaging Library) (`PIL.Image`)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py:214-223

metadata:
{"docstring": "", "end_line": 223, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py", "name": "collate_fn", "start_line": 214, "type": "function", "chroma:document": "**Function Summary: `collate_fn`**\n\n**Purpose and Functionality:** Defines a function to collate and preprocess input data for training, aggregating pixel values, input IDs, and attention masks.\n\n**Implementation Details:**\n\n* Prepends to each tensor data using stack on pixel values.\n* Creates tensors from input data (input_ids and attention_mask) with specified data types (long).\n\n**Notable Dependencies/Requirements:**\n\n* `torch`: required for tensor operations\n\n**Output:** A dictionary containing the preprocessed input data (`pixel_values`, `input_ids`, `attention_mask`) with a boolean flag indicating that returns loss (`\"return_loss\": True`)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py:226-527

metadata:
{"docstring": "", "end_line": 527, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/contrastive-image-text/run_clip.py", "name": "main", "start_line": 226, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\nThe code trains a Contrastive Image-Text Modeling (CITM) model using the Hugging Face Transformers library. The CITM model is trained to learn representations from pairs of images and text captions. \n\nThe training process involves:\n\n1. Preprocessing: removing corrupt images, tokenizing text inputs, transforming images on the fly.\n2. Training: using the Trainer class from Hugging Face to train the model with a batched dataset (if `do_train` is True).\n3. Evaluation: evaluating the trained model on a separate validation set (if `do_eval` is True) and saving the results.\n4. Saving models, tokenizers, and image processors to disk and pushing them to the Hugging Face model hub if `push_to_hub` is True.\n\nThe code makes use of utility functions from Hugging Face such as `collate_fn`, `finetuned_from` which are passed as arguments to various Trainer methods.\n\nThis code can be used to train a pre-trained CITM model on a specific dataset and fine-tune it for a specific task. \n\nHere's some key information:\n\n- Training strategy: Training is configured using the Hugging Face Trainer.\n- Loss function and metrics: Not explicitly specified, but common loss functions and metrics may include cross-entropy loss or top-1 accuracy.\n- Dataset: Pre-trained dataset (e.g., COCO) used for training and evaluation.\n- Parameters:\n  - `data_args`: Command-line arguments for model pretraining.\n  - `training_args`: Training parameters such as batch size, number of epochs, etc. \n  - `model`: Instance of the Contrastive Image-Text Modeling (CITM) model.\n\nThe code is written in Python using several Hugging Face libraries, including Transformers, Trainer, and datasets."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner.py:216-641

metadata:
{"docstring": "", "end_line": 641, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner.py", "name": "main", "start_line": 216, "type": "function", "chroma:document": "This code is for training and testing a Hugging Face model, specifically a sequence classification model, on a token classification task. The model is trained on a dataset provided in the `data_args` object.\n\nMain components of this code:\n\n1. **Data**: The data is loaded using the `Tokenizer` class from Hugging Face. The data is tokenized and labeled.\n2. **Model**: The model is instantiated with the specified parameters, including the learning rate, batch size, and number of epochs.\n3. **Trainer**: The `Trainer` class from Hugging Face is used to train the model on the training dataset. The trainer also evaluates the model on the evaluation dataset and predicts on the prediction dataset.\n4. **Evaluation Metrics**: Several metrics are computed during the training process, including precision, recall, F1-score, accuracy, and entity-level metrics.\n\nThe code also includes features for:\n\n* Resuming from a checkpoint\n* Logging metrics to a file\n* Saving predictions to a file\n* Pushing the model to Hugging Face's Model Hub\n\nOverall, this code provides a basic framework for training and evaluating a sequence classification model using Hugging Face."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner.py:644-646

metadata:
{"docstring": "", "end_line": 646, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner.py", "name": "_mp_fn", "start_line": 644, "type": "function", "chroma:document": "**Function Summary:**\n\n`_mp_fn(index)` is an internal control flow function used with XLA Spawn (TPU) infrastructure. Its primary purpose is to execute the `main()` function.\n\n**Key Implementation Details:**\n\n* Expects an `index` parameter but does not utilize it.\n* Calls `main()`, which is not part of this function's implementation.\n\n**Notable Dependencies or Requirements:**\n\n* Implicitly relies on XLA Spawn (TPU) infrastructure for execution."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner_no_trainer.py:68-269

metadata:
{"docstring": "", "end_line": 269, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner_no_trainer.py", "name": "parse_args", "start_line": 68, "type": "function", "chroma:document": "**Summary**\n\nThis function defines the command-line interface for fine-tuning a transformer model on a text classification task (NER) with the Accelerate library. It handles various input parameters, including dataset names, configuration names, training and validation files, model name or path, hyperparameters, and more.\n\n**Key Implementation Details**\n\n*   Parses command-line arguments using `argparse.ArgumentParser`.\n*   Expects various file extensions for training and validation data.\n*   Handles hyperparameters such as learning rate, batch size, number of epochs, and others.\n*   Supports fine-tuning from a pre-trained model or model identifier.\n\n**Notable Dependencies/Requirements**\n\n*   `argparse` library for command-line argument parsing\n*   Accelerate library for fine-tuning transformer models\n*   Datasets library for handling dataset names and configuration names\n*   Hugging Face transformers library (optional)"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner_no_trainer.py:272-827

metadata:
{"docstring": "", "end_line": 827, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/token-classification/run_ner_no_trainer.py", "name": "main", "start_line": 272, "type": "function", "chroma:document": "This is a training script for a NLP model using the Hugging Face library. The script trains a model on a specified dataset and saves the model to a directory. Here's a concise summary:\n\n**Key Features:**\n\n* Trains a NLP model on a specified dataset\n* Supports various metrics, including sequence evaluation (seqeval)\n* Allows for checkpointing at specific epochs or steps\n* Updates model weights using gradient descent with optimizer and learning rate scheduler\n* Saves the trained model to a directory\n* Pushes updates to Hugging Face model hub if requested\n\n**Code Organization:**\n\n* The script defines constants for hyperparameters, metrics, and output directories\n* It initializes the Hugging Face client and model loader\n* Training is performed in an iterative loop, where each iteration consists of:\n\t+ Forward pass through the model with a batch of input data\n\t+ Backward pass to update model weights using gradient descent\n\t+ Batch normalization and logging\n* After training concludes, the script saves the trained model to disk and pushes it to Hugging Face if requested. \n\nNote that this code appears to use some advanced features of the Hugging Face library, such as multi-processing, distributed training, and API calls. A more detailed explanation would require a separate response."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/utils_qa.py:31-249
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 249, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/utils_qa.py", "name": "postprocess_qa_predictions", "start_line": 31, "type": "function", "chroma:document": "Here is a concise summary of the provided code:\n\n**Overview**\n\nThis library function generates text answers for questions based on their context and scores generated from machine learning models. It takes in several parameters, including:\n\n* `n_best_size`: the number of best predictions to return\n* `version_2_with_negative`: whether the model can output a null answer (i.e., a blank response)\n* `context`: the question or text being answered about\n* `predictions_file`, `nbest_predictions_file`, and `null_odds_file` optional parameters for saving the results\n\n**Functionality**\n\n1. The function generates predictions by iterating through each prediction in ascending order of score, excluding null answers (if applicable).\n2. For each prediction, it extracts the relevant text from the original context using offsets.\n3. It then computes the softmax of all scores and adds the corresponding probabilities to the prediction dictionary.\n4. If a null answer is returned, its format is determined by the `version_2_with_negative` parameter.\n5. The function returns a dictionary with the best predictions for each question.\n\n**Notes**\n\n* The code uses NumPy to perform some calculations, but it includes checks to ensure compatibility with different math libraries (e.g., PyTorch and TensorFlow).\n* The null answer handling is conditional on the `version_2_with_negative` parameter.\n* Some parts of the code (e.g., comment block) are not shown due to formatting constraints."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/utils_qa.py:252-443

metadata:
{"docstring": "", "end_line": 443, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/utils_qa.py", "name": "postprocess_qa_predictions_with_beam_search", "start_line": 252, "type": "function", "chroma:document": "Here is a concise summary:\n\nThis code implements a function `compute_answers` that generates answers to a question from a set of predictions. The predictions are derived from a question's context and the model's confidence scores for each possible answer. The function takes as input:\n\n* A list of questions with their corresponding contexts\n* A list of prediction objects, each containing:\n\t+ An offset range for the answer in the context\n\t+ A confidence score (derived from start and end log probabilities)\n\t+ A start log probability\n\t+ An end log probability (transformed using LogSumExp trick)\n* The maximum length of answers to consider\n* Optional output directory to save the predictions\n\nThe function returns two dictionaries:\n\n* `all_predictions`: a dictionary mapping each question ID to its best answer (i.e., the one with the highest confidence score)\n* `scores_diff_json` (optional): a dictionary mapping each question ID to their null odds score (value equal to -2e-6 in case no prediction is found)\n\nThe code uses NumPy for computation and JSON serialization/deserialization."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_qa.py:30-136

metadata:
{"docstring": "", "end_line": 136, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_qa.py", "name": "QuestionAnsweringTrainer", "start_line": 30, "type": "class", "chroma:document": "**Summary:**\n\nThe `QuestionAnsweringTrainer` class is a custom trainer for Question Answering Models, extending the `Trainer` class. Its primary purpose is to evaluate and make predictions on given datasets.\n\n**Main Purpose and Functionality:**\n\n* Evaluates `eval_dataset` using the `evaluate` method\n* Makes predictions on `predict_dataset` using the `predict` method\n* Applies post-processing function (`post_process_function`) to evaluation and prediction outputs\n\n**Key Implementation Details:**\n\n* `evaluate`: uses `prediction_loop` or `evaluation_loop`, computes metrics, and applies post-processing function\n* `predict`: uses `prediction_loop` or `evaluation_loop`, computes metrics, and applies post-processing function\n* Uses `compute_metrics` function to compute evaluation metrics\n\n**Notable Dependencies and Requirements:**\n\n* `Trainer` class (extended by `QuestionAnsweringTrainer`)\n* `post_process_function`\n* `eval_dataset` and `predict_dataset`\n* `compute_metrics` function\n* `prediction_loop` or `evaluation_loop`\n\n**Method Signatures and Interfaces:**\n\n* `evaluate(eval_dataset, eval_examples, ..., metric_key_prefix) -> metrics`\n* `predict(predict_dataset, predict_examples, ..., metric_key_prefix) -> output`"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa.py:227-705
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 705, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa.py", "name": "main", "start_line": 227, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\n**Overview**: This is a script for training and evaluating a Question Answering (QA) model using the Hugging Face Transformers library. The purpose is to fine-tune a pre-trained QA model on a specific dataset.\n\n**Key Components**:\n\n1. **Model Training**: The script loads a pre-trained QA model, sets hyperparameters, and trains the model on a training dataset.\n2. **Model Evaluation**: After training, the script evaluates the model on an evaluation dataset using metrics such as exact match, F1 score, etc.\n3. **Model Prediction**: If required, the script can predict answers for a new test dataset.\n\n**Configuration Options**:\n\n* `data_args`: A dictionary of configuration options, including dataset name, version number (v2 or v2_with_negative), and hyperparameters.\n* `training_args`: A dictionary of training arguments, such as resume from checkpoint, save model, etc.\n\n**Output**:\n\n* The script saves the trained model to a specific directory.\n* It also logs metrics during training and evaluation phases.\n* If required, it can predict answers for new test datasets."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa.py:708-710
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 710, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa.py", "name": "_mp_fn", "start_line": 708, "type": "function", "chroma:document": "**Summary:**\n\n`_mp_fn(index)` is a recursive function that executes the `main()` function when working with XLA Spawn on TPU's. Specifically, it serves as a worker function for multi-processing and parallel computing.\n\n**Key Details:**\n\n* Primary purpose: Execute `main()` in a multi-processing context.\n* Algorithm/pattern: Recursive function call to utilize multiple processes/cores.\n\n**Notable Dependencies/R requirements:**\n\n* None explicitly listed, but implies:\n  + `xla_spawn`: a library for XLA (Accelerated Linear Algebra) and TPU's.\n  + Python multiprocessing capabilities."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py:69-89

metadata:
{"docstring": "", "end_line": 89, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py", "name": "save_prefixed_metrics", "start_line": 69, "type": "function", "chroma:document": "**Function Summary: `save_prefixed_metrics`**\n\n### Purpose and Functionality\n\nThe `save_prefixed_metrics` function saves results to a JSON file while prefixing metric names. It takes in a dictionary of results, an output directory, and optional parameters for file name and metric key prefix.\n\n### Key Implementation Details\n\n* Prefixes all keys with the specified `metric_key_prefix`.\n* Utilizes `json.dump()` to write the results to a file.\n\n### Notable Dependencies or Requirements\n\n* Uses `os` module for path manipulation.\n* Relying on `json` module for data serialization."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py:92-335

metadata:
{"docstring": "", "end_line": 335, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py", "name": "parse_args", "start_line": 92, "type": "function", "chroma:document": "This function defines a script for fine-tuning a transformer model on a Question Answering (QA) task. The script takes in various command-line arguments that configure the training process, such as:\n\n* Data files and locations\n* Model type and parameters\n* Training and validation settings\n* Overwrite cache and push to hub options\n\nThe function performs sanity checks to ensure consistency and validity of input arguments. It then parses the argument using `argparse` and returns a `Namespace` object containing all the arguments.\n\nIn essence, this script allows users to customize the training process for a transformer-based QA model and provides a flexible way to perform fine-tuning with various configuration options."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py:338-1039

metadata:
{"docstring": "", "end_line": 1039, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_no_trainer.py", "name": "main", "start_line": 338, "type": "function", "chroma:document": "This is a PyTorch script for training and evaluating a sequence-to-sequence model using the SQuADv2 dataset. The script covers various aspects of model training, including:\n\n1. Setting up the training environment\n2. Defining the model architecture and hyperparameters\n3. Training the model on the SQuADv2 dataset\n4. Evaluating the model's performance using precision and recall metrics\n5. Logging progress and results\n6. Saving the trained model to a local directory\n\nThe script is well-structured, with clear section headings and concise comments. However, it is quite long and might be improved with additional documentation or annotations.\n\nSome minor issues and suggestions for improvement are:\n\n1. The `create_and_fill_np_array` function is not defined within this script.\n2. The `post_processing_function` and `metric` functions are not defined here either.\n3. The `save_prefixed_metrics` function is called without being imported from any module.\n\nTo make the code more concise, some suggestions are:\n\n1. Extract common functions into separate modules to improve readability and maintainability.\n2. Use type hints for the script's variables and function parameters to enhance clarity.\n3. Consider using logging libraries (e.g., `logging`) instead of custom logging statements to conform with standard Python practices.\n\nHere's a minor refactored version incorporating some of these suggestions:\n```python\nimport torch\nfrom typing import Dict\n\n# Define post-processing and evaluation functions (e.g., in separate modules)\n\ndef create_and_fill_np_array(data, max_len):\n    # Function definition remains the same\n    pass\n\ndef compute_metrics(predictions, labels):\n    # Implementation omitted for brevity\n    return predictions, labels\n\ndef save_prefixed_metrics(eval_metric, output_dir):\n    # Import necessary module (e.g., `logging`) and implementation adjusted accordingly\n    import logging\n    logging.info(\"Evaluation metrics: \" + str(eval_metric))\n    # ...\n\n# Training script remains largely unchanged...\n\nif args.with_tracking:\n    log = {\n        \"squad_v2\" if args.version_2_with_negative else \"squad\": eval_metric,\n        \"train_loss\": total_loss.item() / len(train_dataloader),\n        \"epoch\": epoch,\n        \"step\": completed_steps,\n    }\nif args.do_predict:\n    # ...\n\n# Training the model\noptimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)\nfor epoch in range(args.num_epochs):\n    # ...\n```\nPlease adjust the refactoring according to your specific needs and requirements."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py:225-732

metadata:
{"docstring": "", "end_line": 732, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py", "name": "main", "start_line": 225, "type": "function", "chroma:document": "This is an example Python script that trains a Question Answering (QA) model using the Hugging Face Transformers library. The script uses a pre-trained QA model and fine-tunes it on a specific dataset.\n\nHere's a brief summary of the code:\n\n1. **Data preparation**: The script loads the trained pre-trained QA model, tokenizer, and dataset.\n2. **Training setup**: The script sets up the training process by creating an instance of the `QuestionAnsweringTrainer` class, which takes the model, arguments, train dataset, evaluation datasets, and other configuration options as inputs.\n3. **Training loop**: The script trains the model using the `train` method of the `Trainer` instance, which calls the `train` function with various parameters such as batch size, number of epochs, and optimization algorithm.\n4. **Evaluation and prediction**: After training, the script evaluates the model on a hold-out test dataset and generates predictions for new, unseen data.\n\nThe script is likely part of a larger project that aims to train high-performance models using efficient algorithms and computational resources."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py:735-737

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


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:65-85

metadata:
{"docstring": "", "end_line": 85, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "save_prefixed_metrics", "start_line": 65, "type": "function", "chroma:document": "**Summary:**\n\n### save_prefixed_metrics Function\n\n*   Saves prefixed metric results to a JSON file.\n*   Modifies input dictionary `results` by prefixing keys with a specified name (`metric_key_prefix`) if necessary.\n\n#### Key Details:\n\n*   **Function Argument:** \n    ```python\ndef save_prefixed_metrics(results: dict, output_dir: str, file_name: str = \"all_results.json\", metric_key_prefix: str = \"eval\")\n```\n*   **Data Structure Manipulation:** Prefixes dictionary keys with `metric_key_prefix` using `dict.pop()` and f-strings for renaming.\n*   **File Handling:** Saves modified dictionary to a JSON file with specified `output_dir` and `file_name`.\n\n#### Dependencies:\n\n*   External dependencies: \n    *   `os`: module used for interacting with the operating system, e.g., joining path components together (`os.path.join`)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:88-297

metadata:
{"docstring": "", "end_line": 297, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "parse_args", "start_line": 88, "type": "function", "chroma:document": "**Function Summary:**\n\nThis function, `parse_args()`, creates an ArgumentParser object and populates it with 35 optional arguments for fine-tuning a transformer model on a Question Answering task.\n\n**Purpose and Functionality:**\nThe primary purpose of this function is to parse command-line arguments for the fine-tuning process. It allows users to specify various parameters such as dataset name, model configuration, training data file, preprocessing configurations, and hyperparameters.\n\n**Key Implementation Details:**\n\n* The function supports both `dataset_name` and `train_file` modes of input for the first set of required arguments.\n* Various hyperparameters such as learning rate, batch size, number of epochs, and evaluation metrics are optional.\n* Additional parameters like `doc_stride`, `n_best_size`, and `null_score_diff_threshold` control specific aspects of the question answering system.\n\n**Overall:**\n\nThe function is designed to provide a flexible way to configure the fine-tuning process for transformer models on Question Answering datasets. It includes many options for customization, allowing users to tailor their experiments to suit their needs."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py:300-1062

metadata:
{"docstring": "", "end_line": 1062, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py", "name": "main", "start_line": 300, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\nThis is an implementation of the DistilBERT model on the Stanford Question Answering Dataset (SQuAD). The code trains the model using Adam optimizer and cross-entropy loss, and evaluates its performance on both training and prediction datasets. It also tracks various metrics, including train loss, epoch, step, and prediction accuracy, and logs them to a log file.\n\nKey features:\n\n* Training and evaluation of DistilBERT model on SQuAD\n* Adam optimizer with cross_entropy loss\n* Tracking of training and prediction performance metrics (train_loss, epoch, step, predict_accuracy)\n* Logging of metrics to a log file\n* Options for pushing the model to a hub repository after training\n\nThe code is written in Python and uses the Hugging Face Transformers library for the DistilBERT model. It appears to be designed for use with popular logging frameworks such as TensorBoard or Summarize, although this is not explicitly mentioned in the code."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_seq2seq_qa.py:33-163

metadata:
{"docstring": "", "end_line": 163, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_seq2seq_qa.py", "name": "QuestionAnsweringSeq2SeqTrainer", "start_line": 33, "type": "class", "chroma:document": "**Summary:**\n\nThis is an implementation of the `QuestionAnsweringSeq2SeqTrainer` class in Hugging Face Transformers. It inherits from `Seq2SeqTrainer` and provides functionality for training and evaluating question-answering models using sequence-to-sequence learning.\n\n**Main Functionality:**\n\n1. Evaluates a model on a dataset using the `evaluate` method.\n2. Generates predictions on a test dataset using the `predict` method.\n3. Preprocesses and post-processes input data for evaluation or prediction.\n4. Computation of metrics, such as speed, compilation time, and loss.\n\n**Key Implementation Details:**\n\n* `evaluate`: uses `prediction_loop` or `evaluation_loop`, disables metric computation temporarily, and updates output metrics with speed statistics.\n* `predict`: uses `prediction_loop` or `evaluation_loop`, generates predictions using `post_process_function`, and computes metrics on the prediction output.\n\n**Notable Dependencies:**\n\n* Hugging Face Transformers library\n* PyTorch library\n\nExternal dependencies:\n\n* None\n\nInterfaces:\n\n* `Seq2SeqTrainer`\n* `Dataset`\n* `PredictionOutput`\n\nThis summary provides a concise overview of the key functionality, implementation details, and dependencies of the provided class code chunk."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py:272-733

metadata:
{"docstring": "", "end_line": 733, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py", "name": "main", "start_line": 272, "type": "function", "chroma:document": "This code is for training and evaluating a Question Answering (QA) model using the Hugging Face Transformers library. It includes dataset processing, model training, evaluation, prediction, and model pushing to a hub.\n\nHere's a high-level overview:\n\n1. **Dataset Preprocessing**: The code preprocesses the QA data by applying tokenization, label encoding, and padding.\n2. **Model Training**: It trains the model using the `QuestionAnsweringSeq2SeqTrainer` class, which combines sequence-to-sequence training with loss-based optimization.\n3. **Evaluating Performance**: During evaluation, the code assesses the model's performance on a validation set using various metrics (e.g., accuracy, precision, recall).\n4. **Predicting Answers**: The model uses its trained weights to predict answers for input questions.\n5. **Saving and Pushing Model**: After training or prediction, the model is saved and, optionally, pushed to a Model Hub.\n\nThe code also includes several hyperparameters that can be adjusted (e.g., `num_beams`, `max_length`, `dataset_name`).\n\n**Key Features:**\n\n* Supports both traditional and sequence-to-sequence training\n* Uses Hugging Face's Transformers library for tokenization and optimization\n* Includes post-processing for answer decoding and evaluation metric calculation"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_seq2seq_qa.py:736-738

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


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py:92-320

metadata:
{"docstring": "", "end_line": 320, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py", "name": "parse_args", "start_line": 92, "type": "function", "chroma:document": "**Function Summary**\n\nThis function parses command-line arguments for the Hugging Face Transformers library. The function takes in various hyperparameters and optimization settings for training a model, including:\n\n* Model architecture parameters\n* Training data configuration\n* Hyperparameter tunables (e.g., learning rate, weight decay)\n* Optimization settings (e.g., scheduler type, accumulation steps)\n\nThe function returns a dictionary of parsed arguments.\n\n**Key Parameters**\n\n* `--train_data`: Training dataset file(s) or dataset name\n* `--validation_data`: Validation dataset file(s) or dataset name\n* `--output_dir`: Directory for saving the trained model\n* `--learning_rate`: Initial learning rate\n* `--num_train_epochs`: Total number of training epochs\n* `--scheduler_type`: Optimization scheduler type (e.g., linear, cosine)\n* `--gradient_accumulation_steps`: Accumulation steps for gradient updates\n\n**Return Value**\n\nA dictionary containing the parsed command-line arguments."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py:323-796

metadata:
{"docstring": "", "end_line": 796, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py", "name": "main", "start_line": 323, "type": "function", "chroma:document": "This code is the main training loop for a large language model. Here's a concise summary:\n\n**Overview**\n\nThe code trains a pre-trained language model on a custom dataset using transformers from Hugging Face's library. The training process involves two phases: inference and validation.\n\n**Key Components**\n\n1. **Training Loop**: This section contains the core training logic, including loading data, configuring hyperparameters, and iterating over batches to perform one step of backpropagation through time (PTT) or asynchronous training.\n2. **Inference Phase**: After a specified number of epochs, the model switches from training mode to inference mode. In this phase, the model evaluates itself on a validation set based on pre-defined evaluation metrics.\n3. **Evaluation and Results**: The final results are stored in various locations for sharing with others or saving locally.\n\n**Key Highlights**\n\n* The code assumes a custom dataset, tokenizer, and optimizer, which must be defined and configured beforehand.\n* The training process uses asynchronous optimization (PTT or async) to improve the efficiency of computation on multi-gpu systems.\n* The model is saved periodically during and after training in two locations: `args.output_dir` or in Hugging Face's model hub (`repo_id`).\n* Evaluation metrics are computed based on a post-processing function applied to both the predicted and reference token sequences.\n\n**Additional Notes**\n\nThe code likely relies heavily on the Hugging Face `transformers` library, which provides pre-trained models and efficient tools for NLP tasks. The extensive logging statements indicate that debugging is enabled.\n\nThis training script is likely part of a larger project or research paper, aiming to improve large language modeling capabilities by leveraging the best practices from state-of-the-art approaches in natural language processing (NLP)."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py:311-763

metadata:
{"docstring": "", "end_line": 763, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py", "name": "main", "start_line": 311, "type": "function", "chroma:document": "This code is for training and testing a text summarization model using the Hugging Face library. Here's a high-level overview of what it does:\n\n1. **Data preparation**: Loads pre-processed data from a file ( dataset_path ) and splits it into training, validation, and prediction datasets.\n2. **Model configuration**: Configures the model with hyperparameters such as number of beams, max length for predictions, and other settings.\n3. **Trainer setup**: Creates a trainer instance to handle training, evaluation, and prediction tasks.\n4. **Training loop**: Trains the model using the `Seq2SeqTrainer` class, which uses Hugging Face's automatic mixed precision training and checkpointing mechanisms.\n5. **Evaluation and prediction**: Evaluates the model on validation datasets, measures its performance metrics (e.g., BLEU score), and makes predictions on test datasets.\n6. **Saving and uploading**: Saves the trained model in a specific directory (`output_dir`) and can be uploaded to a Hugging Face Hub.\n\nKey aspects of this code include:\n\n* Using Hugging Face's `Seq2SeqTrainer` class for efficient training and evaluation\n* Implementing custom metrics (e.g., BLEU score, prediction length) to evaluate model performance\n* Supporting various configuration options, such as language, dataset name, and training parameters"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization.py:766-768

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


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py:67-265

metadata:
{"docstring": "", "end_line": 265, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py", "name": "parse_args", "start_line": 67, "type": "function", "chroma:document": "**Functionality Summary:**\n\nThe `parse_args()` function creates an ArgumentParser instance to parse command-line arguments for fine-tuning transformer models. It supports various hyperparameters, dataset configuration options, and model-specific parameters.\n\n**Key Implementation Details:**\n\n*   The parser includes arguments for dataset configuration (`dataset_name`, `.dataset_config_name`), training data files (`train_file`, `validation_file`), hyperparameter settings (e.g., batch size, learning rate), model architecture (model name or path, model type), and additional options for caching, logging, and model deployment.\n\n**Notable Dependencies:**\n\n*   The `argparse.ArgumentParser` class from the Python Standard Library\n*   External dependencies:\n    *   **_datasets**: for loading dataset files\n    *   **transformers**: for transformer models\n    *   **torch**: for PyTorch\n\n**Interfaces and Inherited Methods:**\n\n*   none explicitly stated, but inheritance from `argparse.ArgumentParser` implies the use of its methods (e.g., `parse_args`, `print_help`) to construct a parser that handles command-line arguments.\n\nHere is an example code snippet to demonstrate how this function might be used to create and parse command-line arguments for a transformer model fine-tuning scenario:\n\n```python\nimport argparse\n\n# Create the ArgumentParser instance, as shown in the provided function\nparser = argparse.ArgumentParser(description=\"Fine-tuning transformer models\")\n\n# Define some common hyperparameter settings\nparser.add_argument(\"--batch_size\", type=int, default=32)\nparser.add_argument(\"--learning_rate\", type=float, default=5e-4)\n\n# Define a few example dataset names and their paths\ndataset_config_names = [\"bert-base-uncased\"]\ndataset_paths = {\n    \"bert-base-uncased\": \"/data/bert-base-uncased\",\n}\n\ndef create_argparse_args():\n    # Parse command-line arguments, including the dataset configuration option, training data files, etc.\n    return parser.parse_args()\n\n# Create argument parses with specific configs\nargparse_obj = argparse.ArgumentParser()\nargparse_obj.add_argument(\"-dc\", \"--dataset-config-name\", type=str,\n                         default=dataset_config_names[0],\n                         choices=[name for name in dataset_config_names])\nargparse_obj.add_argument(\"-tr_file\", \"--train-file\", type=str, required=False)\nargparse_obj.add_argument(\"-va_file\", \"--validation-file\", type=str, required=False)\n... # Other args\n\n# Parse and return\nargs = create_argparse_args()\n\n```\n\nNote that you may choose your own options, as the provided `create_argparse_args()` method is just a demonstration of how similar such functions could be constructed using argparse. The structure should generally look like this:\n\n```python\n    parser.add_argument(name, type=int, default=32, help=\"Model architecture option\")\n```"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py:268-758

metadata:
{"docstring": "", "end_line": 758, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py", "name": "main", "start_line": 268, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\n** overview**\n\nThis is a training script for a language model using the Hugging Face Transformers library. It trains the model on a dataset and evaluates its performance.\n\n**main components**\n\n1. **Data loading**: The script loads the dataset and shuffles it.\n2. **Model initialization**: The script initializes the model with the specified configuration and architecture.\n3. **Training loop**: The script runs through a series of epochs, where:\n\t* It trains the model on the shuffled data for a specified number of steps.\n\t* It evaluates the model's performance using evaluation metrics (e.g., perplexity).\n\t* It saves the model checkpoints at regular intervals.\n4. **Tracking and logging**: The scripttracks progress, logs metrics, and saves results to a file and/or upload them to Model Hub (if `push_to_hub` is enabled).\n\n**Details**\n\nThe script uses the following libraries:\n\n* Hugging Face Transformers\n* Torch\n\nIt leverages the Hugging Face framework for efficient training and evaluation, including utilities such as:\n\n* Auto-optimization of hyperparameters\n* Automatic logging and tracking of metrics\n* Saving and uploading models to Model Hub (if `push_to_hub` is enabled)\n\n**Notes**\n\nThe script assumes that you have installed the required libraries and have a valid hub token for pushing models to Model Hub. The output directory (`output_dir`) specified in the script should be created before running the script, as it will store the model checkpoints and evaluation results."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py:72-320

metadata:
{"docstring": "", "end_line": 320, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py", "name": "parse_args", "start_line": 72, "type": "function", "chroma:document": "The provided code snippet is written in Python and appears to be the main entry point for a script that utilizes the Hugging Face Transformers library for natural language processing tasks. The script accepts various command-line arguments that define how the model should be trained, saved, and potentially pushed to the Model Hub.\n\nHere's a summary:\n\n*   **Usage**: This script can be run from the command line, passing in various arguments that control its behavior.\n*   **Arguments**:\n    *   Training and validation file paths (csv, json, or txt format)\n    *   Hyperparameters for training, such as learning rate, batch size, etc.\n    *   Model configuration options\n    *   Integration with the Model Hub\n    *   Experiment tracking options\n\n**Example Argument Parsers:**\n\n```python\n# Example argument parsers\nif __name__ == \"__main__\":\n    parser = TrainerArguments(\n        resume_model=None,\n        output_dir=None,\n        push_to_hub=False,\n        hub_model_id=None,\n        hub_token=None,\n        save_steps=700,\n        save_total_limit=None,\n        save_on_each_device=True,\n        num_train_epochs=3.0,\n        per_device train_batch_size=16,\n        per_device_val_batch_size=64,\n        warmup_steps=500,\n        weight_decay=0.01,  # Add this argument\n    )\n```\n\n**Key Features:**\n\n*   **Training**: Automatic model training and tuning.\n*   **Model Saving**: Model saving options, including saving on each device or total limit.\n*   **Hub Integration**: Push the trained model to the Model Hub for sharing and collaboration."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py:323-911

metadata:
{"docstring": "", "end_line": 911, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py", "name": "main", "start_line": 323, "type": "function", "chroma:document": "Here is a concise summary:\n\nThis is the code for a PyTorch-based machine translation model training script. The script trains the model on an evaluation dataset, keeps track of losses and perplexities at each epoch, and saves the final model to a specified output directory when training completes. The script also has the option to push the final model to a Hugging Face Model Hub repository and display progress updates during training.\n\nKey features include:\n\n* Training on an evaluation dataset with batch-sized inference\n* Tracking losses and perplexities at each epoch\n* Saving the final model to a specified output directory\n* Option to push the final model to a Hugging Face Model Hub repository\n* Progress updates during training\n\nNote that some parts of the code are commented out or not clear, which may require further clarification or debugging."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py:228-574
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 574, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py", "name": "main", "start_line": 228, "type": "function", "chroma:document": "This is a PyTorch code snippet that defines a script for training and evaluating a pre-trained model, likely on the Hugging Face Transformers library. The script:\n\n1. Initializes a dataset from a specified data source (e.g., text file) and collates it into batches of permutated language inputs.\n2. Setups a `Trainer` object with the trained model, training arguments, and evaluation datasets.\n3. Trains the model using the trainer, saving checkpoints as needed.\n4. Evaluates the model on the validation dataset, logging metrics.\n\nSome high-level points:\n\n* The script is designed to work with Hugging Face's Transformers library, which provides a simple framework for text processing and machine learning tasks.\n* The `Trainer` object is used to manage training and evaluation of the model, including saving checkpoints and metrics.\n* The script uses PyTorch's built-in tensors and data structures to process and store input data.\n* The final script saves output models, logs train and eval metrics, and optionally pushes the trained model to Hugging Face's Hub.\n\nExample use case:\n\nTo use this code as a starting point for fine-tuning a pre-trained language translation model on a custom dataset, you would:\n\n1. Replace the `model_args` dictionary with your own training arguments (e.g., maximum sequence length).\n2. Update the `data_collator` to accommodate any specific requirements of your model.\n3. Specify your own datasets in the script (e.g., using the `Dataset.from_iterable()` constructor in Hugging Face's library).\n\nSome key functions used in this code:\n\n* `Trainer.train()`: Trains a model on the specified dataset and evaluation metrics.\n* `Trainer.evaluate()`: Evaluates a model on the validation dataset, returning loss values and other metrics.\n* `DataCollatorForPermutationLanguageModeling` is used to collate permutated input data in batches.\n\nPlease note that some variables are set to default or specific values (e.g., `max_train_samples`, `eval_loss`) which you can change according to your data size and optimization requirements."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py:577-579
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 579, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_plm.py", "name": "_mp_fn", "start_line": 577, "type": "function", "chroma:document": "**Summary:**\n\n`_mp_fn(index)` appears to be a function wrapper for distributing computation across multiple computing resources, likely relevant for TPUs (Tensor Processing Units). The main purpose is to execute the `main()` function in an multi-processing context. \n\n**Key Details:**\n\n- Uses `_mp_fn(index)`, suggesting potential multi-threading or process management.\n- Calls `main()`, indicating that this function serves as a wrapper around the primary computation logic.\n\n**Notable Dependencies/Requirements:**\n\n- Relies on XLA Spawn (TPUs), a technology introduced by Google to simplify machine learning inference on TPUs."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py:248-681
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 681, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py", "name": "main", "start_line": 248, "type": "function", "chroma:document": "This is a code snippet for fine-tuning a pre-trained language model (such as BERT) on the fill-mask task using Hugging Face's Trainer API. The goal is to create a custom trained model that can be used to solve the fill-mask problem from a given dataset.\n\nHere are the key points:\n\n1. Loading and preparing datasets\n2. Creating and configuring the model and args\n3. Defining data collators, preprocessors, and metrics\n4. Initializing the Trainer\n5. Training and evaluation\n\nThe code includes various sections for different parts of the process, such as loading datasets and models, defining hyperparameters and data collators, initializing the Trainer, training, and evaluating the model.\n\nNote that this is a complex piece of code that assumes knowledge of Hugging Face's API and Python programming. It may not be suitable for reading or understanding without prior experience with these concepts."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py:684-686
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 686, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm.py", "name": "_mp_fn", "start_line": 684, "type": "function", "chroma:document": "**Summary:**\n\nThe `_mp_fn` function is part of a multi-processing framework and serves as an entry point for executing tasks in parallel. It calls the `main` function when operating on Google TPU's (`xla_spawn`). The precise purpose is:\n\n*   **Primary Functionality:** Execute task in parallel using multiple processes (TPUs).\n*   **Key Implementation Detail:** Calls `main()` as a fallback for xla_spawn environment.\n*   **Notable Dependencies/Requirements:**\n    *   `xla_spawn` (Google TPU)\n   "}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py:237-647
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 647, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py", "name": "main", "start_line": 237, "type": "function", "chroma:document": "This code is used to train and evaluate a transformer-based language generation model using the Hugging Face Python library. Here's a high-level overview of what it does:\n\n1. Loads pre-trained tokenizer and model from specific paths or configurations.\n2. Downloads and preprocesses text data for training and evaluation (e.g., dataset names, file paths).\n3. Initializes a `Trainer` object with the trained model, hyperparameters, and necessary datasets (training, evaluation) if specified.\n4. Trains the model using the `train` method with optional resumption from a checkpoint (`resume_from_checkpoint`) or last saved training state (`last_checkpoint`).\n5. Evaluates the model on the evaluation dataset after training is completed.\n6. If requested, fine-tunes the model to match certain specific tasks or configurations (if applicable) and publishes results to Hub.\n\nOverall, this code is an example of how to use Hugging Face for fine-tuning pre-trained language models for text generation tasks."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py:650-652
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 652, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm.py", "name": "_mp_fn", "start_line": 650, "type": "function", "chroma:document": "**Function Summary: `_mp_fn`**\n\n* Primary purpose: The `_mp_fn` function appears to be part of an implementation supporting Google Cloud Tensor Processing Units (TPUs) execution. It likely serves as the main entry point for spawning multi-threading or parallel processing on TPUs.\n* Key implementation details:\n\t+ Calls the `main()` function, which is not defined in this code snippet.\n\t+ Notable dependency: Uses `xla_spawn` library, specifically designed for TPU execution.\n* Notable dependencies and requirements:\n\t+ Inherently uses external libraries (`xla_spawn`) related to TPU execution."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py:304-854
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 854, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py", "name": "main", "start_line": 304, "type": "function", "chroma:document": "This is the code for a Hugging Face Transformers training script. It's used to train and evaluate a text generation model on a dataset of your choice.\n\n**Key Features:**\n\n*   Supports both fine-tuning and training from scratch.\n*   Handles streaming datasets with a progress bar and metric logging.\n*   Trains models on different devices (GPU/TPU) if available and optimizes for performance, memory, or mixed-mode training.\n*   Automatically saves checkpoints and logs metrics to help track the training progress.\n\n**Preprocessing Steps:**\n\n1.  **Load Data**: The script loads a dataset using `tokenized_datasets.load()`.\n2.  **Perform Fine-tuning or Training from Scratch**: Before starting the actual training process, it fine-tunes or trains your model using `Trainer.train()` if you already have data. Otherwise, it starts with empty datasets and begins preprocessing your data.\n3.  **Fine-tune or Train Model**: Depending on whether you want to continue or start anew, it processes your dataset before calling `Trainer.train()`.\n4.  **Logging of Metrics**: As part of the training process, metrics like accuracy, perplexity, train loss, and validation metrics are logged.\n\n**Customization Options:**\n\nYou can customize several aspects of your experiment, including:\n\n*   The task type (e.g., \"text-generation\")\n*   Model configuration\n*   Data collator (by specifying `default_data_collator` as you can if you desire)\n*   Training settings (`do_train`, `do_eval`, `max Train samples`, `max eval samples`)\n*   Model configuration\n\n**Example Code:**\n\n```python\n# Load pre-trained model and tokenizer\nfrom transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n\nmodel = AutoModelForSeq2SeqLM.from_pretrained('t5-small')\ntokenizer = AutoTokenizer.from_pretrained('t5-small')\n\n# Create a custom dataset class for your specific task\nclass MyDataset(torch.utils.data.Dataset):\n    def __init__(self, inputs, labels):\n        self.inputs = inputs\n        self.labels = labels\n\n    def __getitem__(self, idx):\n        item = {key: torch.tensor(val[idx]) for key, val in enumerate(reversed(self.labels)) if key not in ['labels']}\n        item['labels'] = torch.tensor(self.labels[idx])\n        \n        input_seq = tokenizer.encode(['generate'] + self.inputs[idx], return_tensors='pt').squeeze(0)\n        labels = torch.tensor(self.labels[idx])  \n        \n        item.update({'input_ids': input_seq, 'attention_mask': (input_seq != 0).unsqueeze(-1)})\n        return item\n\n    def __len__(self):\n        return len(self.labels)\n\n# Perform preprocessing and setup for training\n\ndef create_dataset(model_name_or_path):\n    # Load pre-trained model and tokenizer\n    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoTrainingArguments\n    \n    global dataset_args\n    dataset_args = TrainingArguments(\n         # Input args for the dataset\n        output_dir='training outputs',\n        seed=42,\n        num_train_epochs=3.5,\n        per_device_train_batch_size=16 ,\n        per_device_eval_batch_size=64,\n        \n        # Training arguments\n        per_device_eval_iter_size=32, warmup_steps=500 , warmup_ratio=0.1\n    )\n    \n    args = AutoTrainingArguments(model_name_or_path=model_name_or_path, \n                                output_dir=output_dir, \n                                seed=42, \n                                num_train_epochs=num_train_epochs, \n                                per_device_train_batch_size=per_device_train_batch_size,\n                                per_device_eval_batch_size=per_device_eval_batch_size,\n                                warmup_steps=warmup_steps, warmup_ratio=warmup_ratio\n                            )\n    \n    training_args = args\n    \n    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n    dataset = load_dataset('your_dataset')\n    if dataset is None:\n        return\n    dataset = dataset.map MyDataset(tokenizer).\n         batched(train=True)\n     # Evaluation set (if applicable):\n    eval_dataset  = dataset['validation'].\n            batched(train=False)\n\n    return training_args\n\ntraining_args = create_dataset(\"path_to_your_pretrained_model\")\n# Training \ndef main():\n   trainer = Trainer(\n                        model=model,\n                        args=training_args,  \n                        train_dataset=dataset,    \n                     )\n    trainer.train()\n    # Save the results\n    trainer.save_results()\nif __name__ == \"__main__\":\n    main()\n\n```\n\nThis script provides an example for performing the training with a basic Hugging Face text generation model."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py:857-859
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 859, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py", "name": "_mp_fn", "start_line": 857, "type": "function", "chroma:document": "**Summary**\n\n`_mp_fn(index)` is a function that initializes and executes the multi-threading process. It calls the `main()` function, which appears to be the primary entry point of the program.\n\n**Key Details**\n\n* Purpose: Initialize multi-threading process\n* Implementation: Calls `main()`\n* Parameters: `index` (not used)\n\nNote: The exact purpose and implementation of the `main()` function are not specified in this code chunk."}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm_no_trainer.py:69-258

metadata:
{"docstring": "", "end_line": 258, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm_no_trainer.py", "name": "parse_args", "start_line": 69, "type": "function", "chroma:document": "**Summary**\n\nThis function uses the `argparse` library to define a set of command-line arguments for fine-tuning a transformer model on a causal language modeling task.\n\n### Main Purpose and Functionality\n\nThe function `parse_args()` parses the command-line arguments, allowing users to configure various hyperparameters, such as:\n\n* Model architectures (e.g., `model_name_or_path`)\n* Training parameters (e.g., `num_train_epochs`, `batch_size`, `learning_rate`)\n* Data loading options (e.g., `train_file`, `validation_file`)\n* Output directory and push-to-Hub configuration\n\n### Key Implementation Details\n\nThe function uses the following key algorithms, patterns, or design choices:\n\n* Args parsing using `argparse`\n* Sanity checks to ensure valid input values for user-provided files\n* Hyperparameter validation for training parameters (e.g., batch sizes, learning rates)\n\n### Notable Dependencies and Requirements\n\nThis code relies on the following external dependencies and interfaces:\n\n* `datasets` library for loading datasets\n* `huggingface.co/models` for accessing pre-trained models\n* `argparse` library for defining command-line arguments"}


created_at: 2025-02-01 08:47:38
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm_no_trainer.py:261-720

metadata:
{"docstring": "", "end_line": 720, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm_no_trainer.py", "name": "main", "start_line": 261, "type": "function", "chroma:document": "This code is for training a PyTorch model using the Transformer architecture. The model is trained on an entire corpus of text data and produces a sequence-to-sequence model that can translate or generate new text based on the input prompt.\n\nHere are some key features and behaviors:\n\n1. **Training loop**: The model trains iteratively, updating its weights using backpropagation to minimize the loss function.\n2. **Batching and parallelization**: The code uses PyTorch's `DataLoader` to load batches of data in parallel, utilizing multiple GPUs or CPU cores for faster training.\n3. **Loss and optimization**: The model measures its performance on a per-epoch basis using the perplexity score, which is calculated at each step of the training process.\n4. **Regularization and checkpointing**: The code saves the model's state every `checkpoint_steps` steps, ensuring that the model can be restored from a previous epoch in case of sudden termination of training.\n5. **Evaluation and metrics**: After completing an epoch, the model evaluates its performance on a held-out validation set, tracking the loss, perplexity, and other relevant metrics.\n\nHowever, there are also some areas for potential improvement:\n\n1. **Data preprocessing**: The code assumes that the input data is already preprocessed and split into batches, but in practice, this step may require significant effort.\n2. **Hyperparameter tuning**: While the code includes a basic optimizer (e.g., Adam), more extensive hyperparameter tuning would be beneficial to explore optimal settings for training performance.\n3. **Early stopping and patience**: The code does not implement early stopping or a patience mechanism, which can significantly improve the stability of the training process.\n\nTo make this code more concise and readable, here are some suggestions:\n\n1. Extract the training loop into a separate function to simplify and reuse the logic.\n2. Consider using `torchvision`'s `DataLoaders` instead of creating batches manually for easier management.\n3. Define constants or variables at the top to improve readability and minimize global variable usage.\n\nHere is an updated version of the code following these suggestions:\n\n```python\nimport torch\n\n# Set model architecture, training parameters, and data loading path\nmodel = TransformerModel()\noptimizer = Adam(model.parameters(), lr=0.001)\ndata_path = \"path/to/data\"\n\ndef train_model(data_loader, epoch):\n    # Model evaluation metrics will be tracked\n    total_loss = 0\n    \n    for batch in data_loader:\n        model.train()\n        optimizer.zero_grad()\n        \n        # Generate output and loss\n        \n        outputs = model(**batch)\n        loss = outputs.loss\n        loss.backward()\n        optimizer.step()\n\n        # Update total loss across batches\n        total_loss += loss.item() * len(batch[\"input_ids\"])\n    \n    # Return total loss for evaluation at the end of each epoch\n    return total_loss / (len(data_loader) * len(batch[\"input_ids\"]))\n```"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification_no_trainer.py:58-218

metadata:
{"docstring": "", "end_line": 218, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification_no_trainer.py", "name": "parse_args", "start_line": 58, "type": "function", "chroma:document": "**Function Summary:**\n\nThis function generates and parses command-line arguments for fine-tuning a pre-trained Transformers model on an image classification dataset. It defines various hyperparameters, paths to training/validation datasets and checkpoints, and tracking options.\n\n**Key Features:**\n\n* Generates command-line arguments using `argparse`\n* Defines over 30 hyperparameters with default values\n* Supports loading pre-trained models from HuggingFace Hub or own repositories\n* Enforces sanity checks for required inputs\n\n**Function Signature:**\n```python\ndef parse_args():\n    parser = ...  # construct parser object and define arguments\n    args = parser.parse_args()\n    return args\n```\n**Notable Functions/Variables:**\n\n* `argparse.ArgumentParser`: creates an argument parser object\n* `SchedulerType` (not defined in this code snippet): represents different learning rate schedules\n* `output_dir`, `train_dir`, `validation_dir`: paths to output, training, and validation directories respectively"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification_no_trainer.py:221-641

metadata:
{"docstring": "", "end_line": 641, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification_no_trainer.py", "name": "main", "start_line": 221, "type": "function", "chroma:document": "This code is used to train a model and evaluate its performance on a dataset. Here's a brief summary:\n\n**Training**\n\n* The model is trained for multiple epochs\n* During each epoch, the model processes batches of data from a training dataset\n* The loss is calculated for each batch and backpropagated through the network\n* The optimizer updates the model parameters based on the gradient\n\n**Evaluation**\n\n* After each epoch, the model evaluates its performance on a validation dataset\n* The accuracy, loss, epoch number, and step size are logged to a CSV file if tracking is enabled\n* If pushing to Hub is enabled, the model is uploaded to Hugging Face with metadata\n\n**Saving**\n\n* The trained model is saved to a directory at specified output path when training finishes\n* The model can be saved to JSON format using `json.dump` and loaded later for further use\n\nThe code uses PyTorch and its Accelerator library to parallelize the training process across multiple GPUs, which can significantly speed up the training time."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification.py:67-70

metadata:
{"docstring": "", "end_line": 70, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification.py", "name": "pil_loader", "start_line": 67, "type": "function", "chroma:document": "**pil_loader Function Summary**\n\n### Purpose and Functionality\nThe `pil_loader` function loads an image from a given path and converts it to RGB color mode.\n\n### Key Implementation Details\n- Opens the specified file in binary read mode (`\"rb\"`).\n- Uses the Python Imaging Library (PIL) to open the image, represented by the `im` variable.\n- Converts the loaded image to the RGB color space using the `convert(\"RGB\")` method.\n\n### Notable Dependencies or Requirements\n\n* **Python Imaging Library (PIL):** Import in the original codebase is required to utilize this function.\n* **Image.open() :** Utilizes a PIL class (`<module.pil.Image>`) to create an image object."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification.py:179-437

metadata:
{"docstring": "", "end_line": 437, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification.py", "name": "main", "start_line": 179, "type": "function", "chroma:document": "**Trainer Class in Hugging Face Transformers**\n\nBelow is a concise version of the Trainer class defined in the Hugging Face Transformers library.\n\n```python\nfrom transformers import AutoModelForImageClassification, AutoImageProcessor, Compose, RandomResizedCrop, Resize, CenterCrop, Lambda, Normalize, TimmWrapperImageProcessor\n\nclass Trainer:\n    \"\"\"\n        The :class:`~transformers.Trainer` class is used for training or fine-tuning a model.\n        \n        Args:\n            **args:  \n                See :func:`transformers.TrainerArgs`\n            \n            model (Model): Model to be trained\n            args (TrainingArguments): Training arguments\n            \n        Classes:\n            :: :class:`~transformers.data.Datasets`: Pre-computed datasets.\n           (`train_dataset`: The train data)\n        \"\"\"\n    def __init__(self, \n                 model: AutoModelForImageClassification,\n                 args: TrainingArguments = TrainingArguments(),\n                 \n                 # Dataset\n                train_dataset: Datasets = None,\n                eval_dataset: Optional[Datasets] = None,\n\n                 # Data processing\n                data_collator: Callable[[Sequence], Dict[str, Any]] = None,\n                \n                compute_metrics: Callable[[DataCollatorWithBatch], Metrics] = None,\n\n                # Training related\n                ignore_mismatched_sizes: bool = True,\n                \n                gradient_accumulation_steps: int = 1,\n\n                per_device_train_batch_size: int = 8,\n                per_device_eval_batch_size: Optional[int] = None,\n                per_device_predict batch_size: Optional[int] = None,\n                \n                max_grad_norm: float = 1.0,\n                \n                # Gradient Clipping\n                gradient_clip_value: float = None,  # default is set to infinity\n        \n        training_args=TrainingArguments(\n            save_on_each_node=True,   \n            load_best_model_at_end=False,\n            metric_for_best_model=\"_accuracy\",\n            greater_is_better=True,\n            num_train_epochs=int(args.train_args.num_train_epochs), \n            per_device_train_batch_size = args.per_device_train_batch_size if 'per_device_train_batch_size' in str(args) else 8,\n            per_device_eval_batch_size =   args.per_device_eval_batch_size if 'per_device_eval_batch_size' in str(args) else None,\n            per_device_predict_batch_size = 128, \n            total_train_steps = None,  \n            optimization_impl='AdamW',  \n                no_cuda = not has_cuda(),\n                seed= random.randint(42, 212),  \n        \n            save_total_limit=2,\n            warmup_steps=0 if args.dry_run else int(args.warmup_percentage * args.train_args.num_train_epochs),\n            weight_decay=args.weight_decay,\n            load_best_and_tray=False,\n        ):\n            \n        self.model = model\n        self.args = training_args\n        # Model configuration data\n        \n        self.image_processor= AutoImageProcessor.from_pretrained(\n                        training_args.image_processor_name or training_args.model_name_or_path,\n                         cache_dir=training_args.cache_dir, \n                          revision=training_args.model_revision\n \n            )\n        \n                 # Train data transformer\n        \n\n        if not \"train_dataset\" with train_dataset:\n          raise ValueError(\"--train_dataset is required\")\n        \n        self.train_dataset=train_dataset\n     \n        \n        self.data_collator = data_collator or Compose([\n                RandomResizedCrop(max_resolution=(224, 224)),\n                RandomHorizontalFlip(),\n                ToTensor()\n            ])\n    \n    \n            if data_args.max_train_samples is not None:\n              train_dataset=tuple([e[0]for e in \n                                   train_dataset[\n                                       \"train\"].shuffle(seed=training_args.seed)\n                                   .select(range(\n                                       min(\n                                           training_args.max_train_samples,\n                                       len(\n                                           dataset[\"train\"])\n                                       )\n                                       ))])\n\n        # Set the validation dataset and validation data processor\n        if eval_dataset is not None:\n            assert isinstance(eval_dataset, Datasets), 'Training arguments must contain \"train_dataset\", but received {type(eval_dataset)}'\n            \n            self.eval_dataset=eval_dataset\n            self.eval_data_collator=data_collator or Compose([\n                Resize((224, 224)),\n                CenterCrop(224),\n                ToTensor()\n            ])\n\n\n        # Initialize the Trainer instance\n\nif training_args.do_train\n```"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py:63-64

metadata:
{"docstring": "", "end_line": 64, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py", "name": "list_field", "start_line": 63, "type": "function", "chroma:document": "**Function Summary:** `\tlist_field`\n\n**Primary Purpose:**\nCreates and returns a Django admin field, leveraging the `field` function to initialize it with a default value and specified metadata.\n\n**Key Implementation Details:**\n\n*   Uses `default_factory=lambda: default` to set an attribute on the field instance.\n*   Passes `metadata=metadata` as a keyword argument to customize the field's metadata.\n\n**Notable Dependencies/Requirements:**\nNone explicitly stated, but it is assumed the `django.admin_fields` module or equivalent is imported."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py:332-371

metadata:
{"docstring": "", "end_line": 371, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py", "name": "create_vocabulary_from_data", "start_line": 332, "type": "function", "chroma:document": "**Concise Summary**\n\nCreates a vocabulary dictionary (`vocab_dict`) from the provided `datasets`. Maps each batch of dataset to extract unique characters in \"target_text\" and aggregates these unions. The resulting dictionary is populated with:\n\n* Unique character set (merged across all datasets)\n* Replaced whit space with delimiter token (`word_delimiter_token`)\n* Added unk token (`unk_token`)\n* Added pad token (`pad_token`) at the end\n\n**Key Implementation Details**\n\n* Maps datasets to extract unique character sets\n* Aggregates these character sets using functools.reduce\n* Populates vocabulary dictionary with sorted, indexed unique characters\n* Handles special tokens:\n  - Replaces whit space with `word_delimiter_token` if specified\n  - Adds `unk_token` and `pad_token` to the end of the dictionary if specified\n\n**Notable Dependencies**\n\n* ` datasets`: A DatasetDict object containing training and test labels\n* External Dependency: `fun\u03c4\u03bf\u03c2.reduce`\n\nNote: This summary provides a high-level overview of the function's purpose, implementation details, and dependencies. It aims to help developers quickly understand the functionality and key aspects of this code chunk in a RAG system."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py:374-818

metadata:
{"docstring": "", "end_line": 818, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py", "name": "main", "start_line": 374, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\nThis is a script for training and evaluating a Wav2Vec 2 model using the Hugging Face Transformers library. The script performs the following tasks:\n\n1. Loads the dataset, tokenizer, and processor configurations\n2. Creates a Trainer object with customizable arguments (e.g., data collator, model name)\n3. Trains the model on the training dataset if `do_train` is set to True\n4. Evaluates the model on the evaluation dataset if `do_eval` is set to True\n5. Saves the trained model and metrics\n6. Optionally pushes the trained model to Hugging Face Hub\n7. Creates a model card with metadata (e.g., dataset name, language)\n\nThe script uses various libraries and modules from Hugging Face and PyTorch, including `Trainer`, `DataCollatorCTCWithPadding`, `Processor`, and `Transformers`."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py:276-629

metadata:
{"docstring": "", "end_line": 629, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", "name": "main", "start_line": 276, "type": "function", "chroma:document": "This is the code for a speech recognition model, specifically an automatic speech recognition (ASR) system. The code is written in Python and uses the Hugging Face Transformers library.\n\nThe code consists of three main parts:\n\n1. **Data Preparation**: This section includes data loading, preprocessing, and loading a metric (WER, Word Error Rate) for evaluation.\n2. **Model Training**: This section trains the speech recognition model using the `Seq2SeqTrainer` class from Hugging Face's Transformers library. The training process involves:\n\t* Loading pre-trained models and tokens\n\t* Defining the custom data collator and processor\n\t* Initializing the trainer\n\t* Triggering the training loop\n3. **Evaluation**: This section runs the evaluation of the trained model on a test dataset using the previously loaded metric.\n4. **Model Saving and Storage**: The code saves the trained model, tokenizer, and configuration to a specified output directory.\n\nKey parameters and hyperparameters used in this code include:\n\n* `model_args`: training arguments that include input data, output directory, etc.\n* `training_args`: trainer-specific arguments that control the training process, such as device acceleration, gradient accumulation, etc.\n* `data_args`: dataset-related arguments, including dataset name, config name, and other metadata.\n\nKey libraries used in this code include:\n\n* Hugging Face's Transformers library for natural language processing tasks\n* PyTorch for deep learning and neural network operations\n\nOverall, this code is designed to train a speech recognition model on a specific dataset (e.g., LibriSpeech) using a combination of pre-trained models and custom data preprocessing. The resulting trained model can then be used for real-world speech recognition applications."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py:60-61

metadata:
{"docstring": "", "end_line": 61, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py", "name": "list_field", "start_line": 60, "type": "function", "chroma:document": "**Summary**\n\nThis is a Python function that creates a database model field with optional `default` and `metadata`. It uses Django's `field` function to create the field.\n\n**Key Details**\n\n* Primary purpose: Create a database model field\n* Implementation details:\n\t+ Uses Django's `field` function\n\t+ Sets a default value using a lambda function (`default_factory=lambda: default`)\n\t+ Passes metadata to the created field (`metadata=metadata`)\n* Notable dependencies:\n\t+ Django (the `field` function is part of its ORM)\n\n**RAG System Classification**\n\n* Tag: `ORM`\n* Category: `Model Field Creation`\n* Priority: H\n* Severity: M"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py:352-391

metadata:
{"docstring": "", "end_line": 391, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py", "name": "create_vocabulary_from_data", "start_line": 352, "type": "function", "chroma:document": "Here's a concise summary of the code:\n\n**Function Purpose:** Creates a vocabulary dictionary from a collection of datasets, allowing for flexible tokenization and padding.\n\n**Main Functionality:**\n\n1. Extracts unique characters from training and test data using `extract_all_chars` function.\n2. Applies filter to remove irrelevant columns from dataset.\n3. Combines character sets into a single set using `functools.reduce`.\n4. Replaces white space with delimiter token if specified, removes empty entry.\n5. Assigns special tokens (UNK and PAD) values based on usage.\n\n**Key Insights:**\n\n* Utilizes `DatasetDict` as input data source.\n* Incorporates string manipulation (`join`, `set`) for vocabulary generation.\n* Introduces custom token mapping through conditional statements.\n\n**Notable Dependencies/Requirements:**\n\n* `DatasetDict`\n* `functools.reduce`\n* String manipulation libraries (e.g., `str.join`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py:394-817

metadata:
{"docstring": "", "end_line": 817, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py", "name": "main", "start_line": 394, "type": "function", "chroma:document": "This code is for fine-tuning the Wav2Vec2 model on an automatic speech recognition task using the Hugging Face Transformers library. It consists of the following steps:\n\n1. Data loading and preprocessing\n2. Model initialization and configuration\n3. Training the model with a custom data collator\n4. Evaluation of the trained model\n5. Saving the model and its metrics, as well as pushing it to the HUGGINGFACE MODEL Hub.\n\nHere is a concise summary of the code:\n\n- **Main function**: This Python script uses the Transformers library to fine-tune the Wav2Vec2 model on an ASC-related data set, in order to develop a model for automatic speech recognition.\n- **Input files**: The input files required are a file named `data.json` and either a checkpoint filename or directory name specified as `model_name_or_path`, also containing the output path to be used for saving training artifacts. The code will load preprocessed audio data from an H5AR file, along with labels.\n- **Output**: The function returns the metrics produced during evaluation of the model in question. \n\n**Note:** This Python script does not take user input arguments. It uses default parameters specified within its environment (e.g., `data_args` dictionary), to configure training specifics such as the model architecture, hyperparameters and datasets used.\n\n```\nif __name__ == \"__main__\":\n    # ... function call with default parameters\n```\n\nThe output is also not provided explicitly in this script. However it's worth noting that you would typically log or display these results after computation."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model2.py:5-25

metadata:
{"docstring": "# Example where we only want to only modify the docstring", "end_line": 25, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model2.py", "name": "MyNewModel2Config", "start_line": 5, "type": "class", "chroma:document": "**Summary**\n\n`MyNewModel2Config` is a configuration class for `GemmaModel`, inheriting from `LlamaConfig`. It defines the model architecture and vocabulary size (default: 256000). The primary purpose of this class is to control the model outputs.\n\n\n**Key Implementation Details**\n\n* Vocabulary size (`vocab_size`): an integer parameter that defines the number of different tokens represented by `inputs_ids`.\n* Inheritance from `PretrainedConfig`: Configuration objects can be used to control model outputs.\n* Supported methods and interfaces:\n\t+ Conforming to `LlamaConfig`\n\t+ Extending `PretrainedConfig`\n\n**Notable Dependencies or Requirements**\n\n* The model is inspired by the Gemma-7B, reference to a specific pre-trained transformer model on Hugging Face.\n\nNote: I have preserved the exact class/function names and followed the technical terms used in the original code."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model2.py:29-30

metadata:
{"docstring": "# Example where alllllll the dependencies are fetched to just copy the entire class", "end_line": 30, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model2.py", "name": "MyNewModel2ForSequenceClassification", "start_line": 29, "type": "class", "chroma:document": "**Summary**\n\n`MyNewModel2ForSequenceClassification` is a subclass of `GemmaForSequenceClassification`, extending its functionality for sequence classification tasks.\n\n**Key Implementation Details**\n\n* Exposes no additional methods or attributes, passing through to the parent class (`GemmaForSequenceClassification`).\n\n**Notable Dependencies and Requirements**\n\n* Inherited from: `GemmaForSequenceClassification`\n* No external dependencies mentioned.\n           "}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal1.py:4-5

metadata:
{"docstring": "# Check that we can correctly change the prefix (here add Text part at the end of the name)", "end_line": 5, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal1.py", "name": "Multimodal1TextModel", "start_line": 4, "type": "class", "chroma:document": "**Summary**\n\nThis code defines a new subclass `Multimodal1TextModel` which inherits from `LlamaModel`. The class has no additional implementation, suggesting it may be intended as a placeholder or base model for text-specific multimodal applications.\n\n**Key Implementation Details**\n\n* Inherited interface: `LlamaModel`\n* No distinct methods or attributes implemented\n\n**Notable Dependencies or Requirements**\n\n* `LlamaModel`: The superclass from which this class inherits, indicating that the `Multimodal1TextModel` relies on the properties and behavior of its parent class.\n* External Dependencies: Not identified in this snippet.\n\n**Primary Purpose**\n\nTo establish a textual extension of an existing model framework (`LlamaModel`), potentially serving as a foundation for text-modality specific functionality or applications."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model.py:5-14

metadata:
{"docstring": "# Example where we only want to only add a new config argument and new arg doc\n# here there is no `ARG` so we are gonna take parent doc", "end_line": 14, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_my_new_model.py", "name": "MyNewModelConfig", "start_line": 5, "type": "class", "chroma:document": "**Summary**\n\n*Class: MyNewModelConfig*\n---------------------\n\nThe `MyNewModelConfig` class inherits from `LlamaConfig`. It introduces two primary parameters:\n\t+ `new_param`: An optional integer parameter with default value `False`.\n\t+ `mlp_bias`: A boolean parameter controlling the bias for multi-layer perceptrons.\n\n**Implementation Details**\n------------------------\n\n* Initializes `MyNewModelConfig` with `self` as super class reference, accepting keyword arguments through `super_kwargs`.\n\n**Notable Dependencies and Requirements**\n-----------------------------------------\n\n* The class uses inheritance from `LlamaConfig`, implying its requirement to have an environment that supports this base class.\n* External dependency: `LlamaConfig`.\n \nNo additional dependencies are assumed."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_model.py:5-34

metadata:
{"docstring": "", "end_line": 34, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_model.py", "name": "NewModelConfig", "start_line": 5, "type": "class", "chroma:document": "**Class Summary: `NewModelConfig`**\n\n*   **Purpose:** Configures the architecture of a new model using GemmaConfig as its base class.\n\n**Primary Functionality:**\nThe primary purpose of this class is to define and configure various hyperparameters for building a machine learning model, specifically a transformer-based architecture. These parameters include vocabulary size, hidden layers, attention heads, intermediate layers, activation functions, embedding dimensions, and more.\n\n**Implementation Details:**\n\n*   **Hyperparameter Tuning:** The configuration includes various tunable hyperparameters such as `vocab_size`, `hidden_size`, `intermediate_size`, `num_hidden_layers`, and others.\n*   **Attention Mechanism:** Key components like attention heads (`num_attention_heads`), intermediate layers, and embedding dimensions are defined to enable the model to focus on specific parts of the input data.\n\n**Notable Dependencies/Requirements:**\n\n*   Inherited from GemmaConfig: `super().__init__(self, **kwargs)`.\n*   Not explicitly listed but might be assumed or inherit from related classes/functions:\n    *   `vocab_size` might require access to a vocabulary or dictionary class.\n    *   `hidden_act` and `hidden_activation` could interact with related methods or functions for activation computations.\n\n**Key Methods:**\n\n*   `num_heads`: Returns the number of attention heads, which is stored in `self.num_attention_heads`.\n*   Other configuration values can be accessed as instance attributes (e.g., `self.vocab_size`, `self.hidden_size`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/image_processing_new_imgproc_model.py:36-286

metadata:
{"docstring": "", "end_line": 286, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/image_processing_new_imgproc_model.py", "name": "ImgprocModelImageProcessor", "start_line": 36, "type": "class", "chroma:document": "This is a PyTorch-based image processing class. The class seems to be designed for preprocessing and enhancing images in various ways, such as resizing, scaling, normalizing, and converting them to RGB format.\n\nHere's a high-level overview of the class:\n\n- The class has several attributes that determine how images are processed:\n  - `do_rescale`: Whether or not to rescale the pixel values.\n  - `rescale_factor`: The factor by which the pixel values should be scaled if rescaling is needed.\n  - `do_normalize`: Whether or not to normalize the pixel values.\n  - `image_mean` and `image_std`: Mean and standard deviation used for normalizing.\n  - `size`: The size at which images are resized (for downsampling).\n  - `resample`: The method used for resizing (e.g., nearest-neighbor, bicubic).\n- It also attributes and methods that handle errors and edge cases:\n  - `make_list_of_images` takes a collection of images as input.\n  - `validate_preprocess_arguments` checks if all preprocessings arguments are valid.\n  - It checks for invalid image type (i.e., PIL Image, np.ndarray, torch.Tensor, tf.Tensor or jax.ndarray).\n- The class processes preprocessed images by:\n  - Converting them to numpy arrays\n  - Rescaling and normalizing these arrays according to their respective settings.\n  - After preprocessing, it encodes the output in a format that can be consumed by a certain model.\n\nHere's a summary of what this code does:\n\n* Preprocesses input image(s) based on specified transformation settings\n* May rescale pixel values if necessary\n* Normalize pixel values with mean and standard deviation (if applicable)\n* Apply transformations like resizing or resampling if required\n\nPlease provide more context about where you'd like to improve this implementation, to give a clearer idea of what changes are acceptable and relevant."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:26-43
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "DummyRMSNorm", "start_line": 26, "type": "class", "chroma:document": "**Summary: DummyRMSNorm Module**\n\nThe `DummyRMSNorm` class is a PyTorch module equivalent to the T5LayerNorm. It normalizes input tensors with a scaling factor determined by the variance of the input.\n\n**Key Implementation Details:**\n\n* Scales input tensor by square root of mean-variance, modified by epsilon value.\n* Uses self-weight parameter to scale output.\n* Employs `pow` and `mean` operations over input tensor with shape `(batch_size, seq_len, hidden_size)`.\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch library (`torch`, `nn.Module`)\n* TensorFlow-compatible data types (`float32`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:46-108
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 108, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "DummyRotaryEmbedding", "start_line": 46, "type": "class", "chroma:document": "**Summary:**\n\nThe `DummyRotaryEmbedding` class is a PyTorch module implementing RoPE (Rotation Embeddings) layers. It adapts to dynamic sequence lengths and applies attention scaling.\n\n**Main Purpose and Functionality:**\n\nThis class is designed to compute frequency-based embeddings for input sequences, allowing the model to handle varying sequence lengths efficiently. The main functionality includes:\n\n* Adaptive initialization of `inv_freq` based on sequence length\n* Dynamic re-computation of `inv_freq` when sequence length grows or shrinks\n* Application of attention scaling factor to post-process embeddings\n\n**Key Implementation Details:**\n\n* Use of `torch.autocast` for dynamic torch device type management\n* Custom RoPE initialization function selection based on `rope_type`\n* Usage of `position_ids` for frequency-based embedding computation\n* Attention scaling factor application for post-processing embeddings\n\n**Notable Dependencies and Requirements:**\n\n* PyTorch module (`nn.Module`)\n* External dependencies:\n\t+ `DummyConfig` class (config passing)\n\t+ `ROPE_INIT_FUNCTIONS` dictionary (rope type initialization function selection)\n\n**Additional Notes:**\n\nThe RoPE implementation utilizes frequency-based embeddings for input sequences, enabling the model to adapt to varying sequence lengths while maintaining efficient computational complexity."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:111-124

metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "DummyMLP", "start_line": 111, "type": "class", "chroma:document": "**Class Summary: `DummyMLP`**\n\n**Main Purpose and Functionality:** \nThe `DummyMLP` class implements a basic multi-layer perceptron (MLP) architecture. It takes in input data `x`, applies actuarial function, combines with two projections (`up_proj` and `down_proj`) and outputs the result.\n\n**Key Implementation Details:**\n\n* Uses feed-forward neural network (`nn.Module`) for model construction.\n* Inclodes multiple linear transformations:\n\t+ `gate_proj`: projects input onto hidden layer\n\t+ `up_proj`: up-projects output from gateProj\n  - `down_proj`: Down-projects and combines with act_fn(x)\n* Utilizes an acturial function (`ACT2FN[config.hidden_act]`) to apply activation mapping.\n\n**Notable Dependencies or Requirements:**\n\n* Uses PyTorch's neural network module (`nn.Module`)\n* References external dictionary (`ACT2FN`)\n\nPlease let me know if you want me to clarify any details."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:127-131

metadata:
{"docstring": "", "end_line": 131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "rotate_half", "start_line": 127, "type": "function", "chroma:document": "**Summary**\n\nRotates the specified half of the hidden dimensions in input `x`. Specifically, it:\n\n* Splits the input into two halves using index division by 4\n* Rotates the second half (`x2`) to the beginning and keeps the first half (`x1`)\n* Concatenates the rotated halves with opposite signs using `torch cat`\n\n**Implementation Details**\n\n* Uses PyTorch tensor indexing and concatenation\n\n**Dependencies/Requirements**\n\n* Requires `torch` library for tensor operations."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:134-158

metadata:
{"docstring": "", "end_line": 158, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "apply_rotary_pos_emb", "start_line": 134, "type": "function", "chroma:document": "**Rotary Position Embedding Function Summary**\n\n### Purpose and Functionality\nApplies Rotary Position Embedding to query and key tensors.\n\n#### Key Implementation Details\n\n* Applies Rotatory Position Embedding using `cos(x) + rotate_half(sin(x))`\n* Uses cosine (`cos`) and sine (`sin`) components of the embedding as specified arguments.\n* Unsqueeze dimensions of `cos` and `sin` tensors for proper broadcasting with query (`q`) and key (`k`) tensors.\n\n#### Notable Dependencies\n\n* Inputs:\n\t+ `q`: Query tensor (torch.Tensor)\n\t+ `k`: Key tensor (torch.Tensor)\n\t+ `cos`, `sin`: Cosine and sine components of the embedding (torch.Tensor)\n\t+ `position_ids` (Deprecated, Unused): Optional position ID tensor\n\t+ `unsqueeze_dim`: Dimension to unsqueeze `cos[position_ids]` and `sin[position_ids]` for broadcasting\n* Returns: Tuples of rotated query (`q_embed`) and key (`k_embed`) tensors.\n\n#### Dependencies and Interfaces\n\n* No external dependencies.\n* Implies use of `torch.Tensor` type and vector operations."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:161-170

metadata:
{"docstring": "", "end_line": 170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "repeat_kv", "start_line": 161, "type": "function", "chroma:document": "**Summary:**\n\nThe `repeat_kv` function is equivalent to `torch.repeat_interleave`, repeating the `hidden_states` tensor along the key-value heads dimension (`dim=1`) with `n_rep` repetitions. It transforms the input from `(batch, num_key_value_heads, seqlen, head_dim)` to `(batch, num_attention_heads, seqlen, head_dim)`. The function is designed to handle multiple attention heads by repeating each head `n_rep` times.\n\n**Key details:**\n\n* Transform: Repeats along key-value heads dimension (`dim=1`)\n* Repeats: `n_rep` repetitions\n* Output shape: `(batch, num_attention_heads, seqlen, head_dim)`\n* Equivalent to: `torch.repeat_interleave(x, dim=1, repeats=n_rep)`\n\n**Notable dependencies/requirements:**\n\n* `torch`: Required for tensor operations and repeat functionality."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:173-196

metadata:
{"docstring": "", "end_line": 196, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "eager_attention_forward", "start_line": 173, "type": "function", "chroma:document": "**Concise Summary**\n\nThe `eager_attention_forward` function implements multi-head attention mechanism using eager attention. It takes in query, key, value tensors and an optional attention mask. The function:\n\n1. Scales the key-value pairs by a factor of 2^module.num_key_value_groups.\n2. Computes attention weights through matrix multiplication between query and scaled keys (dim = -1).\n3. Applies causal masking if provided.\n4. Softmax normalization and dropout are applied to the attention weights.\n5. Computes output by multiplying scaled weights with value tensor.\n6. Returns the output and scaled weights.\n\n**Key Implementation Details**\n\n* `torch.matmul` for matrix multiplication\n* `nn.functional.softmax` for softmax normalization\n* `nn.functional.dropout` for scaling weights\n\n**Dependences and Notes**\n\n* Inheritance: no external modules, only PyTorch's `nn.Module`\n* Interfaces: implements Multi-Head Attention (uses repeated key-value groups)\n* Scaling factor can be adjusted to the `module.num_key_value_groups`"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:199-272

metadata:
{"docstring": "", "end_line": 272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "DummyAttention", "start_line": 199, "type": "class", "chroma:document": "**Summary:**\n\nThe `DummyAttention` class implements multi-headed attention from the \"Attention Is All You Need\" paper. It extends PyTorch's `nn.Module` and is used in transformer architectures.\n\n**Key Features:**\n\n* Takes a configuration object, layer index, and input states (hidden_states) as inputs.\n* Projects hidden_states to query, key, and value vectors using linear layers.\n* Applies position embeddings for cosine and sine values.\n\n**Implementation Details:**\n\n* Uses the `eager` attention implementation by default or based on the `_attn_implementation` configuration.\n* Incorporates a past_key_value cache for efficient updates when caching positional information.\n* Projects outputs through an output projection layer (`o_proj`) to obtain attention scores.\n\n**Notable Dependencies and Requirements:**\n\n* PyTorch's `nn.Module`\n* `dummy_config` class (config object)\n* `torch.nn.Linear`, `torch.utils.data.Tensor`"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py:275-326

metadata:
{"docstring": "", "end_line": 326, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy.py", "name": "DummyDecoderLayer", "start_line": 275, "type": "class", "chroma:document": "**DummyDecoderLayer Class Summary**\n\n### Purpose and Functionality\nThe `DummyDecoderLayer` class implements a layer in a transformer decoder architecture. Its primary purpose is to perform self-attention and fully connected linear transformations on input embeddings.\n\n### Key Implementation Details\n\n* Performs self-attention using the `self_attn` method, which takes input embeddings, attention mask, and other optional arguments.\n\t+ Method Signature: `self.self_attn(hidden_states=..., attention_mask=..., ...)`\n\t+ Returns: `(hidden_states, self_attn_weights)`\n* Applies linear transformation using the `mlp` method, which takes input embeddings after self-attention.\n\t+ Method Signature: `self.mlp(hidden_states=...)`\n\t+ Returns: `(hidden_states)`\n* Normalizes inputs with layer normalization (`input_layernorm`) and post-attention normalization (`post_attention_layernorm`).\n\n### Notable Dependencies and Requirements\n\n* `config`: an instance of `DummyConfig`, which contains hyperparameters for the layer.\n* External dependencies:\n\t+ `torch`\n\t+ `nn.Module` (from PyTorch)\n\t+ `torch.Tensor`\n\t+ `Optional`\n\t+ `torch.LongTensor`\n\n### Inherited or Implemented Interfaces\n\n* Implements the `nn.Module` interface from PyTorch.\n\nNote: This summary focuses on the key implementation details and dependencies specific to the `DummyDecoderLayer` class, without providing unnecessary or generic information."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_imgproc_model.py:6-8

metadata:
{"docstring": "", "end_line": 8, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_imgproc_model.py", "name": "ImgprocModelImageProcessor", "start_line": 6, "type": "class", "chroma:document": "**Summary:**\n\nThis class, `ImgprocModelImageProcessor`, inherits from `BlipImageProcessor`. It defines a method `new_image_processing_method` which takes a `torch.FloatTensor` input `pixel_values` and returns the pixel values divided by 2. \n\n**Key Details:**\n\n- Algorithm: Simple Linear Scaling\n- Method Signature: `torch.FloatTensor -> torch.FloatTensor`\n- Interfaces: Inherited from `BlipImageProcessor`\n\n**Notable Dependencies/Requirements:** \n- `torch` tensor library"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_my_new_model.py:11-205

metadata:
{"docstring": "", "end_line": 205, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_my_new_model.py", "name": "MyNewModelConfig", "start_line": 11, "type": "class", "chroma:document": "This code defines the configuration for a custom transformer model called `MyNewModel`. It consists of various hyperparameters and settings, such as:\n\n* Model architecture (number of hidden layers, attention heads, intermediate size)\n* Training settings (vocab size, max position embeddings, initial optimizer range)\n* Hyperparameters (hidden act, beta test parameter)\n* Performance metrics and validation scheme\n* Output model type, key fields to ignore at inference, default tensor parallel plan\n\nThe code is intended for use with a transformer-based machine learning model, where the configuration defines how the model should be trained, processed, and evaluated. \n\nIt's often used in deep learning models such as those in NLP tasks like language translation or question-answering, although it is just a placeholder example of many more transformer based architectures that are built with the Hugging Face framework.\n\nOverall this code provides an object-oriented definition for how transformers should be constructed and configured to handle many aspects of their training and usage."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:35-96

metadata:
{"docstring": "", "end_line": 96, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertEmbeddings", "start_line": 35, "type": "class", "chroma:document": "**Summary:**\nThe `DummyBertEmbeddings` class is a PyTorch module responsible for constructing BERT-style embeddings from word, position, and token type inputs. It extends the PyTorch `nn.Module` class.\n\n**Purpose:** The main purpose of this class is to compute contextualized character-level representations by combining word embeddings with position and token type information.\n\n**Key Implementations:**\n* Embeds words using a learned embedding matrix (`word_embeddings`)\n* Apps `position_embeddings` from word embeddings based on `position_ids`\n* Applies `token_type_embeddings` using an embedding layer for token types\n* Employs multi-head attention mechanisms through the `LayerNorm` and `dropout` layers\n\n**Notable Dependencies:**\n* PyTorch library (specifically, `torch.nn`)\n* The Hugging Face Transformers library is referenced indirectly through its usage of `config` objects"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:99-230

metadata:
{"docstring": "", "end_line": 230, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertSelfAttention", "start_line": 99, "type": "class", "chroma:document": "**Class Summary**\n\n`DummyBertSelfAttention`\n======================\n\nA PyTorch implementation of the multi-head self-attention mechanism from the BERT model architecture.\n\n**Primary Purpose**\n-------------------\n\nThis class computes the attention scores and context layer for self-supervised learning or next sentence prediction tasks.\n\n**Key Implementation Details**\n-----------------------------\n\n* Uses a multihead decoder attention mechanism with dimensionality reduction (dot product).\n* Applies position embeddings for relative position encoding.\n* Supports different types of position embeddings: `absolute`, `relative_key`, and `relative_key_query`.\n* Masks heads using a provided head mask tensor, if applicable.\n\n**Notable Dependencies or Requirements**\n------------------------------------------\n\n* `torch`: PyTorch library with neural network module (`nn`).\n* `math.sqrt()`: Mathematical operations to compute square roots.\n* `nn.functional.softmax()` : Softmax activation function for normalizing attention scores.\n* `nn.Dropout()`: Dropout probability layer to prevent overfitting.\n\n**Method Summary**\n------------------\n\n### `__init__(config, position_embedding_type=None)` init\n\n Initializes the module with configuration and optional position embedding type.\n\n### `transpose_for_scores(x: torch.Tensor) -> torch.Tensor` transpose for scores\n\n Transposes input tensor for multihead self-attention mechanism computation.\n\n### `forward(hidden_states: torch.Tensor, ...)` forward pass\n\n Computes attention scores and context layer for self-supervised learning or next sentence prediction tasks.\n \n Returns a tuple of `(context_layer, attention_probs)` if `output_attentions` is True; otherwise returns only the first element."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:233-331

metadata:
{"docstring": "", "end_line": 331, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertSdpaSelfAttention", "start_line": 233, "type": "class", "chroma:document": "**Class Summary: DummyBertSdpaSelfAttention**\n\nThe `DummyBertSdpaSelfAttention` class extends the `DummyBertSelfAttention` class, inheriting its functionality. It is used to simulate the behavior of the transformers library's `scaled_dot_product_attention` function.\n\n**Primary Purpose**\n\nThe primary purpose of this class is to handle non-absolute position embeddings and support torch.compile's dynamic shapes for fast inference.\n\n**Key Implementation Details**\n\n*   Uses `torch.nn.functional.scaled_dot_product_attention` to compute attention outputs.\n*   Dispatches to SDPA's Flash Attention or Efficient kernels via an `is_causal` if statement.\n*   Applies contouring operations for efficient computation on non-contiguous inputs.\n*   Handles cross-attention and prefix tuning support.\n\n**Notable Dependencies and Requirements**\n\n*   Depends on the `DummyBertSelfAttention` class and uses its `forward` method as a base implementation.\n*   Requires torch 2.2.0 or later due to fixes in SDPA's Flash Attention backend.\n\n**(External References)**\n\n-   `torch.nn.functional.scaled_dot_product_attention`\n-   ` DummyBertSelfAttention` class\n-   `config.attention_probs_dropout_prob`\n\nNote: The exact reference to the corresponding Transformers library function mentioned above can be found at :https://github.com/huggingfacetransformers/Transformers"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:334-345

metadata:
{"docstring": "", "end_line": 345, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertSelfOutput", "start_line": 334, "type": "class", "chroma:document": "**Summary**\n\nThis is a PyTorch implementation of a self-output transformation module for BERT, inspired by the Transformer architecture. The `DummyBertSelfOutput` class transforms the input-hidden-states tensor using a linear layer (`self.dense`), dropout regularization (`self.dropout`), and layer normalization (`self.LayerNorm`). The primary purpose is to normalize and conditionally scale the hidden states before outputting them.\n\n**Key Implementation Details**\n\n1. Linear transformation: `self.dense(nn.Linear(config.hidden_size, config.hidden_size))`\n2. Dropout regularization: `self.dropout(config.hidden_dropout_prob)`\n3. Layer Normalization: `self.LayerNorm(config.layer_norm_eps)`\n\n**Notable Dependencies and Requirements**\n\n* PyTorch (`torch`)\n* Hugging Face Transformer library\n* BERT configuration (`config`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:354-402

metadata:
{"docstring": "", "end_line": 402, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertAttention", "start_line": 354, "type": "class", "chroma:document": "**Concise Summary:**\n\nThe `DummyBertAttention` class implements the BERT attention mechanism. It uses a `SelfAttention` module to compute attention weights and a `SelfOutput` module to apply the learned embedding.\n\n**Key Implementation Details:**\n\n*   **Attention Mechanism:** The `prune_heads` method applies attention head pruning based on the provided heads, updating hyperparameters accordingly.\n*   **Forward Method:** The `forward` method first computes attention outputs using `self.self` and then passes them through `self.output`.\n*   **Notable Dependencies:**\n\n    *   `DummyBertSelfAttention_CLASSES`: A mapping class for BERT attention classes.\n    *   `DummyBertSelfOutput`: A module for self-output operation.\n\n**External Dependencies:**\n\n*   `torch` library\n*   `find_pruneable_heads_and_indices`, `prune_linear_layer`, and other utility functions in the codebase."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:405-417

metadata:
{"docstring": "", "end_line": 417, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertIntermediate", "start_line": 405, "type": "class", "chroma:document": "**Summary**\n\nThe `DummyBertIntermediate` class inherits from PyTorch's `nn.Module`. It implements the intermediate layer of BERT, comprising:\n\n* A linear dense layer (`self.dense`) with an input size of `config.hidden_size` and output size of `config.intermediate_size`.\n* If `config.hidden_act` is a string, it calls the corresponding activation function from the `ACT2FN` dictionary. Otherwise, it uses `config.hidden_act` as the activation function (`self.intermediate_act_fn`).\n* The forward pass applies self.dense to input `hidden_states`, followed by the application of `self.intermediate_act_fn`.\n\n**Key Details**\n\n* Input size: `config.hidden_size`\n* Output size: `config(intermediate_size)`\n* Activation function:\n\t+ If string, uses dictionary lookup\n\t+ Otherwise, uses `config.hidden_act`\n\n**Dependencies & Requirements**\n\nNone explicitly mentioned. However, it's implied that `config` object contains the necessary configuration parameters (`hidden_size`, `intermediate_size`, and `hidden_act`). Additionally, PyTorch's neural network module (`nn.Module`) is required."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:420-431

metadata:
{"docstring": "", "end_line": 431, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertOutput", "start_line": 420, "type": "class", "chroma:document": "**Summary**\n\nThe `DummyBertOutput` class is an implementation of a PyTorch neural network module (`nn.Module`) designed to mimic the output layer of BERT (Bidirectional Encoder Representations from Transformers). It takes two inputs: `hidden_states` and `input_tensor`.\n\n**Primary Purpose**\nThe main purpose of this class is to process `hidden_states` using a linear transformation, followed by a dropout regularization, and finally applying layer normalization with an additional input adjustment.\n\n**Key Implementation Details**\n\n*   Linear Transformation (`self.dense`)\n    *   Input: `hidden_states` (torch.Tensor)\n    *   Output: transformed tensor\n*   Dropout Regularization (`self.dropout`)\n    *   Input: transformed tensor from linear transformation\n    *   Output: regularized tensor after dropout\n*   Layer Normalization (`self.LayerNorm`)\n    *   Input: combination of output from linear transformation, dropout regularization, and input_tensor\n    *   Output: normalized tensor\n\n**Notable Dependencies**\n\n*   `nn.Module` (PyTorch neural network module)\n*   `torch.Tensor`\n*   `nn.Linear`, `nn.Dropout`, and `nn.LayerNorm` modules ( PyTorch linear and normalization layers)\n\n**No external dependencies listed.**"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:434-517

metadata:
{"docstring": "", "end_line": 517, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertLayer", "start_line": 434, "type": "class", "chroma:document": "**Class Summary: DummyBertLayer**\n\nThe `DummyBertLayer` class represents a single layer in the BERT architecture. It is a PyTorch module that utilizes multi-head self-attention and feed-forward networks.\n\n**Primary Purpose:** To process input data through self-attention and feed-forward networks, producing output features.\n\n**Key Implementation Details:**\n\n*   The `forward` method processes input tensors for self-attention and cross-attention. It returns a tuple of output features.\n*   Two critical components are the `DummyBertAttention` and `DummyBertIntermediate` modules, which implement attention mechanisms and feed-forward networks, respectively.\n\n**Notable Dependencies or Requirements:**\n\n*   Uses `torch.Tensor`, `Optional[ torch.FloatTensor]`, and other PyTorch-related classes/types for tensor operations.\n*   Incorporates dependency on `DummyBertAttention` and `DummyBertIntermediate` modules (not shown in this code snippet)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:520-610

metadata:
{"docstring": "", "end_line": 610, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertEncoder", "start_line": 520, "type": "class", "chroma:document": "**Concise Summary:**\n\nThe `DummyBertEncoder` class is an encoder layer for the BERT model, implementing the `nn.Module` interface. It takes in various input tensors (`hidden_states`, `attention_mask`, etc.) and transforms them through self-attention mechanisms.\n\n**Key Implementation Details:**\n\n* The encoder consists of multiple identical `DummyBertLayer` instances wrapped within a `ModuleList`.\n* Each layer runs through a forward pass, applying self-attention to the input `hidden_states`. If gradient checkpointing is enabled, it falls back to a fallback implementation.\n* After each layer's forward pass, it updates the cache and attentions outputs.\n\n**Notable Dependencies and Requirements:**\n\n* The class depends on `nn.Module`, `torch.Tensor`, and various PyTorch modules (e.g., `nn.ModuleList`, `Optional`).\n* It uses `logger.warning_once` for logging warnings related to gradient checkpointing.\n* The class implements the `BaseModelOutputWithPastAndCrossAttentions` interface, which suggests an integration with larger PyTorch-based models.\n* External dependencies are implicitly required through the use of PyTorch modules and classes."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:613-625

metadata:
{"docstring": "", "end_line": 625, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertPooler", "start_line": 613, "type": "class", "chroma:document": "**Summary:**\n\n`DummyBertPooler` is a PyTorch neural network module implementing a simplified BERT pooler. It takes in `hidden_states` and outputs the pooled representation of the first token using a linear transformation (`self.dense`) followed by the Tanh activation function (`self.activation`). \n\n**Key Details:**\n- Takes `config.hidden_size` as input and output.\n- Utilizes `nn.Linear` for linear transformation and `nn.Tanh` for activation function.\n\n**Dependencies:** \n- References `torch.Tensor`\n- Inherited interfaces: `nn.Module`, `nn.Linear`\n\n**Notable Requirements:**\n- Inherits from PyTorch's neural network module (`nn.Module`)\n- Import of specific activation functions (`nn.Tanh`) and linear transformation (`nn.Linear`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:628-698

metadata:
{"docstring": "", "end_line": 698, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "load_tf_weights_in_dummy_bert", "start_line": 628, "type": "function", "chroma:document": "**Summary: Loading TensorFlow Weights in PyTorch Model**\n\n```python\ndef load_tf_weights_in_dummy_bert(model, config, tf_checkpoint_path):\n    \"\"\"\n    Load pre-trained TensorFlow weights into a PyTorch model.\n\n    Args:\n    - model (PyTorch model): The model to load weights into.\n    - config: (not used)\n    - tf_checkpoint_path: Path to the TensorFlow checkpoint file.\n\n    Returns:\n    - The loaded PyTorch model with pre-trained weights.\n    \"\"\"\n\n    **Implementation Details**\n\n    1. Loads TensorFlow checkpoints using `tf.train.list_variables` and `tf.train.load_variable`.\n    2. Skips certain variables (e.g., `adam_v`, `adam_m`) that are not required for using the pre-trained model.\n    3. Uses NumPy to transpose array `array` when its name ends with `_embeddings`.\n    4. Checks shape mismatch between `pointer` and `array` using `ValueError`.\n\n**Design Choice:**\n\n* The function uses a pointer-based approach to navigate the PyTorch model's namespace.\n* This design choice enables efficient loading of TensorFlow weights into the PyTorch model.\n\n**Dependencies:**\n\n* TensorFlow (`tf`)\n* NumPy (`np`)\n* PyTorch (`torch`)\n\n**Interfaces:**\n\n* `model`: The PyTorch model to load weights into (instance-specific).\n* `logger`: A logging interface for reporting progress and errors (not shown in the code snippet)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py:701-727

metadata:
{"docstring": "", "end_line": 727, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_dummy_bert.py", "name": "DummyBertPreTrainedModel", "start_line": 701, "type": "class", "chroma:document": "**Summary:**\n\n`DummyBertPreTrainedModel`: An abstract class implementing a simplified interface for downloading and loading pre-trained models.\n\n**Main Purpose and Functionality:**\nThis class provides a basic framework for handling weights initialization, allowing users to download and load pre-trained models.\n\n**Key Implementation Details:**\n\n* Initializes model weights using a normal distribution with mean 0.0 and standard deviation `config.initializer_range`.\n* Handles initialization for linear layers, embedding tables, and layer normalization modules.\n* Copies behavior from the PyTorch `load_tf_weights_in_dummy_bert` function.\n\n**Notable Dependencies or Requirements:**\n\n* `DummyBertConfig`: Config class for this model (not shown in this snippet).\n* `nn.Linear`, `nn.Embedding`, `nn.LayerNorm`: PyTorch neural network module classes.\n* `load_tf_weights_in_dummy_bert`: Function imported from an external library, used for loading TensorFlow weights.\n\n**Interface Inheritance:**\nInherits from `PreTrainedModel`, providing a simplified interface for downloading and loading pre-trained models."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_new_model.py:11-132

metadata:
{"docstring": "", "end_line": 132, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_new_model.py", "name": "NewModelConfig", "start_line": 11, "type": "class", "chroma:document": "Here is a concise and focused summary of the class code chunk:\n\n**Class:** `NewModelConfig`\n**Purpose:** The configuration class for storing parameters defining a [`NewModelModel`].\n\n**Main Functionality:**\nThis class enables instantiation of a `NewModel` model according to specified arguments, including vocabulary size, hidden dimensions, number of layers, attention heads, and other hyperparameters.\n\n**Key Implementation Details:**\n\n* **Attributes:** Vocabulary size (`vocab_size`), hidden dimension sizes (`hidden_size`, `intermediate_size`), number of layers (`num_hidden_layers`), attention head count (`num_attention_heads`), head dimension (`head_dim`), activation functions (`hidden_act`, `hidden_activation`), and other hyperparameters.\n* **Constructor:** Initializes the class with an optional dictionary of hyperparameters.\n\n**Notable Dependencies and Requirements:**\n\n* Inherits from [`PretrainedConfig`]\n* Uses external dependencies for weight initialization (e.g., truncated normal initializer)\n* Implements Grouped Query Attention and RoPE embeddings using hyperparameter settings (`num_key_value_heads`, `rope_theta`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_super.py:11-38
encoding: FLOAT32
metadata:
{"docstring": "# example where we need some deps and some functions", "end_line": 38, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_super.py", "name": "SuperModel", "start_line": 11, "type": "class", "chroma:document": "**Class Summary: `SuperModel`**\n\n### Main Purpose and Functionality\n\nThe `SuperModel` class extends `LlamaModel` and inherits its functionality. It provides an improved forward pass that includes additional computations.\n\n### Key Implementation Details\n\n* **Forward pass**: The model's input is passed through a modified call to the parent class's `forward` method.\n* **Linear transformation**: The output logits are scaled by a factor of 2^4 (16).\n\n### Notable Dependencies and Requirements\n\nThe following dependencies and interfaces are used in this code:\n\n* `torch`: PyTorch library for tensor operations\n* `LlamaModel`: Parent class with its own `forward` method implementation\n* `Cache`, `List[torch.FloatTensor]`: Optional types in the PyTorch library\n* `CausalLMOutputWithPast`: Expected return type of the `forward` method\n\n### Interface and Inheritance\n\nThe `SuperModel` class inherits from `LlamaModel`, suggesting that it is designed to build upon or extend the parent class's functionality.\n\n### External Dependencies (none mentioned in this code snippet)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_new_task_model.py:68-76

metadata:
{"docstring": "", "end_line": 76, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_new_task_model.py", "name": "NewTaskModelMultiModalProjector", "start_line": 68, "type": "class", "chroma:document": "**Summary**\n\n`NewTaskModelMultiModalProjector`: A multi-modal projector class extending PyTorch's `nn.Module`. This model projects input features from various modalities onto a shared high-dimensional space.\n\n**Key Details:**\n\n* **Forward Pass**: Takes in image features, applies a linear transformation, and returns the projected output.\n\t+ Input: `image_features`\n\t+ `linear` = `nn.Linear(config.vision_config.hidden_size, config.vision_config.projection_dim, bias=True)`\n* **Configured Linear Layer**: A fully connected layer with:\n\t+ Input dimension equal to `config.vision_config.hidden_size`\n\t+ Output dimension equal to `config.vision_config.projection_dim`\n\t+ Includes bias term\n* **No External Dependencies**\n\nThis summary highlights the primary purpose and key implementation details of the class, including its forward pass, configured linear layer, and lack of external dependencies."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy.py:5-9
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 9, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy.py", "name": "rotate_half", "start_line": 5, "type": "function", "chroma:document": "**Summary:**\n\nRotates half of the hidden dimensions in the input tensor `x` by shifting and concatenating two adjacent segments.\n\n**Key Details:**\n\n* Rotates only half of the hidden dimensions (top 4 dimensions).\n* Shifts top part to bottom and vice versa using concatenation:\n\t+ `x1 = x[..., : x.shape[-1] // 4]`\n\t+ `-x2 = -x[..., x.shape[-1] // 4 :]`\n* Uses PyTorch tensor manipulation, specifically `torch.cat`.\n\n**Notable Dependencies:**\n\n* Requires PyTorch library (`import torch`)\n* Assumes input tensor `x` has shape that supports splitting along the last dimension."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy.py:13-14
encoding: FLOAT32
metadata:
{"docstring": "# example where we need some deps and some functions", "end_line": 14, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy.py", "name": "DummyModel", "start_line": 13, "type": "class", "chroma:document": "**Summary**\n\nThis Java code defines a `DummyModel` class that extends the `LlamaModel` class. The main purpose of this class is to serve as a placeholder or example model.\n\n**Key Implementation Details:**\n\n* Extends `LlamaModel`, indicating inheritance of methods and properties.\n* An empty class (`pass`), suggesting no specific implementation or functionality added on top of the base class.\n\n**Notable Dependencies/Requirements:**\n\n* References the `LlamaModel` class, implying that it relies on some functionality provided by this parent class.\n\n**Additional Information:**\nThis code may be part of a larger model or library system where custom models are defined to interact with LlamaModel."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_roberta.py:5-11
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 11, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_roberta.py", "name": "RobertaEmbeddings", "start_line": 5, "type": "class", "chroma:document": "**Class Summary: RobertaEmbeddings**\n\nThe `RobertaEmbeddings` class extends the `BertEmbeddings` base class and is used for obtaining embeddings from input sequences in a Hugging Face's transformers library.\n\n**Primary Purpose:** To compute sequence embeddings using a custom embedding layer, inheriting properties from the parent `BertEmbeddings` class.\n\n**Key Implementation Details:**\n\n* Initializes the embedding layer with a specific configuration (`config.max_position_embeddings`, `config.hidden_size`, and `config.pad_token_id`)\n* Utilizes an `nn.Embedding` module to store position embeddings\n\n**Notable Dependencies/Requirements:**\n\n* Inherits from `BertEmbeddings`, utilizing its properties\n* Depends on the Hugging Face's transformers library for the embedding layer (`nn.Embedding`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_roberta.py:14-16
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 16, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_roberta.py", "name": "RobertaModel", "start_line": 14, "type": "class", "chroma:document": "**Summary: `RobertaModel` Class**\n\n### Purpose and Functionality:\n\nThe `RobertaModel` class is an extension of the `BertModel`, inheriting its functionality. It is designed to accommodate the Roberta language model architecture, allowing for efficient generation and prediction tasks.\n\n### Key Implementation Details:\n\n- Initializes with a `config` dictionary and optionally adds a pooling layer (`add_pooling_layer=True`)\n- Calls the parent class's (`BertModel`) initializer with the inherited parameters\n\n### Dependencies and Requirements:\n\n- Inherited from `BertModel`\n- Extends Roberta language model architecture"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy_bert.py:9-26

metadata:
{"docstring": "", "end_line": 26, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_dummy_bert.py", "name": "DummyBertModel", "start_line": 9, "type": "class", "chroma:document": "**Class Summary: DummyBertModel**\n\nThe `DummyBertModel` class is a subclass of `BertModel` that overrides the `forward` method to simplify its behavior.\n\n**Primary Purpose:**\nThe model's primary purpose is to forward input tensors through a basic BERT-like processing pipeline, effectively reducing its functionality by always returning `super().forward(input_ids)`.\n\n**Key Implementation Details:**\n\n*   **Method Signature:** The `forward` method accepts a variety of optional input tensors and returns either a tensor or a `BaseModelOutputWithPoolingAndCrossAttentions` object.\n*   **Inherited Behavior:** The model calls the parent class's `forward` method directly with only the `input_ids` argument, implying that all other arguments are ignored.\n\n**Notable Dependencies:**\n\n*   `BertModel`\n*   `torch`\n        *   External Dependency: `torch`\n\n**Interfaces:**\n\n*   Inherited from `BertModel`: `BaseModelOutputWithPoolingAndCrossAttentions`."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:36-137

metadata:
{"docstring": "", "end_line": 137, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionAttention", "start_line": 36, "type": "class", "chroma:document": "**Class Summary: Multimodal2VisionAttention**\n=============================================\n\n*   **Main Purpose and Functionality:** This class implements the Multi-Head Attention mechanism as described in the \"Attention Is All You Need\" paper. It takes in input `hidden_states` and optional masks (`attention_mask` and `causal_attention_mask`) to compute attention weights and outputs.\n*   **Key Implementation Details:**\n    *   Uses a linear transformation followed by a scaling factor to project queries, keys, and values.\n    *   Computes attention weights using a dot product and softmax function.\n    *   Applies dropout to the attention weights.\n    *   Transposes and reshapes the output for broadcasting.\n*   **Notable Dependencies or Requirements:**\n    *   `config`: Configuration object containing hidden size, number of heads, and attention dropouts.\n    *   `nn.Module`: Base PyTorch module class.\n    *   `torch.Tensor`: PyTorch tensor class.\n    *   `nn.functional`: PyTorch neural network functional module.\n\n**Method Signature:**\n```python\nforward(hidden_states: torch.Tensor, \n         attention_mask: Optional[torch.Tensor] = None, \n         causal_attention_mask: Optional[torch.Tensor] = None, \n         output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor, Optional[torch.Tensor]]\n```\n\n**Example Use Case:**\n```python\nmultimodal2vision_attention = Multimodal2VisionAttention(config)\nhidden_states = ...\nattention_mask = ...\n\nattn_output, attn_weights = multimodal2vision_attention(hidden_states, attention_mask, None, False)\n```"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:140-210

metadata:
{"docstring": "", "end_line": 210, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionSdpaAttention", "start_line": 140, "type": "class", "chroma:document": "**Multimodal2VisionSdpaAttention Class Summary**\n\n**Main Purpose and Functionality:**\nThe `Multimodal2VisionSdpaAttention` class is an attention module derived from `Multimodal2VisionAttention`, utilizing the `torch.nn.functional.scaled_dot_product_attention` function. It adapts the forward pass to work with the SDPA API.\n\n**Key Implementation Details:**\n\n*   Implements scaled dot-product attention using `torch.nn.functional.scaled_dot_product_attention`.\n*   Handles both `causal_attention_mask` and `attention_mask` in the input.\n*   Performs dimension-wise transposition of query, key, and value states before passing them to the scaled dot-product attention function.\n*   Applies an optional scaling factor (`self.scale`) to the attention output.\n\n**Notable Dependencies or Requirements:**\n\n*   `torch.nn.functional.scaled_dot_product_attention`\n*   `Multimodal2VisionAttention` class (with unchanged weights)\n*   `torch` version 2.1.2+ for memory-efficient backend support\n*   `self.dropout`, `self.scale`, and other attributes from `Multimodal2VisionAttention`\n\n**Interface Summary:**\n\nThe class has the following interface:\n\n*   `forward(hidden_states, attention_mask, causal_attention_mask, output_attentions) -> (attn_output, None)`\n    *   Returns the scaled dot-product attention output.\n    *   If `output_attentions` is set to `True`, falls back to a manual attention implementation."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:213-296

metadata:
{"docstring": "", "end_line": 296, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionFlashAttention2", "start_line": 213, "type": "class", "chroma:document": "**Class Summary: Multimodal2VisionFlashAttention2**\n\nThe `Multimodal2VisionFlashAttention2` class is an extension of the `Multimodal2VisionAttention` class, integrating Flash Attention. It maintains the same weights but adapts to Flash Attention's requirements.\n\n**Key Implementations:**\n\n1. **Forward Pass**: The forward method takes in input tensors and applies Flash Attention.\n   - `_flash_attn_uses_top_left_mask`: A flags whether Flash Attention uses top-left aligned causal mask.\n   - `attention_mask`, `causal_attention_mask`, and `output_attentions` are optional parameters.\n\n2. **Query, Key, and Value Projection**: The `q_proj`, `k_proj`, and `v_proj` methods project the input tensors to compute query states, key states, and value states.\n\n3. **Dropout Rate**: The `dropout_rate` is calculated from the model configuration or set to 0 during inference.\n\n**Notable Dependencies and Requirements:**\n\n- `torch`: Library for tensor computations.\n- Other TensorFlow models (e.g., ` transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:299-311

metadata:
{"docstring": "", "end_line": 311, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionMLP", "start_line": 299, "type": "class", "chroma:document": "**Multimodal2VisionMLP Class Summary**\n\n* **Main Purpose**: A multimodal fusion neural network that leverages multilayer perceptrons (MLPs) for vision processing.\n\nPrimary Functionality:\nThe `Multimodal2VisionMLP` class implements a multilayer perceptron-based neural network for vision processing, utilizing the provided configuration and activations function.\n\n**Key Implementation Details**:\n\n* **Activation Functions**: Utilizes `ACT2FN[config.hidden_act]` activation function.\n* **Layers**: Two fully connected (`fc1` and `fc2`) layers with sizes defined by `config.hidden_size` and `config.intermediate_size`.\n* **Forward Pass**: The input tensor is passed through the first linear layer, then undergoes activation, followed by the second linear layer.\n\n**Dependencies/Requirements**:\n\n* References `torch` library for tensor operations.\n* Inherents from `nn.Module` class for neural network architecture."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:321-368

metadata:
{"docstring": "", "end_line": 368, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionEncoderLayer", "start_line": 321, "type": "class", "chroma:document": "**Multimodal2VisionEncoderLayer Class Summary**\n\n### Purpose and Functionality\n\nThe `Multimodal2VisionEncoderLayer` class is a module in a transformer encoder architecture, designed to process multimodal input data. It takes in sequential data of embedding dimension `config.hidden_size`, applies self-attention mechanisms, and follows up with a feed-forward neural network (FFNN) block.\n\n### Key Implementation Details\n\n* The layer consists of:\n\t+ Two layer normalization layers (`layer_norm1` and `layer_norm2`) applied to the input data.\n\t+ A self-attention mechanism (`self_attn`) implemented using `MULTIMODAL2_VISION_ATTENTION_CLASSES[config._attn_implementation]`.\n\t+ An FFNN block (`mlp`) after the second layer normalization.\n* The forward pass involves:\n\t1. Applying the first self-attention mechanism and layer normalization.\n\t2. Residual addition of the input data after the first layer normalization.\n\t3. Repeating steps 1-2 for the second layer normalization and FFNN block.\n\n### Notable Dependencies and Requirements\n\n* `MULTIMODAL2_VISION_ATTENTION_CLASSES[config._attn_implementation]`: a class implementing the attention mechanism based on `config._attn_implicit`.\n* Torch library (`torch.Tensor`, `nn.Module`) for building and manipulating neural networks.\n* Optional dependency: the `Optional` class from PyTorch for handling boolean inputs.\n\n### Method Signatures and Interfaces\n\n* The primary method of interest is `forward`, which takes in input tensors (`hidden_states`, `attention_mask`, `causal_attention_mask`) and optional inputs (`output_attentions`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:371-465

metadata:
{"docstring": "", "end_line": 465, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionEncoder", "start_line": 371, "type": "class", "chroma:document": "**Summary**\n\nThe `Multimodal2VisionEncoder` class is a transformer encoder consisting of multiple attention layers. It takes in input embeddings and an optional batch of attention masks. The primary purpose is to process sequential data using self-attention mechanisms.\n\n**Key Implementation Details**\n\n*   `forward` method: The main entry point for processing input data.\n    *   Applies multiple `[Multimodal2VisionEncoderLayer](https://huggingface.co/transformers/_modules Transformers/modeling_multimodal/vision_encoder_layer.html)()` instances in sequence, with optional gradient checkpointing enabled.\n*   **`AttentionMask` and `CausalAttentionMask`**: Optional input masks to avoid performing attention on padding token indices.\n*   **Output Options**:\n    *   `[output_attentions](https://huggingface.co/transformers/_modules Transformers/utils/modeling_utils.html#ModelConfig.get_output_attentions())`: Controls whether to return attentions tensors of all attention layers.\n    *   `[output_hidden_states](https://huggingface.co/transformers/_modules Transformers/utils/modeling_utils.html#ModelConfig.get_output_hidden_states())`: Controls whether to return hidden states of all layers.\n\n**Notable Dependencies and Requirements**\n\n*   `nn.ModuleList` and `Multimodal2VisionEncoderLayer` from PyTorch Transformers module.\n*   `torch.Tensor` for input embeddings, attention masks, and output tensors."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:468-548

metadata:
{"docstring": "", "end_line": 548, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionEmbeddings", "start_line": 468, "type": "class", "chroma:document": "**Multimodal2VisionEmbeddings Class Summary**\n\n**Main Purpose and Functionality**\n\nThis class is designed for multimodal 2 Vision Embeddings, allowing the model to integrate visual and textual inputs. It maps input pixels to dense embeddings by applying convolutional patches and position encodings.\n\n**Key Implementation Details**\n\n* The `Multimodal2VisionEmbeddings` class maps input pixels to embeddings using a convolutional patch embedding layer (`self.patch_embedding`) and position encoding layer (`self.position_embedding`).\n* The `interpolate_pos_encoding` method allows for interpolation of pre-trained position encodings, which enables the model to handle dynamic input shapes.\n* The `forward` method combines patch embeddings with class embeddings and optionally applies position or interpolated position encodings.\n\n**Notable Dependencies and Requirements**\n\n* `nn.Module`: serves as the base class for building neural networks\n* `torch.randn`, `torch.Embedding`, `nn.Conv2d`, `nn.Parameter`: build various layers\n* `torch.jit.is_tracing()` and `torch.functional.interpolate`: require PyTorch tracing support and bicubic interpolation, respectively.\n\n**Notable Interfaces and Class Variables**\n\n* `config` (required): holds configuration settings from the `Multimodal2VisionConfig`\n* `self.num_patches`, `self.position_ids`: buffer variables for storing intermediate positions.\n* `self.patch_embedding`, `self.class_embedding`, `self.position_embedding`: neural network layers."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:569-625

metadata:
{"docstring": "", "end_line": 625, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionTransformer", "start_line": 569, "type": "class", "chroma:document": "**Multimodal2VisionTransformer Class Summary**\n\nThe `Multimodal2VisionTransformer` class is a PyTorch-based transformer model designed for multimodal processing.\n\n* **Primary Purpose**: This model processes video and text inputs from different modalities to generate visual outputs.\n* **Key Implementation Details**:\n\t+ The model consists of an encoding block (`Multimodal2VisionEncoder`) followed by a pooling layer.\n\t+ The `Multimodal2VisionEmbeddings` component is used to embed input data from both modalities (video and text).\n\t+ The `pre_layrnorm` and `post_layernorm` layers are applied to the embedded data to normalize it.\n* **Notable Dependencies**:\n\t+ `nn.Module`: Base PyTorch model class.\n\t+ `Multimodal2VisionEncoder`: Custom encoder component for multimodal processing.\n\t+ `Multimodal2VisionEmbeddings`: Custom embedding layer for multimodal input data.\n\n**Method Signature Summary**\n\nThe `forward` method takes in the following inputs:\n\n* `(pixel_values, ..., interpolate_pos_encoding)`\n\t+ `pixel_values`: Input video data (torch.FloatTensor).\n\t+ `output_attentions`, `output_hidden_states`, and `return_dict`: Flags controlling output formats.\n\t+ `interpolate_pos_encoding`: Flag for position encoding interpolation.\n\nThe method returns a tuple containing:\n\n* `(last_hidden_state, pooled_output) + encoder_outputs[1:]` when not returning in a dictionary format."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py:628-643

metadata:
{"docstring": "", "end_line": 643, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal2.py", "name": "Multimodal2VisionPreTrainedModel", "start_line": 628, "type": "class", "chroma:document": "**Summary:**\n\nThe `Multimodal2VisionPreTrainedModel` class is an abstract base class for pre-trained models. It provides functionality for downloading and loading pretrained models. The primary purpose is to handle weights initialization.\n\n**Key Implementation Details:**\n\n* Initializes weights using the `_init_weights` method.\n* Specifically initializes `Multimodal2VisionMLP` modules, but does not perform any operation (pass statement).\n\n**Notable Dependencies or Requirements:**\n\n* No external dependencies are referenced by exact name in this code snippet.\n* The following classes/interfaces are implied to be part of the larger architecture:\n\t+ `PreTrainedModel`\n\t+ `Multimodal2Config`\n\nNote that without the full context of the `Multimodal2VisionMLP` class, some details remain unclear."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:35-99

metadata:
{"docstring": "", "end_line": 99, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaEmbeddings", "start_line": 35, "type": "class", "chroma:document": "**Summary:**\n\nThe `RobertaEmbeddings` class is a PyTorch implementation of the BERT embeddings layer. Its primary purpose is to construct embeddings from word, position, and token type embeddings.\n\n**Key Implementation Details:**\n\n1. **Module Architecture:** The module consists of:\n\t* `word_embeddings`: word embedding layer with `config.vocab_size` vocab and `config.hidden_size` hidden dim.\n\t* `position_embeddings`: position embedding layer with `config.max_position_embeddings` max pos emb and `config.hidden_size` hidden dim.\n\t* `token_type_embeddings`: token type embedding layer with `config.type_vocab_size` tok vocab and `config.hidden_size` hidden dim.\n\t* `LayerNorm`, `dropout`, and `position_embedding_type`.\n2. **Forward Pass:** The forward pass takes in input IDs, token types, positions, and optional inputs embeds:\n\t* If input IDs is provided, it uses the word embeddings to get the embeddings.\n\t* Otherwise, it uses the position embeddings to get the embeddings.\n\t* It adds the token type embeddings to the result.\n\t* It applies LayerNorm and dropout to the result.\n\n**Notable Dependencies:**\n\n1. `nn.Module`, `nn.Embedding`, `LayerNorm`, `Dropout`\n2. `torch` library\n\n**Interfaces/Inherited Interfaces:**\n\n1. The class inherits from `nn.Module`."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:102-233

metadata:
{"docstring": "", "end_line": 233, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaSelfAttention", "start_line": 102, "type": "class", "chroma:document": "**Overview**\n\nThe `RobertaSelfAttention` class implements a self-attention mechanism in the context of transformer-based neural networks. It is designed to compute the weighted sum of input elements that are most relevant to a given element, based on their similarity.\n\n**Key Functionality**\n\n1.  Initializes the attention head sizes and linear layers for query, key, and value.\n2.  Transposes input tensors for computation efficiency.\n3.  Computes attention scores by taking the dot product between query and key representations.\n4.  Applies position embeddings to consider relative positions between tokens.\n5.  Normalizes attention scores to produce probabilities.\n6.  Attenuates self-attention weights through dropout and applies masked heads.\n\n**Notable Components**\n\n*   `query`, `key`, and `value` linear layers\n*   `transpose_for_scores` method for efficient computation\n*   `position_embedding_type` parameter controlling relative position embedding usage\n\n**Dependencies**\n\n*   PyTorch (`torch`)\n*   Math library (math)\n\n**Key Interfaces/Methods**\n\n*   `__init__(config, position_embedding_type)`\n*   `transpose_for_scores(x)`\n*   `forward(hidden_states, *, attention_mask, head_mask, encoder_hidden_states, ..., output_attentions)`"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:236-334

metadata:
{"docstring": "", "end_line": 334, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaSdpaSelfAttention", "start_line": 236, "type": "class", "chroma:document": "**RobertaSdpaSelfAttention Class Summary**\n\nThe `RobertabaSdpaSelfAttention` class is a variant of the standard RoBerta self-attention mechanism.\n\n**Main Purpose and Functionality**\n\nThis class implements a modified version of the scaled dot-product attention (SDPA) that uses a memory-efficient backend.\nIt dispatches to either Flash Attention or Efficient kernels depending on the specific configuration.\nThe class also provides an ability to use `past_key_value` for prefix tuning.\n\n**Key Implementation Details:**\n\n* The `forward` method takes in various inputs and returns the attention output, query layers, value layers, key layers, and value layers.\n* It uses the `torch.nn.functional.scaled_dot_product_attention` function under the hood.\n* The `require_contiguous_qkv` attribute control the behavior of the memory-efficient backend.\n\n**Notable Dependencies or Requirements:**\n\n* `torch` with version 2.0.0 or later for optimized performance.\n* `RobertaSelfAttention` as a base class.\n\nHere is an example of how to use this class:\n```python\nimport torch\n\nconfig = ...  # define the model configuration\nattention_model = RobertaSdpaSelfAttention(config)\n```\n**Warning:**\n\nThe class uses warnings when non-absolute position embeddings or output attentions are used, indicating that it will fall back to a manual attention implementation.\nThis can be improved by specifying the `attn_implementation` argument to `\"eager\"` when loading the model."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:337-348

metadata:
{"docstring": "", "end_line": 348, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaSelfOutput", "start_line": 337, "type": "class", "chroma:document": "**Concise Summary**\n\nThis code defines a `RobertaSelfOutput` class (`nn.Module`) that extends PyTorch's neural network functionality.\n\n**Main Purpose and Functionality**\nThe primary purpose of this class is to calculate the self-attention output for a given input tensor using the Roberta model architecture. It applies density, dropout, and layer normalization operations to transform the hidden states into a final output tensor.\n\n**Key Implementation Details**\n\n* `[ Dense Layer ]`: A linear (`nn.Linear`) transformation with `config.hidden_size` as input and output dimensions.\n* `[ Dropout Layer ]`: Applies random dropout to the transformed hidden state with probability `config.hidden_dropout_prob`.\n* `[ LayerNorm Layer ]`: Applies layer normalization (additive) to the combined output of density and dropout layers, using eps-value from the `LayerNorm` arguments.\n\n**Notable Dependencies and Requirements**\n\n* `torch.nn` (PyTorch's neural network module)\n* `nn.Linear`, `nn.LayerNorm`, `nn.Dropout` classes from PyTorch (`torch`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:357-405

metadata:
{"docstring": "", "end_line": 405, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaAttention", "start_line": 357, "type": "class", "chroma:document": "**Concise Summary**\n\nThe `RobertaAttention` class implements the multi-head attention mechanism using the RoBERTa architecture. It inherits from PyTorch's `nn.Module`.\n\n**Main Purpose and Functionality**\n---------------------------\n\n* Attentive computation of weighted sums with learned query, key, and value vectors.\n* Allows for dynamic pruning of irrelevant attention heads.\n\n**Key Implementation Details**\n-----------------------------\n\n* Employs the ROBERTA_SELF_ATTENTION_CLASSES implementation from config.\n* Utilizes `find_pruneable_heads_and_indices` to identify pruneable heads.\n* Modifies linear layers using `prune_linear_layer` after identifying pruneable heads.\n\n**Notable Dependencies and Requirements**\n-----------------------------------------\n\n* Extends PyTorch's `nn.Module`.\n* Requires `ROBERTA_SELF_ATTENTION_CLASSES`, `prune_linear_layer`, `find_pruneable_heads_and_indices`, and `config._attn_implementation`.\n\n**Method Signatures and Interfaces**\n--------------------------------------\n\n* `__init__: self, config: nn(cfg), position_embedding_type=None`\n* `prune_heads: heads (list) -> None`\n* `forward: hidden_states (torch.Tensor), *args (Optional[torch.FloatTensor] = None)`"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:408-420

metadata:
{"docstring": "", "end_line": 420, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaIntermediate", "start_line": 408, "type": "class", "chroma:document": "**Summary**\n\nThe `RobertaIntermediate` class is a PyTorch neural network module that implements the attention intermediate layer of the BERT (Bidirectional Encoder Representations from Transformers) architecture. Its primary purpose is to apply an activation function (`config.hidden_act`) to the output of a dense linear transformation (`dense`) of input `hidden_states`.\n\n**Key Features**\n\n* Applies a linear transformation using `nn.Linear`\n* Conditional application of an activation function:\n\t+ If `config.hidden_act` is a string, uses the corresponding mapping from `ACT2FN`\n\t+ Otherwise, uses the provided `config.hidden_act` value as the activation function\n* Inheriting interface: `nn.Module`\n\n**Dependences**\n\n* PyTorch library (`torch`)\n* `nn.Linear`, `torch.Tensor`, and other PyTorch modules."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:423-434

metadata:
{"docstring": "", "end_line": 434, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaOutput", "start_line": 423, "type": "class", "chroma:document": "**Concise Summary**\n\nThe `RobertaOutput` class is an instance of the PyTorch neural network module (`nn.Module`). It serves as the output layer for a transformer-based model. The primary purpose of this class is to apply a dense output layer, followed by normalization and dropout to process input tensor.\n\n**Key Implementation Details**\n\n* **forward method**: Applies self-dense, then dropout on `hidden_states`, and finally layers normalization with added `input_tensor`.\n* **Attributes**:\n\t+ `dense`: A linear layer (`nn.Linear`) applied to `hidden_states` and `input_tensor`.\n\t+ `LayerNorm`: Layer normalized output of hidden tensor and input tensor.\n\t+ `dropout`: Applicative dropout on output.\n\n**Notable Dependencies/Requirements**\n\n* Imported classes: `nn.Module`, `nn.Linear`, `nn.LayerNorm`, `nn.Dropout`\n* External dependencies:\n\t+ `torch.Tensor`"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:437-520

metadata:
{"docstring": "", "end_line": 520, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaLayer", "start_line": 437, "type": "class", "chroma:document": "**Summary**\n\nThe `RobertaLayer` class is a custom PyTorch nn.Module implementing the Roberta transformer layer. It consists of:\n\n1. **Initialization**: Sets default parameters and instantiates attention, intermediate, and output layers based on the provided configuration (`config`).\n2. **Forward Pass**:\n\t* Computes self-attention outputs using `RoBERTaAttention` with optional cross-attention if `add_cross_attention` is enabled.\n\t* Applies a feed-forward chunk to the self-attention outputs.\n3. **Notable Dependencies/Requirements**:\n\t+ `config`: Provides configuration settings for the layer.\n\t+ `torch.nn.Module`, `torch.Tensor`\n\t+ `apply_chunking_to_forward`: A custom function that applies chunking to compute forward pass.\n\n**Key Method Signatures and Interfaces**\n\n* `__init__(config)`: Initializes the layer with the provided configuration.\n* `forward(hidden_states: torch.Tensor, ... -> Tuple[torch.Tensor])`: Computes self-attention outputs and applies a feed-forward chunk to produce the final output.\n* `RoBERTaAttention(config)`: An instance of the Roberta attention class used in the forward pass.\n* `RoBERTaIntermediate(config)`: An instance of the Roberta intermediate layer used in the feed-forward chunk.\n\n**External Dependencies**\n\n* None (self-contained PyTorch code)\n\n**Class Variables**\n\n* `chunk_size_feed_forward`: A configuration setting for the feed-forward chunk size.\n* `is_decoder` and `add_cross_attention`: Configuration flags enabling cross-attention.\n* `seq_len_dim`, `past_key_value`, and `encoder_hidden_states`: Miscellaneous variables used in the forward pass."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:523-613

metadata:
{"docstring": "", "end_line": 613, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaEncoder", "start_line": 523, "type": "class", "chroma:document": "**Summary:**\n\nThis is an implementation of the `RobertaEncoder` class, which inherits from PyTorch's `nn.Module`. The class processes input sequences using multi-head attention and feed-forward networks.\n\n**Primary Purpose:**\nThe primary purpose of this class is to encode input sequences into a context-free representation that can be used for downstream tasks such as classification or question answering.\n\n**Key Implementation Details:**\n\n*   Multi-layer architecture using `RobertaLayer` modules, which process input sequences in parallel.\n*   Support for gradient checkpointing and caching of attention outputs.\n*   Optional output of attentions, hidden states, and past key values.\n*   Interface with other models for multi-task training.\n\n**Notable Dependencies and Requirements:**\n\n*   `torch` library for tensor operations\n*   `nn.ModuleList` to manage multiple layers\n*   `RobertaLayer` to implement individual layer logic\n\n**Interfaces Implemented:**\n\n*   Inherited `nn.Module`\n*   Supports output formats including `BaseModelOutputWithPastAndCrossAttentions`\n\nThis summary aims to capture the core functionality and key features of the provided code chunk, providing a concise overview for developers familiar with PyTorch models."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:616-628

metadata:
{"docstring": "", "end_line": 628, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaPooler", "start_line": 616, "type": "class", "chroma:document": "**Summary:**\n\nThis is a PyTorch implementation of `RobertaPooler` class, which inherits from the `nn.Module` class. The primary purpose of this class is to \"pool\" the input model by taking only the first token's hidden state.\n\n**Key Details:**\n\n* Takes in a `hidden_states` tensor as input.\n* Uses a linear layer (`self.dense`) with dimensions `(config.hidden_size, config.hidden_size)` to transform the first token into pooled output.\n* Applies the `torch.nn.Tanh()` activation function to the pooled output.\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch `nn.Module` class\n* PyTorch `Linear` and `Tanh` modules (`import torch.nn as nn`)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:631-701

metadata:
{"docstring": "", "end_line": 701, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "load_tf_weights_in_roberta", "start_line": 631, "type": "function", "chroma:document": "**Function Summary:**\n\n`load_tf_weights_in_roberta`\n\nLoads TensorFlow checkpoints into a PyTorch RoBERTa model.\n\n**Purpose and Functionality:**\n\nThis function loads the pre-trained weights from a specified TensorFlow checkpoint file (`tf_checkpoint_path`) and initializes the corresponding variables in the provided PyTorch model instance (`model`). It filters out unnecessary variables, such as Adam optimizer variables and biases, to ensure proper compatibility with PyTorch. The loaded weights are then stored in the model's tensor fields.\n\n**Key Implementation Details:**\n\n* Loads `init_vars` from the TensorFlow checkpoint file\n* Iterates through each variable `name` and its corresponding value `array`\n* Filters out unnecessary variables based on name patterns\n* Assigns the loaded array to the corresponding PyTorch variable using ` pointer.data = torch.from_numpy(array)`\n\n**Notable Dependencies and Requirements:**\n\n* NumPy (`numpy`)\n* TensorFlow (`tensorflow`)\n* PyTorch (`torch`)\n\n**Interface and Method Signatures:**\n\n* `tf.train.list_variables(tf_path)`: Lists TF variables in the specified path\n* `tf.train.load_variable(tf_path, name)`: Loads a single variable from the TensorFlow checkpoint file\n* `getattr(model, name)`: Accesses an attribute by its name within the model instance\n\n**External Dependencies:**\n\n* Re (regular expression)\n* Logger ( error and info logging functions)\n\nNote: This summary provides a high-level overview of the function's purpose, key implementation details, and notable dependencies. The original code remains unchanged to preserve its integrity."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py:704-730

metadata:
{"docstring": "", "end_line": 730, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_roberta.py", "name": "RobertaPreTrainedModel", "start_line": 704, "type": "class", "chroma:document": "**Concise Summary**\n\n### Model: Roberta Pre-Trained Model\n```\nAn abstract base class for handling weights initialization and downloading/preloading pretrained models.\n\n#### Key Highlights:\n\n* Initializes weights using `normal_(mean=0.0, std=config.initializer_range)` for:\n\t+ `Linear` layers\n* Zeroes biases with `zero_()` if present\n* Normalizes weight matrix with `fill_(1.0)` in `LayerNorm` layers\n\n#### Configuration:\n\n* `config_class`: RobertaConfig\n```\n\n### Notes\n\nNo notable dependencies or implementation details are listed, assuming this code is part of a larger package with existing config implementation.\n\nInherited Interfaces:\n\n* `PreTrainedModel`\n* No external dependencies mentioned"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_from_uppercase_model.py:4-5

metadata:
{"docstring": "# Check if we can correctly grab dependencies with correct naming from all UPPERCASE old model", "end_line": 5, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_from_uppercase_model.py", "name": "FromUppercaseModelEncoderLayer", "start_line": 4, "type": "class", "chroma:document": "**Summary**\n\nThis class, `FromUppercaseModelEncoderLayer`, inherits from `CLIPEncoderLayer`. It serves as a transformer layer in the Hugging Face model encoder architecture. The class is responsible for converting input data to uppercase.\n\n**Key Implementation Details**\n\n*   **Primary Purpose:** Converts input data to uppercase.\n*   **Method Signatures:**\n    *   `__init__(self)`: Initializes the layer with no dependencies specified.\n*   **Important Interface:** Inherited from `CLIPEncoderLayer`, which suggests it's an encoder layer in a transformer architecture.\n\n**Notable Dependencies and Requirements**\n\nThe class does not appear to have any external dependencies or requirements. However, it inherits interfaces and classes from the parent class `CLIPEncoderLayer`, indicating that it relies on the underlying architecture provided by this class or its immediate parents. \n\n**Security Note:** Although not explicitly mentioned in this snippet alone, consider that encoding inputs strictly in uppercase might not be suitable for all applications where model input handling requires case sensitivity (for example, during testing or when interacting with APIs)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_my_new_model2.py:11-107

metadata:
{"docstring": "", "end_line": 107, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_my_new_model2.py", "name": "MyNewModel2Config", "start_line": 11, "type": "class", "chroma:document": "**Summary:**\n\nThe `MyNewModel2Config` class is a configuration for the Gemma model, inheriting from `PretrainedConfig`. It stores model architecture definitions and has methods to control output shapes.\n\n**Key Implementation Details:**\n\n* Initializes with `vocab_size`, defining token representation capacity\n* Sets tensor parallel plan for base model (`MyNewModel2Model`) using `base_model_tp_plan` dictionary\n* Sets various model hyperparameters, such as hidden size, intermediate size, and number of attention heads\n\n**Notable Dependencies or Requirements:**\n\n* Inherited from `PretrainedConfig`\n* External dependencies:\n\t+ `gemma-7b` for default configuration\n* References related classes:\n\t+ `GemmaModel`\n\t+ `MyNewModel2Model`\n\nOverall, this class configures the Gemma model and defines its architecture, including hyperparameters and tensor parallel plan."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_task_model.py:11-83

metadata:
{"docstring": "", "end_line": 83, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_task_model.py", "name": "NewTaskModelForNewTask", "start_line": 11, "type": "class", "chroma:document": "**NewTaskModelForNewTask Class Summary**\n\n**Class Overview**\n-----------------\n\n`NewTaskModelForNewTask` is a custom PyTorch model for conditional generation, inheriting from `PaliGemmaForConditionalGeneration`. Its primary purpose is to process input sequences and generate text outputs.\n\n**Key Functionality**\n--------------------\n\nThe main functionality of this class involves:\n\n*   Processing input sequences with customizable projection\n*   Performing L2 normalization on projected sequences\n*   Scaling projections by attention masks\n\n**Notable Implementation Details**\n---------------------------------\n\n*   Uses `nn.Linear` for custom text projection (`self.custom_text_proj`)\n*   Includes a call to `super().forward()` to delegate most processing tasks\n*   Performs forward pass with input variables such as `input_ids`, `pixel_values`, and `attention_mask`\n\n**Dependencies and Requirements**\n-------------------------------\n\n*   Inheriting from `PaliGemmaForConditionalGeneration`\n*   Uses `torch` for computations\n*   Assumes availability of `self.language_model` with corresponding attributes\n\n**External Dependencies**\n----------------------\n\n*   `PyTorch` library (not explicitly listed but assumed as part of PyTorch-E engine)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:26-43

metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "Multimodal1TextRMSNorm", "start_line": 26, "type": "class", "chroma:document": "**Class Summary: Multimodal1TextRMSNorm**\n\n**Main Purpose and Functionality**\n---------------------------\n\nThe `Multimodal1TextRMSNorm` class implements a layer normalization technique, specifically RMSNorm, for multimodal text inputs. It is equivalent to the transformer layer normalization in T5.\n\n* Purpose: Normalize input data by scaling it based on estimated variance.\n* Functionality: Applies layer normalization using the RMSNorm algorithm, which scales input data according to an estimated variance calculated from previous steps in the model pipeline.\n\n**Key Implementation Details**\n-----------------------------\n\n* **Algorithm:** Uses Root Mean Square (RMS) normalization.\n* **Method Signatures:**\n\t+ `forward(hidden_states):` Takes in normalized hidden states and returns output with weight scaling applied.\n\t+ `extra_repr():` Returns extra representation details, including model weights shape and epsilon value.\n\n**Dependencies and Requirements**\n--------------------------------\n\n* **Inherited Interface:** `nn.Module`\n* **External Dependencies:**\n\t+ `torch`: Used for tensor operations (e.g., `rsqrt`, `pow`, `mean`)\n\t+ `nn.Parameter`, `torch.ones`: Used to initialize model parameters.\n* **Notable Classes and Functions:**\n\t+ The RMSNorm algorithm relies on the `numpy` library, although not explicitly imported in this code snippet."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:46-108

metadata:
{"docstring": "", "end_line": 108, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "Multimodal1TextRotaryEmbedding", "start_line": 46, "type": "class", "chroma:document": "Here is a concise summary of the provided class code chunk:\n\n**Summary:**\nThe `Multimodal1TextRotaryEmbedding` class is a PyTorch implementation of a RoPE (Rotary Embedding) layer, which transforms input sequences into dense vector representations. It inherits from PyTorch's `nn.Module`.\n\n**Main Purpose and Functionality:**\n\n* Applies the RoPE transformation to input sequences, utilizing dynamic frequency updates when necessary.\n* Can be configured with different rope types, scaling factors, and maximum sequence lengths.\n\n**Key Implementation Details:**\n\n* The `_dynamic_frequency_update` method adjusts the inverse frequency matrix based on sequence length.\n* In the `forward` method:\n\t+ It expands the RoPE frequency table to match the input sequence shape.\n\t+ Applies the RoPE transformation using the dot product of frequency and position IDs.\n\t+ Optionally applies post-processing scaling factors (if applicable).\n\n**Notable Dependencies and Requirements:**\n\n* `ROPE_INIT_FUNCTIONS`: a dictionary mapping rope types to initialization functions.\n* `Multimodal1TextConfig`: a configuration class for this layer, containing settings such as rope type, scaling factor, and maximum sequence length."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:111-124

metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "Multimodal1TextMLP", "start_line": 111, "type": "class", "chroma:document": "**Class Summary: Multimodal1TextMLP**\n\n* **Main Purpose and Functionality:** A neural network module for multimodal text processing, utilizing MLP architecture in combination with attention gate.\n\n**Key Implementation Details:**\n\n* MLP (Multi-Layer Perceptron) structure with three linear projections (`gate_proj`, `up_proj`, `down_proj`).\n* Gate projection applied to input with attention activation function (`ACT2FN[config.hidden_act]`) followed by up-projection.\n* Output from gate and up-projections are combined using a linear transformation.\n\n**Notable Dependencies and Requirements:**\n\n* `nn.Linear`: Linear transformation layers.\n* `ACT2FN[config.hidden_act]`: Attention activation function (dependent on hidden act type).\n* `config.mlp_bias`: Bias term for linear projections.\n* `config.hidden_size` and `config.intermediate_size`: Hidden state and intermediate layer sizes."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:127-131

metadata:
{"docstring": "", "end_line": 131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "rotate_half", "start_line": 127, "type": "function", "chroma:document": "**Summary:**\n\nRotates half of the hidden dimensions of input tensor `x` by mirroring the latter half around the first half. The rotated tensor is obtained by concatenating the transformed dimensions in reverse order.\n\n**Implementation Details:**\n\n* Rotates x by splitting it into two halves along the last dimension.\n* Inverts and concatenates the second half to create the rotated tensor, preserving the sign of the original values.\n\n**Notable Dependencies/Requirements:**\n\n* Utilizes `torch` library for tensor operations."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:134-158

metadata:
{"docstring": "", "end_line": 158, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "apply_rotary_pos_emb", "start_line": 134, "type": "function", "chroma:document": "**Rotary Position Embedding Function Summarization**\n\nThe `apply_rotary_pos_emb` function applies Rotary Position Embedding to query and key tensors. It takes in two input tensors `q` and `k`, along with cosine and sine parts of the rotary embedding, `cos` and `sin`.\n\n**Summary:**\n\n* Applies Rotary Position Embedding to query (`q`) and key (`k`) tensors\n* Incorporates two separate channels (cosine and sine) into the embedding process\n\n**Notable Implementation Details:**\n\n* Rotating half the input tensor using `rotate_half(q)` or `rotate_half(k)`\n* Broadcasting the cosine and sine parts along an additional dimension (unsqueeze_dim)\n* Composing the embedding process with the query and key tensors\n\n**Dependencies:**\n\n* `torch.Tensor`: Query, Key, Cosine, Sine tensors\n* `rotate_half`: A function not shown in this code snippet (implies a custom implementation)\n\nThe function returns a tuple containing the rotary-embbedded query tensor (`q_embed`) and key tensor (`k_embed`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:161-170

metadata:
{"docstring": "", "end_line": 170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "repeat_kv", "start_line": 161, "type": "function", "chroma:document": "**Summary**\n\nThe `repeat_kv` function transforms a 4D `torch.Tensor` of shape `(batch, num_key_value_heads, seqlen, head_dim)` to match the structure of `torch.repeat_interleave(x, dim=1, repeats=n_rep)`, essentially interleaving key-value pairs.\n\n**Key Implementation Details**\n\n* Repeats each row (`num_key_value_heads`) `n_rep` times along the new axis (dimension 3).\n* New shape: `(batch, num_attention_heads * n_rep, seqlen, head_dim)` where `num_attention_heads` is equivalent to `num_key_value_heads`.\n* Implemented using tensor expansion and reshaping.\n\n**Notable Dependencies and Requirements**\n\n* `torch.Tensor`\n* No external dependencies referenced\n* Adheres to PyTorch's `repeat_interleave` API pattern."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:173-196

metadata:
{"docstring": "", "end_line": 196, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "eager_attention_forward", "start_line": 173, "type": "function", "chroma:document": "**Function Summary: Eager Attention Forward**\n\n*Main Purpose and Functionality:*\nThe `eager_attention_forward` function implements the forward pass of a multi-head attention mechanism using linear transformations and softmax scaling. It takes in query, key, value tensors, and optional attention mask, and returns the output and corresponding attention weights.\n\n**Key Implementation Details:**\n\n* Uses `torch.matmul` to compute attention weights and output\n* Applies softmax scaling and normalizes attention weights\n* Includes causal self-attention (if attention mask is provided)\n* Dropout layer for regularization\n\n**Dependencies and Requirements:**\n\n* `nn.Module`: The input module\n* `torch.Tensor`: Query, key, value tensors\n* `Optional[torch.Tensor]`: Attention mask tensor\n* `kwargs`: Additional keyword arguments (not used in this implementation)\n\n**Method Signatures and Interfaces:**\n\n* `torch.nn.functional.softmax()`\n* `torch.nn.functional.dropout()`\n\t+ `p`: dropout probability\n\t+ `training`: training mode"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:199-272

metadata:
{"docstring": "", "end_line": 272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "Multimodal1TextAttention", "start_line": 199, "type": "class", "chroma:document": "**Multimodal 1 Text Attention Class Summary**\n\n### Purpose and Functionality\nThe `Multimodal1TextAttention` class implements multi-headed attention from the 'Attention Is All You Need' paper. It's a PyTorch module that takes in input data, position embeddings, and attention mask to compute attention weights and output.\n\n### Key Implementation Details\n\n* Three linear projections: `q_proj`, `k_proj`, and `v_proj` for queries, keys, and values respectively.\n* Two more linear projections: `o_proj` for output and `attention_dropout` for regularization.\n\n### Notable Dependencies and Requirements\n* Requires `Multimodal1TextConfig` configuration for hyperparameters.\n* Inherents from `nn.Module` architecture.\n* Depends on `eager_attention_forward` or `ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]` attention interfaces."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py:275-326

metadata:
{"docstring": "", "end_line": 326, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_multimodal1.py", "name": "Multimodal1TextDecoderLayer", "start_line": 275, "type": "class", "chroma:document": "**Multimodal1TextDecoderLayer**\n\n**Main Purpose and Functionality**\nThe `Multimodal1TextDecoderLayer` is a neural network layer that decodes multimodal text inputs. It implements self-attention mechanism followed by fully connected (MLP) layer.\n\n**Key Implementation Details**\n\n* Self-Attention Mechanism: `self_attn`\n\t+ Input: `hidden_states`, `attention_mask`, `position_ids`, `past_key_value`, ... ( keyword arguments )\n\t+ Output: `(hidden_states, self_attn_weights)`\n* Fully Connected Layer: `mlp`\n\t+ Input: `hidden_states`\n\t+ Output: `hidden_states`\n\n**Notable Dependencies and Requirements**\n\n* `Multimodal1TextConfig`: configuration object for the layer\n* `Multimodal1TextAttention`: attention mechanism class\n* `Multimodal1TextRMSNorm` and `Multimodal1TextMLP`: normalization and fully connected layer classes respectively\n* `torch` library and its tensors for computations"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:26-43
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "SuperRMSNorm", "start_line": 26, "type": "class", "chroma:document": "**Concise Summary:**\n\nThe `SuperRMSNorm` class is an equivalent implementation of T5LayerNorm. It normalizes the input tensor using RMS Normalization, which scales and shifts the input to have zero mean and unit variance.\n\n* Primary purpose:\n\t+ Normalizes input tensor using RMS Normalization\n\t+ Returns scaled and shifted output\n* Key implementation details:\n\t+ Computes variance of input using `torch.rsqrt`\n\t+ Applies scaling and shifting factors using `self.weight` and `self.variance_epsilon`\n* Notable dependencies or requirements:\n\t+ Uses PyTorch's nn.Module and nn.Parameter for computation\n\t+ Requires `hidden_size` parameter for weight initialization\n\t+ Inherited the `__init__` method from PyTorch's `nn.Module`\n\n**Key Interface:**\n\n* `forward(hidden_states)`: Normalizes input tensor using RMS Normalization\n* `extra_repr()`: Returns string representation of the module's weights and epsilon value\n\nNote that external dependencies are not listed as they are implicit, being provided by PyTorch."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:46-108
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 108, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "SuperRotaryEmbedding", "start_line": 46, "type": "class", "chroma:document": "Here is a concise summary of the provided code chunk:\n\n**Class**: `SuperRotaryEmbedding` (a PyTorch module)\n\n**Purpose**: The `SuperRotaryEmbedding` class implements a self-attention mechanism using the RoPE (Rotary Position Embedding) method, which allows for efficient and scalable attention over long sequences.\n\n**Key Features**:\n\n* Supports dynamic frequency updates based on sequence length.\n* Can be configured with \"dynamic\" scaling type.\n* Includes post-processing scaling factor for advanced types like Yarn.\n* Applies cosine and sine functions to RoPE embeddings.\n\n**Notable Attributes and Methods**:\n\n* `rope_kwargs`: configuration dictionary for RoPE initialization.\n* `max_seq_len_cached` and `original_max_seq_len`: cached sequence length variables.\n* `_dynamic_frequency_update`: method updating frequency for dynamic scaling.\n* `forward`: the primary forward pass method taking input `x` and `position_ids`.\n\n**Dependencies**: \n- PyTorch ( TorchAutocast )\n- Transformers (ROPE_INIT_FUNCTIONS)"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:111-124

metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "SuperMLP", "start_line": 111, "type": "class", "chroma:document": "**Summary:**\n\nThe `SuperMLP` class is a PyTorch implementation of a multi-layer perceptron (MLP) network, specifically designed for the Super Glance model. It inherits from `nn.Module`.\n\n**Key Functionality:**\n\n- Composes two projection matrices (`gate_proj` and `up_proj`) using the `act_fn` activation function.\n- Applies these projections sequentially to the input `x` in a bottom-up manner.\n\n**Implementation Details:**\n\n- `forward` function uses `down_proj`, `gate_proj`, `up_proj`, and `act_fn` to process inputs.\n - Key modules:\n  - `self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)`\n  - `self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)` \n  - `down_proj = nn.Linear=self.intermediate_size, self.hidden_size, bias=config.mlp_bias)`\n\n**Notable Dependencies/Requirements:**\n\n- `config`: an object containing hyperparameters and configuration settings.\n- `ACT2FN`: a dictionary mapping activation functions to their respective PyTorch implementations."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:127-131

metadata:
{"docstring": "", "end_line": 131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "rotate_half", "start_line": 127, "type": "function", "chroma:document": "**Summary:**\n\nRotates the last half of hidden dimensions of input tensor `x` by reflecting it through the middle dimension and concatenating with its original first half.\n\n**Key Implementation Details:**\n\n- Rotates tensor using slicing (`x[..., : x.shape[-1] // 2]`)\n- Reflects half of the tensor across the midpoint using `-x2`\n- Concatenates rotated and original halves along the last axis (-1)\n- Uses PyTorch's `torch.cat` function for concatenation\n\n**Notable Dependencies:**\n\n- **Input:** `x`: expected input tensor with shape that allows slicing\n- **Output:** Rotated tensor (shape dependent on input)\n\nNote: This summary focuses on the key functionality and implementation details of the provided code chunk. It aims to be concise while preserving essential information for understanding the function's purpose and behavior."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:134-158

metadata:
{"docstring": "", "end_line": 158, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "apply_rotary_pos_emb", "start_line": 134, "type": "function", "chroma:document": "**Summary: RotateQueryEmbedding**\n\nApplies Rotary Position Embedbing to query and key tensors.\n\n**Functionality:**\n\n1. Applies Rotary Position Embedding to query (`q`) and key (`k`) tensors:\n\t* Scales `cos` and `sin` by the specified unsqueeze dimension using `unsqueeze`.\n\t* Computes rotated embeddings as `(q * cos) + (rotate_half(q) * sin)` for `q_embed`, and similarly for `k_embed`.\n2. Returns a tuple containing the rotated query embedding (`q_embed`) and key embedding (`k_embed`).\n\n**Implementation Details:**\n\n* Uses `rotate_half` function to compute half rotation of input tensors.\n* Applies unsqueeze dimension to broadcast tensors with specific shapes.\n\n**Notable Dependencies/Requirements:**\n\n* Input tensors `q`, `k`, `cos`, and `sin` must be of type `torch.Tensor`.\n* The `rotate_half` function is assumed to be defined elsewhere in the codebase.\n\n**Return Value:** A tuple containing the rotated query embedding (`q_embed`) and key embedding (`k_embed`)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:161-170

metadata:
{"docstring": "", "end_line": 170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "repeat_kv", "start_line": 161, "type": "function", "chroma:document": "**Summary**\n\n`repeat_kv` function:\nReplicates hidden states in the multi-head attention mechanism.\n\n**Main Purpose and Functionality**\nTransposes the input `hidden_states` from `(batch, num_key_value_heads, seqlen, head_dim)` to `(batch, num_attention_heads, seqlen, head_dim)`\n\nwhile maintaining the same batch dimensionality. When `n_rep == 1`, original tensor is returned.\n\n**Implementation Details**\n\n* Replicates hidden states in the sequence dimension using `expand`\n* Reshapes the tensor to (batch, num_key_value_heads * n_rep, seqlen, head_dim)\n\n**Notable Dependencies and Requirements**\nReplaces `torch.repeat_interleave` functionality with explicit expansion and reshaping.\n\nNote: External dependency is no additional external library besides PyTorch."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:173-196

metadata:
{"docstring": "", "end_line": 196, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "eager_attention_forward", "start_line": 173, "type": "function", "chroma:document": "**Concise Summary:**\n\nEager Attention Forward Function\n=====================================\n\n### Purpose and Functionality\n\nThe `eager_attention_forward` function computes the attention output for a given query, key, and value tensor in an eager attention mechanism. It applies masked self-attention to account for causal dependencies.\n\n### Key Implementation Details\n\n* Computes attention weights using QK^T (query-key dot product) and softmax normalization\n* Masks out causal dependencies using `attention_mask`\n* Applies dropout regularization\n* Outputs attended value tensor using transformed weights\n\n### Notable Dependencies or Requirements\n\n* `nn.Module`: input module object\n* `torch.Tensor`: query, key, value tensors\n* `attention_mask`: optional attention mask tensor\n* `scaling`: scaling factor for weights\n* `dropout`: dropout rate (default: 0.0)\n* `kwargs`: additional keyword arguments (not used)\n\n### Interface\n\n`eager_attention_forward(output, weights)`\n\nwhere `output` is the attended value tensor and `weights` is a tensor of attention weights.\n\n### External Dependencies\n\nNone"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:199-272

metadata:
{"docstring": "", "end_line": 272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "SuperAttention", "start_line": 199, "type": "class", "chroma:document": "**SuperAttention Class Summary**\n\nThe `SuperAttention` class implements multi-headed attention mechanism based on the \"Attention Is All You Need\" paper. It is a PyTorch module that extends the traditional self-attention mechanism.\n\n### Key Implementation Details:\n\n1. **Projector Layers**: The class defines four projector layers:\n   - `q_proj`: query projection\n   - `k_proj`: key projection\n   - `v_proj`: value projection\n   - `o_proj`: output projection\n\n2. **Attention Dropout and Scaling**: The attention mechanism uses a scaling factor computed as `head_dim**-0.5`, applied after computing the attention.\n\n3. **Eager Attention vs Scaled Dot-Product Attention (SDPA)**: The class distinguishes between two implementations:\n   - Eager attention\n   - SDPA, which is backed by an implementation dependent on the `_attn_implementation` attribute\n\n### Notable Dependencies/Requirements:\n\n1. `torch.nn.Linear`: used to define linear projector layers.\n2. `super().__init__`: inherited from PyTorch's neural network module (`nn.Module`).\n3. External dependencies:\n\t* `torch`: necessary for tensor computations and module initialization.\n\n### Interfaces:\nThe class uses the following interfaces:\n- `eager_attention_forward`\n- `ALL ATTENTION_FUNCTIONS` dictionary, backed by an implementation dependent on the `_attn_implementation` attribute."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py:275-326

metadata:
{"docstring": "", "end_line": 326, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_super.py", "name": "SuperDecoderLayer", "start_line": 275, "type": "class", "chroma:document": "**Summary:**\n\n`SuperDecoderLayer` is a PyTorch implementation of the decoder layer in a transformer model. It inherits from `nn.Module`.\n\n**Primary Purpose:** This class handles the decoding process of a sequence-to-sequence model, consisting of self-attention and fully connected (MLP) components.\n\n**Key Implementation Details:**\n\n* Self-attention mechanism using `SuperAttention` with custom settings.\n* Post-attention layer normalization (`SuperRMSNorm`) applied to intermediate results.\n* Fully connected layers using `SuperMLP` with the same configuration.\n* Input and post-attention layer normalizations applying to hidden states.\n\n**Notable Dependencies:**\n\n* `SuperConfig`: defines configuration parameters, including hidden size and RMS norm epsilon.\n* `SuperAttention`, `SuperRMSNorm`, `SuperMLP`: custom modules implementing specific attention mechanisms, normalization techniques, and fully connected layers.\n\nThis code is part of a larger sequence-to-sequence transformer model, likely for machine translation tasks."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:26-43

metadata:
{"docstring": "", "end_line": 43, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "MyNewModel2RMSNorm", "start_line": 26, "type": "class", "chroma:document": "**Summary: MyNewModel2RMSNorm Class**\n\nThe `MyNewModel2RMSNorm` class is a PyTorch neural network module that implements the RMS Norm normalization technique. It normalizes input tensors using their mean squared values, scaling by a learned weight.\n\n**Key Features:**\n\n* **Primary Purpose:** Normalize input tensors using their mean squared values.\n* **Implementation Details:** The `_norm` method computes the normalized output by multiplying the input `x` with the square root of its mean magnitude plus epsilon. The `forward` method applies this normalization and then scales the result by a learned weight vector.\n* **Notable Dependencies:**\n\t+ `nn.Module`: PyTorch neural network module base class.\n\t+ `torch.nn.Parameter`: Learnable parameter container.\n\t+ `torch`: Torch library for PyTorch functions (e.g., `rsqrt`, `pow`).\n* **Key Method Signatures:**\n\t+ `_norm(x)`: Compute normalized output by x.\n\t+ `forward(x)`: Apply RMS Norm normalization and scaling.\n\t+ `extra_repr()`: Return model weight shape and epsilon value as string representation.\n\n**Dependencies:**\n\n* `torch`\n* External dependencies not applicable (self-contained PyTorch implementation)."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:46-59

metadata:
{"docstring": "", "end_line": 59, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "MyNewModel2MLP", "start_line": 46, "type": "class", "chroma:document": "**Summary:**\n\nThe `MyNewModel2MLP` class is an implementation of a multi-layer perceptron (MLP) using PyTorch's `nn.Module`. The model takes in input data `x`, applies an activation function (`self.act_fn`) to the linear transformation result of two projection layers (`self.gate_proj` and `self.up_proj`), and then passes the output through another projection layer (`self.down_proj`). The primary purpose is to transform the input data.\n\n**Key Implementation Details:**\n\n* `ACT2FN[config.hidden_act]`: uses a configuration-dependent activation function\n* Compositions of linear transformations and element-wise multiplications with activation functions\n\n**Notable Dependencies or Requirements:**\n\n* `nn.Linear`\n* `nn.Module`\n* `nn.init` (activation functions)\n* PyTorch's dynamic computation graph\n\n**Main Purpose and Functionality:**\nThe model transforms input data using a composition of linear projections, followed by an element-wise multiplication with an activation function applied to the gate projection result."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:62-124

metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "MyNewModel2RotaryEmbedding", "start_line": 62, "type": "class", "chroma:document": "**Summary:**\n\nThis is a PyTorch implementation of the RoPE (Rotated Polynomial Embeddings) model, which is an attention mechanism. The `MyNewModel2RotaryEmbedding` class encapsulates the model's functionality.\n\n**Main Purpose and Functionality:**\nThe primary purpose is to compute the attention weights for a given input sequence using the RoPE algorithm.\n\n**Key Implementation Details:**\n\n1.  **Initialization**: The model takes in a configuration object `config` and an optional device (default=None). It initializes several internal variables, including `rope_type`, `max_seq_len_cached`, and `original_max_seq_len`.\n2.  **Dynamic Frequency Update**: The `_dynamic_frequency_update` method adjusts the `inv_freq` tensor based on the sequence length to maintain precision.\n3.  **Forward Pass**: The `forward` method computes the attention weights using the RoPE algorithm. It performs the following steps:\n    *   Updates the frequency tensor if necessary.\n    *   Computes the cosine and sine components of the embeddings.\n    *   Applies post-processing scaling factors to the cosine and sine components.\n\n**Notable Dependencies or Requirements:**\n\n1.  `ROPE_INIT_FUNCTIONS`: A dictionary mapping `rope_type` values to their corresponding initialization functions.\n2.  `MyNewModel2Config`: The configuration class for this model, containing settings like `rope_scaling`.\n3."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:127-131

metadata:
{"docstring": "", "end_line": 131, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "rotate_half", "start_line": 127, "type": "function", "chroma:document": "**Summary**\n\nRotates half of the hidden dimensions of input tensor `x` by mirroring the latter half around the middle dimension.\n\n**Key Details**\n\n* Rotates half dimensions using slicing (`x[..., : x.shape[-1] // 2]`, `x[..., x.shape[-1] // 2 :]`)\n* Mirrors half with negation (`-x2`) followed by concatenation along last axis (-1)\n* Reforms tensor shape with resulting rotation applied\n\n**Related Dependencies**\n\n* torch for tensor operations and cat function implementation\n* no external dependencies noted"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:134-158

metadata:
{"docstring": "", "end_line": 158, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "apply_rotary_pos_emb", "start_line": 134, "type": "function", "chroma:document": "**Summary: Rotary Position Embedding Function**\n\nApplies Rotary Position Embedding to query and key tensors.\n\n### Main Purpose and Functionality\nApplys Rotary Position Embedding to `q` and `k` tensors using `(q * cos) + (rotate_half(q) * sin)` for `q_embed` and `(k * cos) + (rotate_half(k) * sin)` for `k_embed`.\n\n### Key Implementation Details\n\n* Cosine (`cos`) and sine (`sin`) tensors are unsqueezed along specified dimension.\n* Use of `rotate_half()` function.\n* Rotary Position Embedding formula applied: `(q * cos) + (rotate_half(q) * sin)`.\n\n### Notable Dependencies or Requirements\n\n* `torch.Tensor`\n* `rotate_half()` function, assuming it's defined elsewhere in the codebase.\n* No explicit external dependencies listed.\n\n**Method Signatures and Interfaces**\n\n* None explicitly listed, but implies usage of `torch.Tensor` interface."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:161-170

metadata:
{"docstring": "", "end_line": 170, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "repeat_kv", "start_line": 161, "type": "function", "chroma:document": "**Summary**\n\nRepeats key-value pairs in the `hidden_states` tensor along the attention heads dimension.\n\n**Functionality**\n\n- Takes in:\n  - `torch.Tensor hidden_states`: Input tensor with shape (batch, num_key_value_heads, seqlen, head_dim)\n  - `int n_rep`: Number of times to repeat each key-value pair\n- Returns a new tensor with shape (batch, num_attention_heads, seqlen, head_dim)\n\n**Implementation Details**\n\n- Repeats `hidden_states` by expanding along the fourth dimension and repeating that repeat `n_rep` times.\n\n**Dependencies and Requirements**\n\n- Uses PyTorch's tensor operations and does not require any external dependencies."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:173-196

metadata:
{"docstring": "", "end_line": 196, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "eager_attention_forward", "start_line": 173, "type": "function", "chroma:document": "**Concise Summary**\n\n### Function Purpose and Functionality\nThe `eager_attention_forward` function implements the Eager Attention mechanism, which generates weighted sums of key-value pairs in a neural network module (`module`).\n\nPrimary purpose: Compute attention weights and output.\n\n### Key Implementation Details\n\n*   Computes attention weights using matrix multiplication with scaling factor (`scaling`) applied.\n*   Applies dropout to attention weights.\n*   Merges attention weights with causal mask (if provided) for masked token handling.\n*   Softmax-activation of attention weights.\n*   computes attention output by multiplying attention weights with value states.\n\n### Notable Dependencies and Requirements\n\n*   `torch` tensor modules (`nn.Module`, `torch.Tensor`)\n*   `torch.functional` module (`nn.functional.softmax`, `nn.functional.dropout`)\n*   External dependency: `repeat_kv` function (not provided in the code snippet)\n*   Implemented interface: `module.num_key_value_groups`\n*   Optional dependencies:\n    *   Relies on causal mask for masked token handling. If not available, no additional operations are performed.\n        |"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:199-272

metadata:
{"docstring": "", "end_line": 272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "MyNewModel2Attention", "start_line": 199, "type": "class", "chroma:document": "**Summary:**\n\nThe `MyNewModel2Attention` class is an implementation of multi-headed attention from the 'Attention Is All You Need' paper. This class takes in configuration parameters and layer index and consists of three projection layers (`q_proj`, `k_proj`, `v_proj`) for queries, keys, and values respectively.\n\n**Key Implementation Details:**\n\n*   Uses scaled-dot product attention with linear transformations and position embeddings.\n*   Supports both eager and static cache implementations for past key-value updates.\n*   Scales the input to match the attention head dimension via self-scaling.\n\n**Notable Dependencies or Requirements:**\n\n*   `torch` with its tensor operations and `nn.Linear`.\n*   `AllAttentionFunctions` map with implemented scaling dot product attention functions (e.g., `ScaledDotProductAttention`, `MatMulAttention`).\n*   `MyNewModel2Config` with necessary configuration parameters."}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py:275-326

metadata:
{"docstring": "", "end_line": 326, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_my_new_model2.py", "name": "MyNewModel2DecoderLayer", "start_line": 275, "type": "class", "chroma:document": "**MyNewModel2DecoderLayer Class Summary**\n\n* **Primary Purpose**: A PyTorch module implementing the decoder layer of a transformer model, handling self-attention and feed-forward networks.\n\n* **Key Implementation Details**:\n\t+ `self_attn` : MyNewModel2Attention module for self-attention mechanism.\n\t+ `mlp` : MyNewModel2MLP module for fully connected feed-forward network.\n\t+ `input_layernorm` and `post_attention_layernorm`: Two RMSNorm layers for layer normalization.\n\n* **Notable Dependencies/Requirements**:\n\t+ `MyNewModel2Config` class, providing configuration parameters.\n\t+ `MyNewModel2Attention`, `MyNewModel2MPM`, and `RMSNorm` classes from surrounding codebase.\n\n**Method Signatures:**\n\n- `forward`: Takes in input `hidden_states`, `attention_mask`, `position_ids`, and optional keyword arguments, returning a tuple of `(output, attention_weights)`.\n\n**External Dependencies:**"}


created_at: 2025-02-01 08:47:41
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:27-28

metadata:
{"docstring": "", "end_line": 28, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionAttention", "start_line": 27, "type": "class", "chroma:document": "**Summary:**\n\nThis `Multimodal2VisionAttention` class is a subclass of `CLIPAttention`, implying it is implementing the Attention mechanism specifically designed for multimodal 2 (image and text) input in the context of CLIP (Visual-BERT) models.\n\n**Key Features:**\n\n* Subclass of `CLIPAttention`\n* Primary purpose: Implementing multimodal 2 attention for image-text pairs\n\n**Implementation Details:**\n\n* No explicit key implementation details are provided, suggesting that this class might be a placeholder or an abstract implementation that needs to be completed by its subclasses.\n\n**Notable Dependencies and Requirements:**\n\n* Inherited/from `CLIPAttention`: Attention mechanism, likely containing methods like `compute_score()` and `compute \u0432\u043d\u0438\u043c()`.\n* External Dependencies:\n\t+ None explicitly mentioned, but it is assumed to rely on the CLIP library or similar models for multimodal processing."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:32-33

metadata:
{"docstring": "# Check that adding the second base class correctly set the parent, even though in clip it does not have the \"Vision\" part", "end_line": 33, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionSdpaAttention", "start_line": 32, "type": "class", "chroma:document": "**Class Summary**\n\n`Multimodal2VisionSdpaAttention`\n\n inherites from `CLIPSdpaAttention` and `Multimodal2VisionAttention`. This class appears to implement a multimodal attention mechanism, combining features of two classes: Multimodal2VisionAttention and CLIPSdpaAttention. However, the actual implementation seems to be an empty pass, indicating a potential mistake in the class inheritance.\n\n**Key Implementation Details**\n\n* Implements `Multimodal2VisionAttention`\n* Inherites from `CLIPSdpaAttention`\n\n**Notable Dependencies or Requirements**\n\nNone specified.\n\n**Primary Purpose**\nThe primary purpose of this class is not explicitly stated due to its empty implementation, but it likely aims to combine the attention mechanisms of Multimodal2VisionAttention and CLIPSdpaAttention."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:37-38

metadata:
{"docstring": "# Check that adding the second base class correctly set the parent, even though in clip it does not have the \"Vision\" part", "end_line": 38, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionFlashAttention2", "start_line": 37, "type": "class", "chroma:document": "**Summary:**\n\nThe `Multimodal2VisionFlashAttention2` class is a subclass of both `CLIPFlashAttention2` and `Multimodal2VisionAttention`. This allows inheriting the functionality of `CLIPFlashAttention2` while also utilizing the multimodal attention mechanism from its base class.\n\n**Key Implementation Details:**\n\n- Inherited `CLIBrushLayerNormalization`\n- Implemented `Multimodal2VisionFlashAttention.forward()` to utilize the vision module's flash attention\n- Utilizes `CLIPFlashAttention2`'s `config` and `num_heads_per_layer` features\n\n**Notable Dependencies or Requirements:**\n\n- `CLIBrushLayerNormalization`\n- Extends `CLIPFlashAttention2` via inheritance\n- Depends on `Multimodal2VisionAttention` for multimodal attention functionality"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:48-49

metadata:
{"docstring": "", "end_line": 49, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionMLP", "start_line": 48, "type": "class", "chroma:document": "**Summary:**\n\nThe `Multimodal2VisionMLP` class inherits from `CLIPMLP`, inheriting its functionality. The actual implementation of the MLMPLP is empty (`pass` statement), indicating that this class likely requires additional configuration or override of parent class methods.\n\n**Key Details:**\n\n* **Method Signature:** None explicitly defined; inheritance from `CLIPMLP` implies method implementation.\n* **Design Choice:** Empty class body suggests need for further customization or subclassing.\n* **Notable Dependencies/Requirements:** Requires a base class (`CLIPMLP`) and potential configuration options."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:52-56

metadata:
{"docstring": "", "end_line": 56, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionEncoderLayer", "start_line": 52, "type": "class", "chroma:document": "**Multimodal 2 Vision Encoder Layer Summary**\n\nThis class implements the 2nd layer of the Multimodal 2 Vision encoder, inheriting from CLIP's EncoderLayer. Its primary purpose is to facilitate multi-modal attention and feature transformation.\n\n**Key Components:**\n\n* `self_attn`: Instantiates a self-attention mechanism using `MULTIMODAL2_VISION_ATTENTION_CLASSES` based on `_attn_implementation`.\n* `mlp`: Creates an MLP (Multi-Layer Perceptron) instance for feature transformations, utilizing the provided `config`.\n\n**Interfaces and Dependencies:**\n\n* Inherit from `CLIPEncoderLayer`\n* Utilizes external class `Multimodal2VisionMLP` \n* depends on `_attn_implementation` configuration value"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:59-62

metadata:
{"docstring": "", "end_line": 62, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionEncoder", "start_line": 59, "type": "class", "chroma:document": "**Summary:**\n\nThe `Multimodal2VisionEncoder` class is a custom encoder that extends `CLIPEncoder`. It inherits the configuration and initialization behavior from its parent class. In the constructor, it:\n\n* Initializes a list of `nn.ModuleList` containing multiple instances of `Multimodal2VisionEncoderLayer`, configured by the provided `config`.\n* The number of layers in the model is determined by `config.num_hidden_layers`.\n\n**Key Details:**\n\n* Extends `CLIPEncoder` using inheritance.\n* Employs a sequence of `Multimodal2VisionEncoderLayers` with user-configured architecture.\n\nNote: Without more context, it's difficult to pinpoint key algorithms or design choices. However, this summary provides a concise overview of the class's purpose and composition."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:66-69

metadata:
{"docstring": "# Finally here the `Vision` part was correct in CLIP, but we still need to tell it that the encoder arg should use it as well", "end_line": 69, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionTransformer", "start_line": 66, "type": "class", "chroma:document": "**Summary**\n\nThe `Multimodal2VisionTransformer` class is a subclass of `CLIPVisionTransformer`. It initializes with a `config` object and sets up an instance of `Multimodal2VisionEncoder`, leveraging the `Vision` part from the CLIP architecture to provide multimodal input processing. \n\n**Key Details**\n\n- Class: `Multimodal2VisionTransformer`\n- Subclass of: `CLIPVisionTransformer`\n- Depends on: `Multimodal2VisionEncoder`\n\n**Notable Dependencies/Requirements**\n\n- External dependency:\n  - No explicit external dependencies mentioned.\n  \n However, this appears to depend on the CLIP library or similar. The actual implementation may include additional external dependencies not explicitly listed here.\n\nNote that some parts of this code snippet seem incomplete (e.g., the `Docstring` entry), but these are likely intended for more substantial documentation and not part of the core class functionality."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py:72-75

metadata:
{"docstring": "", "end_line": 75, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_multimodal2.py", "name": "Multimodal2VisionPreTrainedModel", "start_line": 72, "type": "class", "chroma:document": "**Summary:**\n\nThe `Multimodal2VisionPreTrantedModel` class inherits from `CLIPPreTrainedModel`. The `_init_weights` method checks if the input module is an instance of `Multimodal2VisionMLP`, which is not implemented anywhere in the provided code chunk.\n\n**Key Insights and Requirements:**\n\n- Implemented Interfaces: None (not inheriting or implementing any interfaces)\n- External Dependencies:\n  - `CLIPPreTrainedModel`\n- Notable Design Choices:\n  - Overriding `_init_weights` method to explicitly handle `Multimodal2VisionMLP` instances"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py:24-125

metadata:
{"docstring": "", "end_line": 125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py", "name": "FromUppercaseModelAttention", "start_line": 24, "type": "class", "chroma:document": "**Multi-Head Attention Model**\n\nThis is an implementation of the multi-head attention mechanism from the 'Attention Is All You Need' paper.\n\n### Purpose and Functionality\n\nThe primary purpose of this model is to compute the weighted sum of the input sequence, based on the similarity between the query and key sequences. The output is a new sequence that represents the weighted sum of all possible inputs at each position.\n\n### Key Implementation Details\n\n*   The model uses a multi-head attention mechanism with `num_heads` heads.\n*   Each head applies a linear transformation (projection) to the input sequence using separate linear layers for queries (`q_proj`), keys (`k_proj`), and values (`v_proj`).\n*   The output of each head is a tensor with shape `(batch_size \\* num_heads, tgt_len, head_dim)`.\n\n### Notable Dependencies or Requirements\n\n*   Requires PyTorch 1.9+\n*   Uses the `nn.functional` module for softmax and dropout functions.\n\n### Method Signatures and Interfaces\n\n*   `_shape(tensor: torch.Tensor, seq_len: int, bsz: int) -> torch.Tensor`: A helper function to reshape tensors.\n*   `forward(hidden_states: torch.Tensor, ...) -> Tuple[torch.Tensor, Optional[torch.Tensor]]`: The main forward pass method.\n\n### Class Variables and Attributes\n\n*   `config`: An input configuration object that contains hyperparameters such as `hidden_size`, `num_attention_heads`, and `attention_dropout`.\n*   `scale`: A scaling factor used to stabilize the attention computation (`self.head_dim**-0.5`).\n*   `dropout`: The dropout probability used in the attention mechanism (`config.attention_dropout`)."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py:128-211

metadata:
{"docstring": "", "end_line": 211, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py", "name": "FromUppercaseModelFlashAttention2", "start_line": 128, "type": "class", "chroma:document": "**Class Summary: FromUppercaseModelFlashAttention2**\n\nThis class inherits from `FromUppercaseModelAttention` and implements a modified version of the Flash Attention module for compatibility with previous versions.\n\n**Main Purpose and Functionality**\n=====================================\n\nThe primary purpose of this class is to provide a forward pass through the modified Flash Attention module, which takes into account padding tokens in the input sequence.\n\n**Key Implementation Details**\n-----------------------------\n\n* The `forward` method uses the `flash_attention_forward` function from `transformers.models.llama.modeling_llama.LlamaFlashAttention2`.\n* It casts the layer norms to a specific data type (float32 or _pre_quantization_dtype) for training stability reasons.\n* The `use_top_left_mask` attribute is set based on whether the Flash Attention version is 2.1 or later.\n\n**Notable Dependencies and Requirements**\n-----------------------------------------\n\n* `flash_attention` module from `transformers.models.llama.modeling_llama`\n* `_flash_attention_forward` function from `transformers.models.llama.modeling_llama.LlamaFlashAttention2`\n* `_pre_quantization_dtype` attribute in the config\n* Optional tensors for attention mask, causal attention mask, and output attentions\n\n**Method Signatures and Interfaces**\n------------------------------------\n\nThe class has one method:\n\n* `forward`: takes `hidden_states`, `attention_mask`, `causal_attention_mask`, and `output_attentions` as input, returns a tuple of `(attn_output, attn_weights)`.\n\n**External Dependencies**\n------------------------\n\n* The `_flash_attn_uses_top_left_mask` attribute depends on the `is_flash_attn_greater_or_equal_2_10()` function.\n* The `_pre_quantization_dtype` attribute depends on the config."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py:214-284

metadata:
{"docstring": "", "end_line": 284, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py", "name": "FromUppercaseModelSdpaAttention", "start_line": 214, "type": "class", "chroma:document": "**Summary:**\n\nThis code defines the `FromUppercaseModelSdpaAttention` class, which inherits from `FromUppercaseModelAttention`. The main purpose is to adapt the attention mechanism of the base model (`torch.nn.functional.scaled_dot_product_attention`) to support SDPA (Scaled-Dot-Product Attention).\n\n**Key Implementation Details:**\n\n* Uses `scaled_dot_product_attention` with modified forward pass to accommodate custom `attn_mask` and `causal_attention_mask`.\n* Projects input states using `q_proj`, `k_proj`, and `v_proj`.\n* Reshapes projections for scaling dot-product attention.\n* Handles compatibility issues between PyTorch 2.x and SDPA backend.\n* Applies output projection with `out_proj`.\n\n**Notable Dependencies:**\n\n* `torch.nn.functional.scaled_dot_product_attention`\n* Custom `attn_mask` (sum of `attention_mask` and `causal_attention_mask`)\n* `FromUppercaseModelAttention`\n\n**Interface:**\n \nThe class respects the following interfaces:\n- `forward(hidden_states, attention_mask, causal_attention_mask, output_attentions)`\n- `q_proj`, `k_proj`, and `v_proj` projections\n- `out_proj` output projection"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py:287-299

metadata:
{"docstring": "", "end_line": 299, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py", "name": "FromUppercaseModelMLP", "start_line": 287, "type": "class", "chroma:document": "**Class Summary: `FromUppercaseModelMLP`**\n\n*   **Purpose:** A PyTorch neural network model that takes input `hidden_states` and applies multilayer perceptron (MLP) transformations, followed by an activation function and an output transformation.\n*   **Primary Purpose:** The primary purpose of this class is to implement a simple multilayer perceptron (MLP) for the attention mechanism using a feed-forward neural network (FNN).\n\n**Key Implementation Details:**\n\n*   **Activation Function:** Uses the `ACT2FN` dictionary to select an activation function based on the `config.hidden_act`.\n    *   `self.activation_fn = ACT2FN[config.hidden_act]`\n*   **Neural Network Layers:**\n    1.  Input `hidden_states` is passed through a fully connected (`fc`) layer with weights and biases determined by `config.hidden_size`.\n        *   `self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)`\n    2.  The output of the first neural network layer undergoes an activation function transformation.\n        *   `hidden_states = self.activation_fn(hidden_states)`\n    3.  The previous output is then passed through another fully connected (`fc`) layer with weights and biases determined by `config.intermediate_size`.\n        *   `self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)`\n\n**Notable Dependencies or Requirements:**\n\n*   **PyTorch Tensors:** Uses PyTorch tensors to represent the input `hidden_states`.\n*   **Neural Network Modules:** Depends on PyTorch neural network modules (e.g., `nn.Linear`).\n*   **ACT2FN Dictionary:** Utilizes a dictionary (`ACT2FN`) that maps activation function names to their corresponding functions."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py:309-356

metadata:
{"docstring": "", "end_line": 356, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modeling_from_uppercase_model.py", "name": "FromUppercaseModelEncoderLayer", "start_line": 309, "type": "class", "chroma:document": "**Summary**\n\nThe `FromUppercaseModelEncoderLayer` class is an encoder layer in the Transformer architecture. It implements self-attention mechanism and follows residual connected feed-forward network (RCFFNN) architecture.\n\n**Key Implementation Details:**\n\n*   **Self-Attention Mechanism**: Utilizes `self_attn` which is an instance of `FROM_UPPERCASE_MODEL_ATTENTION_CLASSES[config]._attn_implementation](config)` from `FromUppercaseModelATTENTION_CLASSES`. It takes in `hidden_states`, `attention_mask`, and `causal_attention_mask`.\n*   **Residual Connections**: Applies residual connections after two separate self-attention mechanisms, which enhance stability and make the network easier to train.\n*   **MLP (Multi-Layer Perceptron) Block**: Implemented using an instance of `FromUppercaseModelMLPB` with configuration settings from `config`.\n\n**Notable Dependencies:**\n\n*   Importantly uses `nn.Module`, `torch.FloatTensor`, `optional` type hinting and `Trapeze` functions\n*   Relies on `FROM_UPPERCASE_MODEL_ATTENTION_CLASSES[config._attn_implementation]`\n*   Incorporates `FromUppercaseModelMLP` with configuration settings from `config`.\n\n**Other Requirements:**\n\n- Uses Torch (PyTorch) library (`torch.Tensor`)\n - Implements LayerNorm (`self.layer_norm1`, `self.layer_norm2`)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py:77-81
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 81, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py", "name": "get_setup_file", "start_line": 77, "type": "function", "chroma:document": "**Summary:**\n\nThe `get_setup_file` function is used to retrieve the setup file name from command-line arguments. It utilizes the `argparse` library to parse the command-line input and extract the value of the `-f` (or `--file`) option.\n\n**Key Details:**\n\n* The function uses `argparse.ArgumentParser()` to create a parser.\n* Adds a single argument `-f` with no default value.\n* Uses `parser.parse_args()` to parse the command-line arguments and extract the value of `-f`.\n* Returns the extracted file name as a string (`return args.f`).\n\n**Notable Dependencies:**\n\n* `argparse`: Used for command-line argument parsing.\n\nNote: No external dependencies or interfaces are explicitly stated in this function. However, it assumes that the `argparse` library is available and properly configured in the environment."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py:84-92
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 92, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py", "name": "get_results", "start_line": 84, "type": "function", "chroma:document": "**Function Summary:**\n\n`get_results(output_dir)` retrieves stored JSON results from the specified output directory.\n\n**Key Implementation Details:**\n\n* Reads and returns existing \"all_results.json\" file from the provided `output_dir`.\n* If file does not exist, raises a ValueError.\n\n**Notable Dependencies:**\n\n* Relies on `json` module for parsing the JSON file.\n* Utilizes `os.path.join()` to construct file path.\n* Inherent usage of built-in file I/O functions (e.g., `open()`, `with` statement).\n\nNo external dependencies are explicitly stated in this snippet. However, the `json` module is a fundamental Python standard library dependency."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
id: /home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py:95-96
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 96, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py", "name": "is_cuda_available", "start_line": 95, "type": "function", "chroma:document": "**Summary:**\n\nThe `is_cuda_available` function checks if CUDA (NVIDIA GPU acceleration) is available on the system by using TensorFlow's configuration API (`tf.config.list_physical_devices(\"GPU\")`) and returns a boolean value indicating availability.\n\n**Implementation Details:**\n\n* The primary purpose of this function is to determine if the system has a CUDA-compatible GPU.\n* It uses the `list_physical_devices` method from TensorFlow's configuration API to query for physical devices with the \"GPU\" label.\n\n**Key Information:**\n\n* Returns: `bool` indicating whether CUDA (GPU) is available\n* Uses: TensorFlow's `tf.config.list_physical_devices(\"GPU\")`\n* Inherited/Implemented Interfaces: None"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py:103-337

metadata:
{"docstring": "", "end_line": 337, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/test_tensorflow_examples.py", "name": "ExamplesTests", "start_line": 103, "type": "class", "chroma:document": "This is a Python class that implements a set of unit tests for various natural language processing (NLP) tasks using the Hugging Face Transformers library. The tests are designed to verify the correct behavior of different NLP models, such as text classification, language modeling, named entity recognition, question answering, sentiment analysis, and image classification.\n\nThe class `ExamplesTests` inherits from a base class not shown in the code snippet, which likely provides a set of common test fixtures or utilities. Each method in this class corresponds to a specific NLP task, and it performs the following steps:\n\n1. It creates a temporary directory (`tmp_dir`) where all the models will be stored.\n2. It defines a list of `testargs` that specify the model name, training parameters, and other settings for each test case.\n3. It patches the `sys.argv` attribute with the `testargs` so that when running the corresponding script (e.g., `run_text_classification.py`, etc.), it runs with these specific arguments.\n4. It calls the `main()` function of the NLP model's script to run the training loop, and then it retrieves the evaluation metrics from the results directory (`tmp_dir`).\n\nThe tests are annotated with additional metadata, such as `@slow` or `@test_run-swag`, which may indicate that these tests take longer to execute or require specific execution environments. The tests also include assertions that verify specific values of the evaluation metrics.\n\nOverall, this class provides a comprehensive set of tests for various NLP tasks, ensuring that different models behave correctly under different conditions and training parameters."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_text_classification.py:65-74

metadata:
{"docstring": "# region Helper classes", "end_line": 74, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_text_classification.py", "name": "SavePretrainedCallback", "start_line": 65, "type": "class", "chroma:document": "**SavePretrainedCallback Class Summary**\n\n### Purpose and Functionality\n\nThe `SavePretrainedCallback` class is a Keras callback that saves Hugging Face models using the `save_pretrained()` method, which includes model weights and metadata, after each epoch.\n\n### Key Implementation Details\n\n* Saves the model using `model.save_pretrained(output_dir)` after each epoch.\n* Uses the `__init__()` method to initialize the output directory.\n\n### Notable Dependencies or Requirements\n\n* Keras\n* Hugging Face models (inherited from `Docstring`)\n\nNote: The `Docstring` class is not shown in this code snippet, but it is likely a base class for other callback classes used by Hugging Face models."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_text_classification.py:210-588

metadata:
{"docstring": "# endregion", "end_line": 588, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_text_classification.py", "name": "main", "start_line": 210, "type": "function", "chroma:document": "Here's a concise summary of the provided code:\n\n**Overview**\n\nThis is a Python script that trains a transformer-based text classification model using the Hugging Face Transformers library. The script performs various tasks, including data preparation, model compilation, training, validation, and prediction.\n\n**Key Components**\n\n1. **Data Preparation**: Loads dataset from Hugging Face datasets or custom database, preprocesses text data (e.g., tokenization), and divides data into training, validation, and testing sets.\n2. **Model Creation**: Initializes the transformer-based model with a specified architecture, learning rate, number of epochs, and other hyperparameters.\n3. **Model Compilation**: Compiles the model with an optimizer, loss function, and metrics (e.g., accuracy).\n4. **Training**: Trains the model on the training data using the `fit()` method and performs validation on the validation data after each epoch.\n5. **Validation and Prediction**: Evaluates the model's performance on the validation data and predicts labels or scores for the testing data.\n6. **Prediction Output**: Saves predictions to a text file and writes results to a JSON file.\n\n**Configuration**\n\nThe script takes several configuration options as input:\n\n1. **Model Architecture**: Specify the transformer-based architecture (e.g., `bert-base-uncased`, `distilbert-v1`) and learning rate.\n2. **Number of Epochs**: Set the number of training epochs.\n3. **Batch Size**: Control the batch size for training and prediction.\n4. **Output Directory**: Define a directory to save model weights, prediction results, and logs.\n\n**Libraries**\n\nThe script relies on several libraries:\n\n1. **Hugging Face Transformers**: For transformer-based architectures, tokenization, and other functionality.\n2. **PyTorch**: For building the model and training loop.\n3. **NLTK**: For text preprocessing (e.g., tokenization).\n\nOverall, this script provides a flexible framework for training and evaluating text classification models using transformer architectures."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_glue.py:190-595

metadata:
{"docstring": "# endregion", "end_line": 595, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/text-classification/run_glue.py", "name": "main", "start_line": 190, "type": "function", "chroma:document": "This code provides an example of how to train a language model using the Transformers library and the Hugging Face framework. The script trains the model on a dataset, performs evaluation and prediction tasks, and returns results.\n\nHere is a high-level summary of what the code does:\n\n* Loads dataset(s)\n* Preprocesses data using standard methods\n* Sets up training loop with various options (e.g., batch size, epochs, learning rate)\n* Trains model for specified number of epochs\n* Evaluates model on validation set to compute evaluation metrics\n* Optionally uses advanced evaluation settings if MNLI double evaluation is required\n* Saves prediction results to output directory\n\nSome notable aspects of the code include:\n\n* The use of Transformers library and Hugging Face framework for training and deployment.\n* Support for multiple dataset formats (raw, tf, datasets).\n* Custom evaluation metrics (e.g., EM, precision) can be used with additional logging statements.\n* Option to save model to local directory when not using hub."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/translation/run_translation.py:266-711

metadata:
{"docstring": "# endregion", "end_line": 711, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/translation/run_translation.py", "name": "main", "start_line": 266, "type": "function", "chroma:document": "This code is for training a machine translation model using the Hugging Face Transformers library. The script trains the model on a specified dataset and evaluates its performance by calculating the BLEU score, saving results to a file, and potentially uploading the trained model to Hub.\n\nThe main components of the script include:\n\n1. Setting up the model and tokenizer\n2. Preparing training data\n3. Defining evaluation metrics\n4. Compiling the model\n5. Training the model\n6. Evaluating the model\n\nThe code also includes various callbacks that are used during training, such as `PushToHubCallback` to upload the trained model to Hub, and `KerasMetricCallback` to evaluate the model's performance on a separate dataset.\n\nHere is a concise summary of the script:\n\n* Set up machine translation model and tokenizer\n* Prepare training data\n* Define evaluation metrics (BLEU score)\n* Compile the model\n* Train the model\n* Evaluate the model using BLEU score\n\nOverall, this code provides a robust framework for training machine translation models on large datasets using popular libraries like Hugging Face Transformers."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/multiple-choice/run_swag.py:237-563

metadata:
{"docstring": "# endregion", "end_line": 563, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/multiple-choice/run_swag.py", "name": "main", "start_line": 237, "type": "function", "chroma:document": "This is a code snippet for training and evaluating a multiple-choice conversational AI model using the Transformers library by Hugging Face. The code defines various parameters, loads pre-trained models, fine-tunes them on a specific task, trains and evaluates the models, saves the results, and optional pushes the updated weights to a Hub.\n\nHere's a concise summary of the main parts:\n\n1. **Model Preparation**:\n   - Defines parameters for training and evaluation.\n   - Prepares a Hugging Face dataset for training and evaluation.\n   - Initializes pre-trained models and fine-tuning task specifics.\n\n2. **Training Loop**:\n   - Creates an optimizer, scheduler, and loss function.\n   - Trains the model on the dataset using the training method from Transformers's TFEf Trainer class.\n\n3. **Evaluation**:\n   - Evaluates the trained model on the validation dataset if training took place.\n\n4. **Results Saving**:\n   - Saves the evaluation metrics or results in a JSON file named according to the directory where `output_dir` was specified.\n\n5. **Pushing to Hub (optional)**:\n   - If `push_to_hub` is True and `output_dir` exists, pushes the updated weights of the model to the chosen hub in Hugging Face.\n\nNote that without knowing the exact implementation details and context of this specific code snippet, some steps or parts might be simplified or described as suggested by the authors."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py:31-89

metadata:
{"docstring": "", "end_line": 89, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py", "name": "parse_args", "start_line": 31, "type": "function", "chroma:document": "**Function Summary:**\n\nThe `parse_args` function is used to parse command-line arguments for preparing TFRecord shards from pre-tokenized samples of the wikitext dataset. It utilizes the `argparse` library to define and validate parameters.\n\n**Key Implementation Details:**\n\n*   Parses various configuration options, including `dataset_name`, `tokenizer_name_or_path`, and `output_dir`.\n*   Defines several argument types (e.g., `str`, `int`) with default values.\n*   Includes optional arguments for customizing the training process (e.g., `trust_remote_code`).\n*   Validates user input through various constraints (e.g., `choices=[\"train\", \"test\", \"validation\"]`).\n\n**Notable Dependencies or Requirements:**\n\n*   The function relies on the `argparse` library to define and parse command-line arguments.\n*   It uses several external classes and functions, including:\n    *   `argparse.ArgumentParser`\n    *   `argparse.Argument`\n    *   `argparse.Action`\n    *   `argparse.parse_args`\n\n**Inherited or Implemented Interfaces:**\n\n*   None.\n\n**Primary Purpose:**\n\nThe function is designed to parse command-line arguments and validate user input, providing a structured configuration for preparing TFRecord shards."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py:92-96

metadata:
{"docstring": "", "end_line": 96, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py", "name": "tokenize_function", "start_line": 92, "type": "function", "chroma:document": "**Concise Summary:**\n\n`tokenize_function` takes a `tokenizer` object as input and returns a function `fn`. The returned `fn` takes a dictionary `examples` containing a `\"text\"` key, and applies the `tokenizer` to it. This suggests that the primary purpose of this function is to wrap a text tokenizer with a custom interface.\n\n**Key Implementation Details:**\n\n* The returned function `fn` delegates text processing to the input `tokenizer`.\n* It returns the tokenized result from the `tokenizer` interface (`return tokenizer(examples[\"text\"])`).\n\n**Notable Dependencies or Requirements:**\n\n* A valid `tokenizer` object is required for the `tokenize_function`."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py:99-112

metadata:
{"docstring": "", "end_line": 112, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py", "name": "get_serialized_examples", "start_line": 99, "type": "function", "chroma:document": "**Function Summary: `get_serialized_examples`**\n\n**Purpose:** Serializes input IDs and attention masks into TensorFlow examples for downstream tokenization tasks.\n\n**Functionality:**\n\n* Iterates through a list of tokens (`input_ids`) and their corresponding attention masks.\n* Creates TF features with serialized input IDs and attention masks.\n* Serializes each example using `tf.train.Example` and stores the result in a list of bytes (`record_bytes`).\n* Returns a list of serialized examples.\n\n**Key Implementation Details:**\n\n* Uses TensorFlow's `int64_list` to serialize integer values efficiently.\n* Implements `tf.train.Features` to create a nested feature structure.\n\n**Notable Dependencies/Requirements:**\n\n* TensorFlow library (specifically, its feature and example serialization APIs)\n* Tokenized data with `input_ids` and `attention_mask` keys."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py:115-186

metadata:
{"docstring": "", "end_line": 186, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py", "name": "main", "start_line": 115, "type": "function", "chroma:document": "**Function Summary**\n\nThis function, `main`, appears to be part of a data processing pipeline for text datasets. It:\n\n1. Loads a dataset from Hugging Face's Datasets API based on user input (dataset_name, split, and trust_remote_code). If a limit is specified, it limits the number of samples in the dataset.\n2. Tokenizes the entire dataset using an AutoTokenizer instance and maps it to a new format.\n3. Concatenates all texts into a single sequence and splits it into chunks of a fixed size (max_length).\n4. Creates TFRecord files from these chunked sequences, one shard per file.\n\n**Key Implementation Details**\n\n* Tokenization is performed using `AutoTokenizer.from_pretrained` and `map` method with `batched=True`.\n* Text concatenation and chunking is done in `group_texts` function.\n* Chunked sequences are written to TFRecord files using `tf.io.TFRecordWriter`.\n\n**Notable Dependencies and Interfaces**\n\n* `datasets`: Hugging Face's Datasets API (load_dataset, select)\n* `AutoTokenizer`: Hugging Face's AutoTokenizer instance (from_pretrained)\n* `get_serialized_examples`: presumably a custom function to serialize examples into TFRecords.\n* `tf.io.TFRecordWriter`: TensorFlow's TFRecord writer interface.\n* `os.path.exists`, `os.makedirs`, and ` os.path.join` for directory management.\n\n**Primary Functionality**\n\nThe primary purpose of this code is to process a text dataset by tokenizing, concatenating, and chunking the text into TFRecord files. The input dataset can be limited and processed in parallel using multiple CPU processes."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py:54-165

metadata:
{"docstring": "", "end_line": 165, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py", "name": "parse_args", "start_line": 54, "type": "function", "chroma:document": "**Function Summary:**\n\n`parse_args` is a function that parses command-line arguments for training a masked language model using TensorFlow and TPU. It uses `argparse` to define and validate input parameters.\n\n**Key Implementations:**\n\n* Configures the `pretrained_model_config`, tokenizer, batch size per TPU core (`per_replica_batch_size`), and hyperparameters (e.g., learning rate, weight decay rate) using command-line arguments.\n* Allows flexible usage on both CPU and TPU environments with options for debugging on non-TPU instances (`--no_tpu`) or initializing a TPU resource (`tpu_name`, `tpu_zone`, `gcp_project`).\n\n**Notable Dependencies/Requirements:**\n\n* TensorFlow\n* `argparse`\n* `tpu` (optional)\n* Google Cloud Storage (for dataset loading)\n\n**Interfaces/Exports:**\n\nNone, returns the parsed command-line arguments (`args`) as output."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py:168-185

metadata:
{"docstring": "", "end_line": 185, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py", "name": "initialize_tpu", "start_line": 168, "type": "function", "chroma:document": "**Summary:**\n\n`initialize_tpu(args)` Function\n=====================================\n\n**Purpose and Functionality:**\nEstablishes a connection to a Google Cloud TPU cluster.\n\n**Key Implementation Details:**\n\n* Creates a `TPUClusterResolver` instance with provided TPU name, zone, and project.\n* Triggers the connection to a TPU cluster using `tf.config.experimental_connect_to_cluster`.\n* Initializes the TPU system using `tf.tpu.experimental.initialize_tpu_system`.\n\n**Notable Dependencies or Requirements:**\n\n* `tf`: TensorFlow library for distributed computation on TPU clusters.\n* `gcp_project`, `tpu_name`, and `tpu_zone` are assumed to be provided through command-line arguments.\n\n**Return Value:**\nReturns the initialized `TPUClusterResolver` instance."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py:188-196

metadata:
{"docstring": "", "end_line": 196, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py", "name": "count_samples", "start_line": 188, "type": "function", "chroma:document": "**Summary:**\n\nThe `count_samples` function takes a list of file paths as input and returns the total number of samples extracted from TFRecord files. It achieves this by:\n\n* Splitting each file path to extract the sample count from the filename using a regular expression\n* Parsing the extracted string to obtain an integer sample count\n* Summing up the individual sample counts\n\n**Key Implementation Details:**\n\n* Uses `re.search` and `.group(1)` to extract the sample count from the format \"-<count>-<id>.tfrecord\"\n* Performs type conversion on the sample count using `int()`\n\n**Notable Dependencies:**\n\n* Uses regular expression (`re`) module for string parsing\n* Assumes TFRecord file format is in the specified pattern."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py:199-214

metadata:
{"docstring": "", "end_line": 214, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py", "name": "prepare_dataset", "start_line": 199, "type": "function", "chroma:document": "**Function Summary:** `prepare_dataset`\n\n**Purpose:**\nCreates and prepares a TensorFlow dataset for training from the given records.\n\n**Key Purpose:** Dataset creation and preprocessing with optional shuffling, batching, and masking.\n\n### Implementation Details:\n\n- **Dataset Creation:** Utilizes `tf.data.Dataset.from_tensor_slices(records)`.\n- **Shuffling (if applicable):** Adds shuffle buffer size assertion if enabled.\n- **TFRecordDataset (optional):** Set `num_parallel_reads=AUTO` for efficient data loading.\n- **Decoding and Masking:**\n  - Applies `decode_fn` mapping on all elements (`dataset.map(decode_fn, num_parallel_calls=AUTO)`).\n  - Applies `mask_fn` mapping on batched dataset (`dataset.batch(batch_size, drop_remainder=True).map(mask_fn, num_parallel_calls=AUTO)`).\n\n### Dependencies and Requirements:\n\n- `tf.data`\n- `count_samples` function (not shown in this snippet)\n- `decode_fn` and `mask_fn` functions (not shown in this snippet)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py:217-317

metadata:
{"docstring": "", "end_line": 317, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/run_mlm.py", "name": "main", "start_line": 217, "type": "function", "chroma:document": "**Function Summary: Training Hugging Face's AutoModelForMaskedLM**\n\n### Main Functionality\n\nThis function is the entry point for training the `AutoModelForMaskedLM` model. It takes in several command-line arguments and uses them to configure the model, tokenizer, data loading, and training parameters.\n\n### Implementation Details\n\n* The function initializes a `TPUStrategy` or `OneDeviceStrategy` based on whether `--no_tpu` is provided.\n* It sets up the mixed precision policy for bfloat16 if specified.\n* It loads the pre-trained tokenizer and config from Hugging Face's model hub.\n* It defines a data decoding function (`decode_fn`) to parse TFRecord files.\n* It creates a data collator using `DataCollatorForLanguageModeling` with custom masking logic.\n* It trains the model on the training dataset, evaluating on the evaluation dataset after each epoch.\n\n### Notable Dependencies and Requirements\n\n* TensorFlow 2.x\n* Hugging Face's `AutoTokenizer`, `AutoConfig`\n* TFRecord files containing training and evaluation datasets\n* External dependencies:\n\t+ `tf.distributestrategy` for parallel training\n\t+ `tf.io.gfile` for file I/O\n\t+ `os` for path manipulation\n\n### Key Method Signatures and Interfaces\n\n* `initilm.fit`: trains the model on the training dataset\n* `model(tf.Tensor):` passes dummy inputs through the model to build weights\n\nThis summary provides a concise overview of the main functionality, key implementation details, and notable dependencies and requirements of this code chunk."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/train_unigram.py:32-76

metadata:
{"docstring": "", "end_line": 76, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/train_unigram.py", "name": "parse_args", "start_line": 32, "type": "function", "chroma:document": "**Summary**\n\nThe `parse_args` function creates an argparse parser to define command-line arguments for training a unigram tokenizer on the wikitext dataset. The main functionality is to:\n\n* Parse input arguments using `argparse.ArgumentParser`\n* Define 6 parameters:\n\t+ `--dataset_name`: dataset name (defaults to \"wikitext\")\n\t+ `--dataset_config`: configuration name of the dataset (defaults to \"wikitext-103-raw-v1\")\n\t+ `--trust_remote_code`: trust the execution of code from datasets/models on the Hub (bool)\n\t+ `--batch_size`: batch size during training (defaults to 1000)\n\t+ `--vocab_size`: size of the desired vocabulary (defaults to 10048)\n\t+ `--limit`: limit the number of shards for debugging (optional, int)\n\t+ `--export_to_hub`: export the trained model to the Hub (bool)\n\n**Key Details**\n\n* The parser is created with a description and default values for the parameters.\n* The `store_true` action is used for boolean flags (`--trust_remote_code` and `--export_to_hub`).\n* The `default=None` value allows an optional integer argument for the `--limit` parameter.\n\n**Dependencies/Requirements**\n\n* The `argparse` library is required."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/train_unigram.py:79-124

metadata:
{"docstring": "", "end_line": 124, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling-tpu/train_unigram.py", "name": "main", "start_line": 79, "type": "function", "chroma:document": "**Concise Summary**\n\n### Main Purpose\n\nThis function trains and modifies a BERT-based model, specifically the Unigram tokenizer, for natural language processing tasks. The primary purpose is to fine-tune the model on a pre-defined dataset with user-specified parameters.\n\n### Key Implementation Details\n\n* Trains the UnigramTokenizer using `train_from_iterator` method, feeding in batches generated by the `batch_iterator`.\n* Modifies the tokenizer's configuration, including token normalization and preprocessing.\n* Defines custom processing templates for class prediction and pair-based classification.\n* Exports the trained model to a Hub repository if specified.\n\n### Notable Dependencies\n\n* `datasets` library (for loading dataset)\n* `Vectorizer()` from `transformers` library\n* UnigramTrainer and Tokenizer classes\n* normalizers, pre_tokenizers, Metaspace, and TemplateProcessing modules from transformers library"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py:208-214

metadata:
{"docstring": "", "end_line": 214, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py", "name": "crop_to_square", "start_line": 208, "type": "function", "chroma:document": "**Summary:**\n\nThis code defines a `crop_to_square` function that takes an image as input and adjusts its bounding box to become a square. The primary purpose is to ensure the aspect ratio of the image remains unchanged.\n\n**Key Implementation Details:**\n\nThe cropping algorithm works dynamically based on the relative size of the image height and width, adjusting the cropping rectangle accordingly.\n\n**Notable Dependencies/Requirements:**\n\n* TensorFlow (TF) library for computing `tf.shape(image)` and applying image transformations (`tf.image.crop_to_bounding_box`).\n \n**Method Signature:**\n\n`def crop_to_square(image: [image]) -> image`\n\nNote: The type hint for the function is missing, assuming it accepts a single image tensor as input."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py:217-240

metadata:
{"docstring": "", "end_line": 240, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py", "name": "load_as_tf_dataset", "start_line": 217, "type": "function", "chroma:document": "**Function Summary:**\n\n`load_as_tf_dataset`\n\nLoads a dataset as TensorFlow Datasets and preprocesses images for use with TensorFlow.\n\n**Primary Purpose:** Load data from TFRecords format into a TensorFlow Dataset, applying image preprocessing steps.\n\n**Key Implementation Details:**\n\n- Loads dataset in \"tensorflow\" format using `with_format(\"tensorflow\")[:]`.\n- Maps input samples to preprocessed images with:\n  - Image reading and decoding\n  - Resizing using bicubic interpolation\n  - Normalization (mean, standard deviation)\n  - Data type conversion\n\n**Notable Dependencies:**\n\n* TensorFlow (`tf`, `tf.data`, `tf.io`)\n\n**External Dependencies:**\n- `crop_to_square` (not shown in this code snippet)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py:243-606

metadata:
{"docstring": "", "end_line": 606, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/contrastive-image-text/run_clip.py", "name": "main", "start_line": 243, "type": "function", "chroma:document": "This is a Python script for training a Contrastive-Image-Text modeling (CITM) model using the Hugging Face Transformers library. The script performs the following tasks:\n\n1. Loads and configures the dataset, including downloading and preprocessing images.\n2. Defines the model architecture, including the CITM model and its corresponding optimizer.\n3. Compiles the model with a custom loss function.\n4. Trains the model using Hugging Face's Trainer class, which handles loading and validation datasets, training, and evaluation.\n5. Evaluates the trained model on a test dataset.\n\nThe script also includes several optional features:\n\n* Pushing the trained model to a GitHub repository, where it can be accessed by other users.\n* Creating a model card with metadata about the CITM model, including its author, tasks, and dataset schema.\n* Tracking metrics during training using Hugging Face's Trainer callbacks."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/token-classification/run_ner.py:203-631

metadata:
{"docstring": "# endregion", "end_line": 631, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/token-classification/run_ner.py", "name": "main", "start_line": 203, "type": "function", "chroma:document": "This is a Python script for fine-tuning a pre-trained language model for token classification tasks. Here's a concise summary:\n\n**Script Overview**\n\nThe script fine-tunes a pre-trained language model on a specific dataset, performs evaluation and prediction, and saves the model.\n\n**Key Features**\n\n1. **Fine-tuning**: Fine-tunes a pre-trained model on a specified dataset.\n2. **Evaluation**: Evaluates the model's performance using metrics such as precision, recall, F1 score, and accuracy.\n3. **Prediction**: Uses the model to make predictions on a test set.\n4. **Saving**: Saves the model to a local directory for later use.\n\n**Notes**\n\n* The script requires pre-trained language models and dataset files.\n* It assumes that the dataset is in TFRecord format.\n* The output directory for saving the model and evaluation results can be specified by the user.\n* If the output directory is not specified, the model will still be saved locally."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/utils_qa.py:31-249

metadata:
{"docstring": "", "end_line": 249, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/utils_qa.py", "name": "postprocess_qa_predictions", "start_line": 31, "type": "function", "chroma:document": "This code is a part of a text classification model, specifically designed to generate answers based on context. Here's a concise summary:\n\n**Functionality**\n\n* The function takes an input dictionary with context and ID as keys, and returns two dictionaries: `all_predictions` and `all_nbest_json`.\n* It identifies the best prediction by comparing the logit scores of the null answer and non-null predictions.\n* The function uses NumPy to compute softmax scores and includes probabilities in the predictions.\n\n**Input/Output**\n\n* Input dictionary with context, ID, and threshold values (not shown in the code snippet).\n* Output dictionaries: `all_predictions` contains best prediction text, and `all_nbest_json` contains all non-null predictions (including probability scores).\n\n**Implementation Details**\n\n* The function uses NumPy to handle floating-point numbers and perform calculations.\n* It uses JSON serialization to convert output data into a human-readable format.\n\nOverall, this code is responsible for determining the most likely answer based on input context and returns two sets of results: one without non-null predictions (null prediction) and another with all predicted answers."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/utils_qa.py:252-443

metadata:
{"docstring": "", "end_line": 443, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/utils_qa.py", "name": "postprocess_qa_predictions_with_beam_search", "start_line": 252, "type": "function", "chroma:document": "Here is a concise summary of the provided function:\n\n**Function Name**: `compute_prediction`\n\n**Description**: This function computes and returns predictions for a given text input. It uses a scoring system to rank possible answers based on their likelihood. It outputs two dictionaries: one containing the best predicted answer (`all_predictions`), and another with null odds scores if the model supports it.\n\n**Input Parameters**:\n\n* `context`: The input text context.\n* `p_mask`: A mask indicating which parts of the input are relevant (not used in this implementation).\n* `output_dir`: An optional directory to save output files to.\n* `n_best_size`: The number of predictions to return.\n* `token_is_max_context`: A function to determine if a token has the maximum context available.\n\n**Return Values**:\n\n* `all_predictions`: A dictionary mapping input IDs to their best predicted answers.\n* `scores_diff_json` (optional): A dictionary with null odds scores for each input ID, if applicable."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/run_qa.py:242-251

metadata:
{"docstring": "# endregion\n# region Helper classes", "end_line": 251, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/run_qa.py", "name": "SavePretrainedCallback", "start_line": 242, "type": "class", "chroma:document": "**SavePretrainedCallback Class Summary**\n\n* **Purpose**: Saves a Keras model using Hugging Face's `save_pretrained()` method after each epoch, preserving weights and necessary metadata.\n\n* **Key Implementation Details**:\n\t+ Saves the model using `self.model.save_pretrained(self.output_dir)`\n\t+ Utilizes Hugging Face's `save_pretrained()` method to save both weights and metadata\n\n* **Notable Dependencies/Requirements**:\n\t+ None explicitly listed\n\t+ Assumes the presence of `Docstring` (potentially a helper class or function)\n\n* **Code Structure**:\n\t+\u7ee7\u627f\u81ea Keras Callback (`super().__init__()`)\n\t+\u6709\u4e00\u4e2a\u8f93\u51fa\u76ee\u5f55 (`self.output_dir`) \u548c\u5176\u4ed6\u53ef\u9009\u53c2\u6570 (`**kwargs`)\n\t+\u5177\u6709\u4e00\u4e2aepoch\u7ed3\u675f\u7684\u56de\u8c03\u4e8b\u4ef6\u5904\u7406\u51fd\u6570\uff08`on_epoch_end()`)"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/run_qa.py:257-826

metadata:
{"docstring": "# endregion", "end_line": 826, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/question-answering/run_qa.py", "name": "main", "start_line": 257, "type": "function", "chroma:document": "This code is a template for a command-line interface (CLI) in PyTorch for training and fine-tuning a pre-trained Hugging Face Transformers model. \n\nThe CLI provides options to train, evaluate, and predict with the model after it has been loaded from a checkpoint file.\n\nKey Features:\n\n1. Training: The CLI allows users to specify the number of epochs, batch size, learning rate, decay factor, and other hyperparameters for training.\n2. Evaluation: Users can specify which datasets to use for evaluation during training (validation and/or test). Advanced metrics are also computed after evaluation is complete.\n3. Prediction: If desired, users can predict answers from a specific dataset using the trained model.\n\nImprovements That Could Be Suggested:\n\n1. Type Hints: The function parameters could have type hints to make the code more readable and self-documenting.\n2. Hyperparameter Tuning: A simple grid search or Random Search for hyperparameters would improve the performance of the model by finding optimal settings.\n3. Early Stopping: Implementing early stopping during training to prevent overfitting.\n4. Model Monitoring: Inbuilt mechanisms like monitoring loss, accuracy, and predictions on validation sets to track model's performance effectively.\n\nOverall, this code serves as a solid foundation for creating custom scripts using PyTorch Transformer models."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/summarization/run_summarization.py:285-745

metadata:
{"docstring": "# endregion", "end_line": 745, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/summarization/run_summarization.py", "name": "main", "start_line": 285, "type": "function", "chroma:document": "Here is a concise summary of the code:\n\nThe code sets up and trains a Hugging Face Transformers model for summarization. It takes in various training parameters, including dataset name, configuration, push to Hub, and output directory. It defines several callbacks for tracking progress, saving models, and logging results.\n\nKey components include:\n\n1. Model setup: importing necessary libraries, loading pre-trained model weights, and initializing model configuration.\n2. Data preparation: loading dataset, tokenizing input/outputs, and building dataloaders.\n3. Callbacks: setting up various callbacks for tracking progress (PushToHubCallback), saving models (model.save_pretrained()), logging results (KerasMetricCallback).\n4. Training loop: training the model using the Transformers `fit` method, passing in training dataset and epochs.\n5. Evaluation: running a standalone evaluation script after training, calculating evaluation metrics using the Hugging Face metric API.\n\nOverall, this code sets up a complete pipeline for training a summarization model with various customizations and logging capabilities."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling/run_mlm.py:228-677

metadata:
{"docstring": "# endregion", "end_line": 677, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling/run_mlm.py", "name": "main", "start_line": 228, "type": "function", "chroma:document": "This code is for training a masked language modeling (MLM) model using the Hugging Face Transformers library. It takes in a large dataset and fine-tunes the model on it to predict the missing words in sentences.\n\nHere's a simplified breakdown of what the code does:\n\n1. **Data Preparation**: The code loads a dataset and splits it into training and validation sets.\n2. **Model Initialization**: A new instance of the MLM model is created with the specified configuration.\n3. **Optimizer and Loss Function**: An optimizer (AdamW) and loss function are defined for the model, which will update the model's parameters to minimize the loss.\n4. **Training Loop**: The training loop iterates through the training data, updating the model's parameters using the optimizer. Every few epochs, the model also evaluates its performance on the validation set.\n5. **Model Evaluation**: After training is complete, the final accuracy, perplexity, and other metrics are calculated for both the training and validation sets.\n6. **Model Saving**: If desired, a local copy of the trained model is saved to disk.\n\nThis code can be used as a base for fine-tuning pre-trained language models on custom datasets. It provides a solid framework for experimenting with different model architectures and hyperparameters."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling/run_clm.py:222-653

metadata:
{"docstring": "# endregion", "end_line": 653, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/language-modeling/run_clm.py", "name": "main", "start_line": 222, "type": "function", "chroma:document": "Here's a concise summary of the code:\n\n**Training a Text Generation Model**\n\nThis is a script that trains a text generation model using the Hugging Face Transformers library. The model takes in a set of input data and generates text based on the task provided.\n\n**Key Steps:**\n\n1. **Data Preparation**: Load the dataset into the model.\n2. **Model Initialization**: Initialize the model with the given parameters (e.g., model name, learning rate).\n3. **Loss Function and Optimizer**: Define the loss function and optimizer for training.\n4. **Model Compilation**: Compile the model with the chosen optimizer and loss function.\n5. **Training**: Train the model on the dataset using the defined hyperparameters.\n6. **Validation**: Validate the model's performance on a separate validation set.\n\n**Key Hyperparameters:**\n\n* Learning rate\n* Number of epochs\n* Batch size\n* Regularization parameters (e.g., weight decay, Adam beta-values)\n\n**Additional Features:**\n\n* Pushing the final trained model to the Hugging Face Hub for sharing.\n* Saving the evaluation results as a JSON file.\n\nOverall, this script provides a flexible framework for training and fine-tuning text generation models using Transformers."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py:65-68

metadata:
{"docstring": "", "end_line": 68, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py", "name": "pil_loader", "start_line": 65, "type": "function", "chroma:document": "**pil_loader Function Summary**\n\n* **Purpose:** Loads and converts an image to RGB format using the Python Imaging Library (PIL).\n\n**Function Details:**\n\n* Reads an image from the specified `path` file in binary mode (`\"rb\"`).\n* Uses PIL's `Image.open()` function to load the image.\n* Converts the loaded image to the \"RGB\" color space using the `convert()` method.\n\n**External Dependencies:**\n- Python Imaging Library (PIL)\n\nNo additional interfaces or classes are implemented or inherited by this function."}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py:185-192

metadata:
{"docstring": "", "end_line": 192, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py", "name": "center_crop", "start_line": 185, "type": "function", "chroma:document": "**Summary:**\n\nThis function `(center_crop)` takes an `image` and optional `size` parameters. It centers a cropped region to the specified size within the original image.\n\n**Implementation Details:**\n\n- **Crop Region Calculation**: The top-left coordinates are calculated based on the difference between the image dimensions and the crop size.\n- **Image Cropping**: The `tf.image.crop_to_bounding_box` function is used to extract the centered cropped region from the original image.\n\n**Notable Dependencies/Requirements:**\n\n- `tf` (TensorFlow) for image processing functionality\n- `(size, size)` or exact integer size as input, defaulting to square if an integer is provided"}


created_at: 2025-02-01 08:47:42
topic: persistent://default/default/1d939003-feb7-4161-8ec5-828008df270e
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py:197-217

metadata:
{"docstring": "# Numpy and TensorFlow compatible version of PyTorch RandomResizedCrop. Code adapted from:\n# https://pytorch.org/vision/main/_modules/torchvision/transforms/transforms.html#RandomResizedCrop", "end_line": 217, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/examples/tensorflow/image-classification/run_image_classification.py", "name": "random_crop", "start_line": 197, "type": "function", "chroma:document": "**Concise Summary**\n\n```python\ndef random_crop(image, \n               scale=(0.08, 1.0), \n               ratio=(3.0 / 4.0, 4.0 / 3.0)):\n    \"\"\"\n    Returns a randomly resized crop of the input image.\n    \n    Args:\n        image: Input image tensor.\n        scale: Tuple of (min_scale, max_scale) for area scaling.\n        ratio: Tuple of (width_ratio, height_ratio) for aspect ratio.\n    \n    Returns:\n        A cropped image tensor.\n    \"\"\"\n```\n\n**Implementation Details**\n\n* The function generates a random crop by iterating 10 times through different scales and ratios to find a valid crop.\n* On the 11th iteration, if no valid crop is found, it falls back to a central crop using a median aspect ratio.\n\n**Dependencies**\n\n* `numpy` for numerical computations\n* No explicit dependencies on TensorFlow (TF) since this code snippet only defines the `random_crop` function.\n\n**Key Insights**\n\n* The function uses both area scaling and aspect ratio scaling for random cropping.\n* It employs a fallback to central crop in case no valid crop is found after 10 iterations."}


