# embeddings_queue

created_at: 2025-02-07 11:33:56
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
id: /home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim.py: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": "CODE:\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n\n\nSUMMARY:\n**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: 2025-02-07 11:33:56
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_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": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Finetune a transformers model on a causal language modeling task\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=None,\n        help=\"The name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--dataset_config_name\",\n        type=str,\n        default=None,\n        help=\"The configuration name of the dataset to use (via the datasets library).\",\n    )\n    parser.add_argument(\n        \"--train_file\", type=str, default=None, help=\"A csv, txt or a json file containing the training data.\"\n    )\n    parser.add_argument(\n        \"--validation_file\", type=str, default=None, help=\"A csv, txt or a json file containing the validation data.\"\n    )\n    parser.add_argument(\n        \"--validation_split_percentage\",\n        default=5,\n        help=\"The percentage of the train set used as validation set in case there's no validation split\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        required=False,\n    )\n    parser.add_argument(\n        \"--config_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained config name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--tokenizer_name\",\n        type=str,\n        default=None,\n        help=\"Pretrained tokenizer name or path if not the same as model_name\",\n    )\n    parser.add_argument(\n        \"--use_slow_tokenizer\",\n        action=\"store_true\",\n        help=\"If passed, will use a slow tokenizer (not backed by the \ud83e\udd17 Tokenizers library).\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\n        \"--model_type\",\n        type=str,\n        default=None,\n        help=\"Model type to use if training from scratch.\",\n        choices=MODEL_TYPES,\n    )\n    parser.add_argument(\n        \"--block_size\",\n        type=int,\n        default=None,\n        help=(\n            \"Optional input sequence length after tokenization. The training dataset will be truncated in block of\"\n            \" this size for training. Default to the model max input length for single sentence inputs (take into\"\n            \" account special tokens).\"\n        ),\n    )\n    parser.add_argument(\n        \"--preprocessing_num_workers\",\n        type=int,\n        default=None,\n        help=\"The number of processes to use for the preprocessing.\",\n    )\n    parser.add_argument(\n        \"--overwrite_cache\", action=\"store_true\", help=\"Overwrite the cached training and evaluation sets\"\n    )\n    parser.add_argument(\n        \"--no_keep_linebreaks\", action=\"store_true\", help=\"Do not keep line breaks when using TXT files.\"\n    )\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--low_cpu_mem_usage\",\n        action=\"store_true\",\n        help=(\n            \"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. \"\n            \"If passed, LLM loading time and RAM consumption will be benefited.\"\n        ),\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if args.dataset_name is None and args.train_file is None and args.validation_file is None:\n        raise ValueError(\"Need either a dataset name or a training/validation file.\")\n    else:\n        if args.train_file is not None:\n            extension = args.train_file.split(\".\")[-1]\n            if extension not in [\"csv\", \"json\", \"txt\"]:\n                raise ValueError(\"`train_file` should be a csv, json or txt file.\")\n        if args.validation_file is not None:\n            extension = args.validation_file.split(\".\")[-1]\n            if extension not in [\"csv\", \"json\", \"txt\"]:\n                raise ValueError(\"`validation_file` should be a csv, json or txt file.\")\n\n    if args.push_to_hub:\n        if args.output_dir is None:\n            raise ValueError(\"Need an `output_dir` to create a repo when `--push_to_hub` is passed.\")\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:33:57
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
encoding: FLOAT32
id:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_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": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_clm_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n    # 'text' is found. You can easily tweak this behavior (see below).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code\n        )\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                args.dataset_name,\n                args.dataset_config_name,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n                trust_remote_code=args.trust_remote_code,\n            )\n    else:\n        data_files = {}\n        dataset_args = {}\n        if args.train_file is not None:\n            data_files[\"train\"] = args.train_file\n            extension = args.train_file.split(\".\")[-1]\n        if args.validation_file is not None:\n            data_files[\"validation\"] = args.validation_file\n            extension = args.validation_file.split(\".\")[-1]\n        if extension == \"txt\":\n            extension = \"text\"\n            dataset_args[\"keep_linebreaks\"] = not args.no_keep_linebreaks\n        raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)\n        # If no validation data is there, validation_split_percentage will be used to divide the dataset.\n        if \"validation\" not in raw_datasets.keys():\n            raw_datasets[\"validation\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[:{args.validation_split_percentage}%]\",\n                **dataset_args,\n            )\n            raw_datasets[\"train\"] = load_dataset(\n                extension,\n                data_files=data_files,\n                split=f\"train[{args.validation_split_percentage}%:]\",\n                **dataset_args,\n            )\n\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.\n\n    # Load pretrained model and tokenizer\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    if args.config_name:\n        config = AutoConfig.from_pretrained(\n            args.config_name,\n            trust_remote_code=args.trust_remote_code,\n        )\n    elif args.model_name_or_path:\n        config = AutoConfig.from_pretrained(\n            args.model_name_or_path,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        config = CONFIG_MAPPING[args.model_type]()\n        logger.warning(\"You are instantiating a new config instance from scratch.\")\n\n    if args.tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    elif args.model_name_or_path:\n        tokenizer = AutoTokenizer.from_pretrained(\n            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code\n        )\n    else:\n        raise ValueError(\n            \"You are instantiating a new tokenizer from scratch. This is not supported by this script. \"\n            \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\n        )\n\n    if args.model_name_or_path:\n        model = AutoModelForCausalLM.from_pretrained(\n            args.model_name_or_path,\n            from_tf=bool(\".ckpt\" in args.model_name_or_path),\n            config=config,\n            low_cpu_mem_usage=args.low_cpu_mem_usage,\n            trust_remote_code=args.trust_remote_code,\n        )\n    else:\n        logger.info(\"Training new model from scratch\")\n        model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)\n\n    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch\n    # on a small vocab and want a smaller embedding size, remove this test.\n    embedding_size = model.get_input_embeddings().weight.shape[0]\n    if len(tokenizer) > embedding_size:\n        model.resize_token_embeddings(len(tokenizer))\n\n    # Preprocessing the datasets.\n    # First we tokenize all the texts.\n    column_names = raw_datasets[\"train\"].column_names\n    text_column_name = \"text\" if \"text\" in column_names else column_names[0]\n\n    def tokenize_function(examples):\n        return tokenizer(examples[text_column_name])\n\n    with accelerator.main_process_first():\n        tokenized_datasets = raw_datasets.map(\n            tokenize_function,\n            batched=True,\n            num_proc=args.preprocessing_num_workers,\n            remove_columns=column_names,\n            load_from_cache_file=not args.overwrite_cache,\n            desc=\"Running tokenizer on dataset\",\n        )\n\n    if args.block_size is None:\n        block_size = tokenizer.model_max_length\n        if block_size > config.max_position_embeddings:\n            logger.warning(\n                f\"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). \"\n                f\"Using block_size={min(1024, config.max_position_embeddings)} instead. You can change that default value by passing --block_size xxx.\"\n            )\n            block_size = min(1024, config.max_position_embeddings)\n    else:\n        if args.block_size > tokenizer.model_max_length:\n            logger.warning(\n        \nSUMMARY:\nThis 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**"}


created_at: 2025-02-07 11:33:59
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Fine-tune a Transformers model on an image classification dataset\")\n    parser.add_argument(\n        \"--dataset_name\",\n        type=str,\n        default=\"cifar10\",\n        help=(\n            \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n            \" dataset).\"\n        ),\n    )\n    parser.add_argument(\"--train_dir\", type=str, default=None, help=\"A folder containing the training data.\")\n    parser.add_argument(\"--validation_dir\", type=str, default=None, help=\"A folder containing the validation data.\")\n    parser.add_argument(\n        \"--max_train_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--max_eval_samples\",\n        type=int,\n        default=None,\n        help=(\n            \"For debugging purposes or quicker training, truncate the number of evaluation examples to this \"\n            \"value if set.\"\n        ),\n    )\n    parser.add_argument(\n        \"--train_val_split\",\n        type=float,\n        default=0.15,\n        help=\"Percent to split off of train for validation\",\n    )\n    parser.add_argument(\n        \"--model_name_or_path\",\n        type=str,\n        help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n        default=\"google/vit-base-patch16-224-in21k\",\n    )\n    parser.add_argument(\n        \"--per_device_train_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the training dataloader.\",\n    )\n    parser.add_argument(\n        \"--per_device_eval_batch_size\",\n        type=int,\n        default=8,\n        help=\"Batch size (per device) for the evaluation dataloader.\",\n    )\n    parser.add_argument(\n        \"--learning_rate\",\n        type=float,\n        default=5e-5,\n        help=\"Initial learning rate (after the potential warmup period) to use.\",\n    )\n    parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n    parser.add_argument(\"--num_train_epochs\", type=int, default=3, help=\"Total number of training epochs to perform.\")\n    parser.add_argument(\n        \"--max_train_steps\",\n        type=int,\n        default=None,\n        help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n    )\n    parser.add_argument(\n        \"--gradient_accumulation_steps\",\n        type=int,\n        default=1,\n        help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n    )\n    parser.add_argument(\n        \"--lr_scheduler_type\",\n        type=SchedulerType,\n        default=\"linear\",\n        help=\"The scheduler type to use.\",\n        choices=[\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\", \"constant\", \"constant_with_warmup\"],\n    )\n    parser.add_argument(\n        \"--num_warmup_steps\", type=int, default=0, help=\"Number of steps for the warmup in the lr scheduler.\"\n    )\n    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"Where to store the final model.\")\n    parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n    parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n    parser.add_argument(\n        \"--hub_model_id\", type=str, help=\"The name of the repository to keep in sync with the local `output_dir`.\"\n    )\n    parser.add_argument(\"--hub_token\", type=str, help=\"The token to use to push to the Model Hub.\")\n    parser.add_argument(\n        \"--trust_remote_code\",\n        action=\"store_true\",\n        help=(\n            \"Whether to trust the execution of code from datasets/models defined on the Hub.\"\n            \" This option should only be set to `True` for repositories you trust and in which you have read the\"\n            \" code, as it will execute code present on the Hub on your local machine.\"\n        ),\n    )\n    parser.add_argument(\n        \"--checkpointing_steps\",\n        type=str,\n        default=None,\n        help=\"Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.\",\n    )\n    parser.add_argument(\n        \"--resume_from_checkpoint\",\n        type=str,\n        default=None,\n        help=\"If the training should continue from a checkpoint folder.\",\n    )\n    parser.add_argument(\n        \"--with_tracking\",\n        action=\"store_true\",\n        help=\"Whether to enable experiment trackers for logging.\",\n    )\n    parser.add_argument(\n        \"--report_to\",\n        type=str,\n        default=\"all\",\n        help=(\n            'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`,'\n            ' `\"wandb\"`, `\"comet_ml\"` and `\"clearml\"`. Use `\"all\"` (default) to report to all integrations. '\n            \"Only applicable when `--with_tracking` is passed.\"\n        ),\n    )\n    parser.add_argument(\n        \"--ignore_mismatched_sizes\",\n        action=\"store_true\",\n        help=\"Whether or not to enable to load a pretrained model whose head dimensions are different.\",\n    )\n    parser.add_argument(\n        \"--image_column_name\",\n        type=str,\n        default=\"image\",\n        help=\"The name of the dataset column containing the image data. Defaults to 'image'.\",\n    )\n    parser.add_argument(\n        \"--label_column_name\",\n        type=str,\n        default=\"label\",\n        help=\"The name of the dataset column containing the labels. Defaults to 'label'.\",\n    )\n    args = parser.parse_args()\n\n    # Sanity checks\n    if args.dataset_name is None and args.train_dir is None and args.validation_dir is None:\n        raise ValueError(\"Need either a dataset name or a training/validation folder.\")\n\n    if args.push_to_hub or args.with_tracking:\n        if args.output_dir is None:\n            raise ValueError(\n                \"Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified.\"\n            )\n\n    if args.output_dir is not None:\n        os.makedirs(args.output_dir, exist_ok=True)\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:00
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef main():\n    args = parse_args()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_image_classification_no_trainer\", args)\n\n    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.\n    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers\n    # in the environment\n    accelerator_log_kwargs = {}\n\n    if args.with_tracking:\n        accelerator_log_kwargs[\"log_with\"] = args.report_to\n        accelerator_log_kwargs[\"project_dir\"] = args.output_dir\n\n    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)\n\n    logger.info(accelerator.state)\n    # Make one log on every process with the configuration for debugging.\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    logger.info(accelerator.state, main_process_only=False)\n    if accelerator.is_local_main_process:\n        datasets.utils.logging.set_verbosity_warning()\n        transformers.utils.logging.set_verbosity_info()\n    else:\n        datasets.utils.logging.set_verbosity_error()\n        transformers.utils.logging.set_verbosity_error()\n\n    # If passed along, set the training seed now.\n    if args.seed is not None:\n        set_seed(args.seed)\n\n    # Handle the repository creation\n    if accelerator.is_main_process:\n        if args.push_to_hub:\n            # Retrieve of infer repo_name\n            repo_name = args.hub_model_id\n            if repo_name is None:\n                repo_name = Path(args.output_dir).absolute().name\n            # Create repo and retrieve repo_id\n            api = HfApi()\n            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id\n\n            with open(os.path.join(args.output_dir, \".gitignore\"), \"w+\") as gitignore:\n                if \"step_*\" not in gitignore:\n                    gitignore.write(\"step_*\\n\")\n                if \"epoch_*\" not in gitignore:\n                    gitignore.write(\"epoch_*\\n\")\n        elif args.output_dir is not None:\n            os.makedirs(args.output_dir, exist_ok=True)\n    accelerator.wait_for_everyone()\n\n    # Get the datasets: you can either provide your own training and evaluation files (see below)\n    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n    # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n    # download the dataset.\n    if args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        dataset = load_dataset(args.dataset_name, trust_remote_code=args.trust_remote_code)\n    else:\n        data_files = {}\n        if args.train_dir is not None:\n            data_files[\"train\"] = os.path.join(args.train_dir, \"**\")\n        if args.validation_dir is not None:\n            data_files[\"validation\"] = os.path.join(args.validation_dir, \"**\")\n        dataset = load_dataset(\n            \"imagefolder\",\n            data_files=data_files,\n        )\n        # See more about loading custom images at\n        # https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder.\n\n    dataset_column_names = dataset[\"train\"].column_names if \"train\" in dataset else dataset[\"validation\"].column_names\n    if args.image_column_name not in dataset_column_names:\n        raise ValueError(\n            f\"--image_column_name {args.image_column_name} not found in dataset '{args.dataset_name}'. \"\n            \"Make sure to set `--image_column_name` to the correct audio column - one of \"\n            f\"{', '.join(dataset_column_names)}.\"\n        )\n    if args.label_column_name not in dataset_column_names:\n        raise ValueError(\n            f\"--label_column_name {args.label_column_name} not found in dataset '{args.dataset_name}'. \"\n            \"Make sure to set `--label_column_name` to the correct text column - one of \"\n            f\"{', '.join(dataset_column_names)}.\"\n        )\n\n    # If we don't have a validation split, split off a percentage of train as validation.\n    args.train_val_split = None if \"validation\" in dataset.keys() else args.train_val_split\n    if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:\n        split = dataset[\"train\"].train_test_split(args.train_val_split)\n        dataset[\"train\"] = split[\"train\"]\n        dataset[\"validation\"] = split[\"test\"]\n\n    # Prepare label mappings.\n    # We'll include these in the model's config to get human readable labels in the Inference API.\n    labels = dataset[\"train\"].features[args.label_column_name].names\n    label2id = {label: str(i) for i, label in enumerate(labels)}\n    id2label = {str(i): label for i, label in enumerate(labels)}\n\n    # Load pretrained model and image processor\n    #\n    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config = AutoConfig.from_pretrained(\n        args.model_name_or_path,\n        num_labels=len(labels),\n        id2label=id2label,\n        label2id=label2id,\n        finetuning_task=\"image-classification\",\n        trust_remote_code=args.trust_remote_code,\n    )\n    image_processor = AutoImageProcessor.from_pretrained(\n        args.model_name_or_path,\n        trust_remote_code=args.trust_remote_code,\n    )\n    model = AutoModelForImageClassification.from_pretrained(\n        args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in args.model_name_or_path),\n        config=config,\n        ignore_mismatched_sizes=args.ignore_mismatched_sizes,\n        trust_remote_code=args.trust_remote_code,\n    )\n\n    # Preprocessing the datasets\n\n    # Define torchvision transforms to be applied to each image.\n    if \"shortest_edge\" in image_processor.size:\n        size = image_processor.size[\"shortest_edge\"]\n    else:\n        size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n    normalize = (\n        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n        if hasattr(image_processor, \"image_mean\") and hasattr(image_processor, \"image_std\")\n        else Lambda(lambda x: x)\n    )\n    train_transforms = Compose(\n        [\n            RandomResizedCrop(size),\n            RandomHorizontalFlip(),\n            ToTensor(),\n            normalize,\n        ]\n    )\n    val_transforms = Compose(\n        [\n            Resize(size),\n            CenterCrop(size),\n            ToTensor(),\n            normalize,\n        ]\n    )\n\n    def preprocess_train(example_batch):\n        \"\"\"Apply _train_transforms across a batch.\"\"\"\n        example_batch[\"pixel_values\"] = [\n            train_transforms(image.convert(\"RGB\")) for image in example_batch[args.image_column_name]\n        ]\n        return example_batch\n\n    def preprocess_val(example_batch):\n        \"\"\"Apply _val_transforms across a batch.\"\"\"\n        example_batch[\"pixel_values\"] = [\n            val_transforms(image.convert(\"RGB\")) for image in example_batch[args.image_column_name]\n        ]\n        return example_batch\n\n    with accelerator.main_process_first():\n        if args.max_train_samples is not None:\n            dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n        # Set the training transforms\n        train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n        if args.max_eval_samples is not None:\n            dataset[\"validation\"] = dataset[\"validation\"].shuffle(seed=args.seed).select(range(args.max_eval_samples))\n        # Set the validation transforms\n        eval_dataset = dataset[\"validation\"].with_transform(preprocess_val)\n\n    # DataLoaders creation:\n    def collate_fn(examples):\n        pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n        labels = torch.tensor([example[args.label_column_name] for example in examples])\n        return {\"pixel_values\": pixel_values, \"labels\": labels}\n\n    train_dataloader = DataLoader(\n        train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size\n    )\n    eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=args.per_device_eval_batch_size)\n\n    # Optimizer\n    # Split weights in two groups, one with weight decay and the other not.\n    no_decay = [\"bias\", \"LayerNorm.weight\"]\n    optimizer_grouped_parameters = [\n        {\n            \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n            \"weight_decay\": args.weight_decay,\n        },\n        {\n            \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n            \"weight_decay\": 0.0,\n        },\n    ]\n    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)\n\n    # Scheduler and math around the number of training steps.\n    overrode_max_train_steps = False\n    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n    if args.max_train_steps is None:\n        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n        overrode_max_train_steps = True\n\n    lr_scheduler = \nSUMMARY:\nThis 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,"}


created_at: 2025-02-07 11:34:00
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef pil_loader(path: str):\n    with open(path, \"rb\") as f:\n        im = Image.open(f)\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:01
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_image_classification\", model_args, data_args)\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n\n    if training_args.should_log:\n        # The default of training_args.log_level is passive, so we set log level at info here to have that default.\n        transformers.utils.logging.set_verbosity_info()\n\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        + f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Initialize our dataset and prepare it for the 'image-classification' task.\n    if data_args.dataset_name is not None:\n        dataset = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n    else:\n        data_files = {}\n        if data_args.train_dir is not None:\n            data_files[\"train\"] = os.path.join(data_args.train_dir, \"**\")\n        if data_args.validation_dir is not None:\n            data_files[\"validation\"] = os.path.join(data_args.validation_dir, \"**\")\n        dataset = load_dataset(\n            \"imagefolder\",\n            data_files=data_files,\n            cache_dir=model_args.cache_dir,\n        )\n\n    dataset_column_names = dataset[\"train\"].column_names if \"train\" in dataset else dataset[\"validation\"].column_names\n    if data_args.image_column_name not in dataset_column_names:\n        raise ValueError(\n            f\"--image_column_name {data_args.image_column_name} not found in dataset '{data_args.dataset_name}'. \"\n            \"Make sure to set `--image_column_name` to the correct audio column - one of \"\n            f\"{', '.join(dataset_column_names)}.\"\n        )\n    if data_args.label_column_name not in dataset_column_names:\n        raise ValueError(\n            f\"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. \"\n            \"Make sure to set `--label_column_name` to the correct text column - one of \"\n            f\"{', '.join(dataset_column_names)}.\"\n        )\n\n    def collate_fn(examples):\n        pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n        labels = torch.tensor([example[data_args.label_column_name] for example in examples])\n        return {\"pixel_values\": pixel_values, \"labels\": labels}\n\n    # If we don't have a validation split, split off a percentage of train as validation.\n    data_args.train_val_split = None if \"validation\" in dataset.keys() else data_args.train_val_split\n    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:\n        split = dataset[\"train\"].train_test_split(data_args.train_val_split)\n        dataset[\"train\"] = split[\"train\"]\n        dataset[\"validation\"] = split[\"test\"]\n\n    # Prepare label mappings.\n    # We'll include these in the model's config to get human readable labels in the Inference API.\n    labels = dataset[\"train\"].features[data_args.label_column_name].names\n    label2id, id2label = {}, {}\n    for i, label in enumerate(labels):\n        label2id[label] = str(i)\n        id2label[str(i)] = label\n\n    # Load the accuracy metric from the datasets package\n    metric = evaluate.load(\"accuracy\", cache_dir=model_args.cache_dir)\n\n    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a\n    # predictions and label_ids field) and has to return a dictionary string to float.\n    def compute_metrics(p):\n        \"\"\"Computes accuracy on a batch of predictions\"\"\"\n        return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)\n\n    config = AutoConfig.from_pretrained(\n        model_args.config_name or model_args.model_name_or_path,\n        num_labels=len(labels),\n        label2id=label2id,\n        id2label=id2label,\n        finetuning_task=\"image-classification\",\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    model = AutoModelForImageClassification.from_pretrained(\n        model_args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n        \nSUMMARY:\n**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  "}


created_at: 2025-02-07 11:34:01
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef list_field(default=None, metadata=None):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:01
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef create_vocabulary_from_data(\n    datasets: DatasetDict,\n    word_delimiter_token: Optional[str] = None,\n    unk_token: Optional[str] = None,\n    pad_token: Optional[str] = None,\n):\n    # Given training and test labels create vocabulary\n    def extract_all_chars(batch):\n        all_text = \" \".join(batch[\"target_text\"])\n        vocab = list(set(all_text))\n        return {\"vocab\": [vocab], \"all_text\": [all_text]}\n\n    vocabs = datasets.map(\n        extract_all_chars,\n        batched=True,\n        batch_size=-1,\n        keep_in_memory=True,\n        remove_columns=datasets[\"train\"].column_names,\n    )\n\n    # take union of all unique characters in each dataset\n    vocab_set = functools.reduce(\n        lambda vocab_1, vocab_2: set(vocab_1[\"vocab\"][0]) | set(vocab_2[\"vocab\"][0]), vocabs.values()\n    )\n\n    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}\n\n    # replace white space with delimiter token\n    if word_delimiter_token is not None:\n        vocab_dict[word_delimiter_token] = vocab_dict[\" \"]\n        del vocab_dict[\" \"]\n\n    # add unk and pad token\n    if unk_token is not None:\n        vocab_dict[unk_token] = len(vocab_dict)\n\n    if pad_token is not None:\n        vocab_dict[pad_token] = len(vocab_dict)\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:02
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_speech_recognition_ctc_adapter\", model_args, data_args)\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    # Set the verbosity to info of the Transformers logger (on main process only):\n    if is_main_process(training_args.local_rank):\n        transformers.utils.logging.set_verbosity_info()\n    logger.info(\"Training/evaluation parameters %s\", training_args)\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # 1. First, let's load the dataset\n    raw_datasets = DatasetDict()\n\n    if training_args.do_train:\n        raw_datasets[\"train\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.train_split_name,\n            token=data_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n\n        if data_args.audio_column_name not in raw_datasets[\"train\"].column_names:\n            raise ValueError(\n                f\"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'.\"\n                \" Make sure to set `--audio_column_name` to the correct audio column - one of\"\n                f\" {', '.join(raw_datasets['train'].column_names)}.\"\n            )\n\n        if data_args.text_column_name not in raw_datasets[\"train\"].column_names:\n            raise ValueError(\n                f\"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. \"\n                \"Make sure to set `--text_column_name` to the correct text column - one of \"\n                f\"{', '.join(raw_datasets['train'].column_names)}.\"\n            )\n\n        if data_args.max_train_samples is not None:\n            raw_datasets[\"train\"] = raw_datasets[\"train\"].select(range(data_args.max_train_samples))\n\n    if training_args.do_eval:\n        raw_datasets[\"eval\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.eval_split_name,\n            token=data_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n\n        if data_args.max_eval_samples is not None:\n            raw_datasets[\"eval\"] = raw_datasets[\"eval\"].select(range(data_args.max_eval_samples))\n\n    # 2. We remove some special characters from the datasets\n    # that make training complicated and do not help in transcribing the speech\n    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic\n    # that could be easily picked up by the model\n    chars_to_ignore_regex = (\n        f'[{\"\".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None\n    )\n    text_column_name = data_args.text_column_name\n\n    def remove_special_characters(batch):\n        if chars_to_ignore_regex is not None:\n            batch[\"target_text\"] = re.sub(chars_to_ignore_regex, \"\", batch[text_column_name]).lower() + \" \"\n        else:\n            batch[\"target_text\"] = batch[text_column_name].lower() + \" \"\n        return batch\n\n    with training_args.main_process_first(desc=\"dataset map special characters removal\"):\n        raw_datasets = raw_datasets.map(\n            remove_special_characters,\n            remove_columns=[text_column_name],\n            desc=\"remove special characters from datasets\",\n        )\n\n    # save special tokens for tokenizer\n    word_delimiter_token = data_args.word_delimiter_token\n    unk_token = data_args.unk_token\n    pad_token = data_args.pad_token\n\n    # 3. Next, let's load the config as we might need it to create\n    # the tokenizer\n    # load config\n    config = AutoConfig.from_pretrained(\n        model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        token=data_args.token,\n        trust_remote_code=data_args.trust_remote_code,\n    )\n\n    # 4. Next, if no tokenizer file is defined,\n    # we create the vocabulary of the model by extracting all unique characters from\n    # the training and evaluation datasets\n    # We need to make sure that only first rank saves vocabulary\n    # make sure all processes wait until vocab is created\n    tokenizer_name_or_path = model_args.tokenizer_name_or_path\n    tokenizer_kwargs = {}\n\n    vocab_dict = {}\n    if tokenizer_name_or_path is not None:\n        # load vocabulary of other adapter languages so that new language can be appended\n        tokenizer = AutoTokenizer.from_pretrained(\n            tokenizer_name_or_path,\n            token=data_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n        vocab_dict = tokenizer.vocab.copy()\n        if tokenizer.target_lang is None:\n            raise ValueError(\"Make sure to load a multi-lingual tokenizer with a set target language.\")\n\n        if data_args.target_language in tokenizer.vocab and not data_args.overwrite_lang_vocab:\n            logger.info(\n                \"Adapter language already exists.\"\n                \" Skipping vocabulary creating. If you want to create a new vocabulary\"\n                f\" for {data_args.target_language} make sure to add '--overwrite_lang_vocab'\"\n            )\n        else:\n            tokenizer_name_or_path = None\n\n    if tokenizer_name_or_path is None:\n        # save vocab in training output dir\n        tokenizer_name_or_path = training_args.output_dir\n\n        vocab_file = os.path.join(tokenizer_name_or_path, \"vocab.json\")\n\n        with training_args.main_process_first():\n            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):\n                try:\n                    os.remove(vocab_file)\n                except OSError:\n                    # in shared file-systems it might be the case that\n                    # two processes try to delete the vocab file at the some time\n                    pass\n\n        with training_args.main_process_first(desc=\"dataset map vocabulary creation\"):\n            if not os.path.isfile(vocab_file):\n                os.makedirs(tokenizer_name_or_path, exist_ok=True)\n                lang_dict = create_vocabulary_from_data(\n                    raw_datasets,\n                    word_delimiter_token=word_delimiter_token,\n                    unk_token=unk_token,\n                    pad_token=pad_token,\n                )\n\n                # if we doing adapter language training, save\n                # vocab with adpter language\n                if data_args.target_language is not None:\n                    vocab_dict[data_args.target_language] = lang_dict\n\n                # save vocab dict to be loaded into tokenizer\n                with open(vocab_file, \"w\") as file:\n                    json.dump(vocab_dict, file)\n\n        # if tokenizer has just been created\n        # it is defined by `tokenizer_class` if present in config else by `model_type`\n        tokenizer_kwargs = {\n            \"config\": config if config.tokenizer_class is not None else None,\n            \"tokenizer_type\": config.model_type if config.tokenizer_class is None else None,\n            \"unk_token\": unk_token,\n            \"pad_token\": pad_token,\n            \"word_delimiter_token\": word_delimiter_token,\n            \"target_lang\": data_args.target_language,\n        }\n\n    # 5. Now we can instantiate the feature extractor, tokenizer and model\n    # Note for distributed traini\nSUMMARY:\nHere 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 "}


created_at: 2025-02-07 11:34:03
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef main():\n    # 1. Parse input arguments\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\n\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_speech_recognition_seq2seq\", model_args, data_args)\n\n    # 2. Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.set_verbosity(log_level)\n    transformers.utils.logging.enable_default_handler()\n    transformers.utils.logging.enable_explicit_format()\n\n    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Set the verbosity to info of the Transformers logger (on main process only):\n    if is_main_process(training_args.local_rank):\n        transformers.utils.logging.set_verbosity_info()\n    logger.info(\"Training/evaluation parameters %s\", training_args)\n\n    # 3. Detecting last checkpoint and eventually continue from last checkpoint\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # 4. Load dataset\n    raw_datasets = DatasetDict()\n\n    if training_args.do_train:\n        raw_datasets[\"train\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.train_split_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n\n    if training_args.do_eval:\n        raw_datasets[\"eval\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.eval_split_name,\n            cache_dir=model_args.cache_dir,\n            token=model_args.token,\n            trust_remote_code=model_args.trust_remote_code,\n        )\n\n    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:\n        raise ValueError(\n            f\"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. \"\n            \"Make sure to set `--audio_column_name` to the correct audio column - one of \"\n            f\"{', '.join(next(iter(raw_datasets.values())).column_names)}.\"\n        )\n\n    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:\n        raise ValueError(\n            f\"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. \"\n            \"Make sure to set `--text_column_name` to the correct text column - one of \"\n            f\"{', '.join(next(iter(raw_datasets.values())).column_names)}.\"\n        )\n\n    # 5. Load pretrained model, tokenizer, and feature extractor\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    config = AutoConfig.from_pretrained(\n        model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n\n    # SpecAugment for whisper models\n    if getattr(config, \"model_type\", None) == \"whisper\":\n        config.update({\"apply_spec_augment\": model_args.apply_spec_augment})\n\n    feature_extractor = AutoFeatureExtractor.from_pretrained(\n        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    tokenizer = AutoTokenizer.from_pretrained(\n        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        use_fast=model_args.use_fast_tokenizer,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n    model = AutoModelForSpeechSeq2Seq.from_pretrained(\n        model_args.model_name_or_path,\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        token=model_args.token,\n        trust_remote_code=model_args.trust_remote_code,\n    )\n\n    if model.config.decoder_start_token_id is None:\n        raise ValueError(\"Make sure that `config.decoder_start_token_id` is correctly defined\")\n\n    if model_args.freeze_feature_encoder:\n        model.freeze_feature_encoder()\n\n    if model_args.freeze_encoder:\n        model.freeze_encoder()\n        model.model.encoder.gradient_checkpointing = False\n\n    if hasattr(model.generation_config, \"is_multilingual\") and model.generation_config.is_multilingual:\n        # We only need to set the language and task ids in a multilingual setting\n        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)\n        model.generation_config.language = data_args.language\n        model.generation_config.task = data_args.task\n    elif data_args.language is not None:\n        raise ValueError(\n            \"Setting language token for an English-only checkpoint is not permitted. The language argument should \"\n            \"only be set for multilingual checkpoints.\"\n        )\n\n    # TODO (Sanchit): deprecate these arguments in v4.41\n    if model_args.forced_decoder_ids is not None:\n        logger.warning(\n            \"The use of `forced_decoder_ids` is deprecated and will be removed in v4.41.\"\n            \"Please use the `language` and `task` arguments instead\"\n        )\n        model.generation_config.forced_decoder_ids = model_args.forced_decoder_ids\n    else:\n        model.generation_config.forced_decoder_ids = None\n        model.config.forced_decoder_ids = None\n\n    if model_args.suppress_tokens is not None:\n        logger.warning(\n            \"The use of `suppress_tokens` is deprecated and will be removed in v4.41.\"\n            \"Should you need `suppress_tokens`, please manually set them in the fine-tuning script.\"\n        )\n        model.generation_config.suppress_tokens = model_args.suppress_tokens\n\n    # 6. Resample speech dataset if necessary\n    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate\n    if dataset_sampling_rate != feature_extractor.sampling_rate:\n        raw_datasets = raw_datasets.cast_column(\n            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)\n        )\n\n    # 7. Preprocessing the datasets.\n    # We need to read the audio files as arrays and tokenize the targets.\n    max_input_length = data_args.max_duration_in_seconds * feature_extractor.samp\nSUMMARY:\nThis 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 inclu"}


created_at: 2025-02-07 11:34:03
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef list_field(default=None, metadata=None):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:04
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef create_vocabulary_from_data(\n    datasets: DatasetDict,\n    word_delimiter_token: Optional[str] = None,\n    unk_token: Optional[str] = None,\n    pad_token: Optional[str] = None,\n):\n    # Given training and test labels create vocabulary\n    def extract_all_chars(batch):\n        all_text = \" \".join(batch[\"target_text\"])\n        vocab = list(set(all_text))\n        return {\"vocab\": [vocab], \"all_text\": [all_text]}\n\n    vocabs = datasets.map(\n        extract_all_chars,\n        batched=True,\n        batch_size=-1,\n        keep_in_memory=True,\n        remove_columns=datasets[\"train\"].column_names,\n    )\n\n    # take union of all unique characters in each dataset\n    vocab_set = functools.reduce(\n        lambda vocab_1, vocab_2: set(vocab_1[\"vocab\"][0]) | set(vocab_2[\"vocab\"][0]), vocabs.values()\n    )\n\n    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}\n\n    # replace white space with delimiter token\n    if word_delimiter_token is not None:\n        vocab_dict[word_delimiter_token] = vocab_dict[\" \"]\n        del vocab_dict[\" \"]\n\n    # add unk and pad token\n    if unk_token is not None:\n        vocab_dict[unk_token] = len(vocab_dict)\n\n    if pad_token is not None:\n        vocab_dict[pad_token] = len(vocab_dict)\n\n\n\nSUMMARY:\nHere'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: 2025-02-07 11:34:04
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n    # information sent is the one passed as arguments along with your Python/PyTorch versions.\n    send_example_telemetry(\"run_speech_recognition_ctc\", model_args, data_args)\n\n    # Detecting last checkpoint.\n    last_checkpoint = None\n    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n        last_checkpoint = get_last_checkpoint(training_args.output_dir)\n        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n            raise ValueError(\n                f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n                \"Use --overwrite_output_dir to overcome.\"\n            )\n        elif last_checkpoint is not None:\n            logger.info(\n                f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n                \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n            )\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, \"\n        f\"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}\"\n    )\n    # Set the verbosity to info of the Transformers logger (on main process only):\n    if is_main_process(training_args.local_rank):\n        transformers.utils.logging.set_verbosity_info()\n    logger.info(\"Training/evaluation parameters %s\", training_args)\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # 1. First, let's load the dataset\n    raw_datasets = DatasetDict()\n\n    if training_args.do_train:\n        raw_datasets[\"train\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.train_split_name,\n            token=data_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n\n        if data_args.audio_column_name not in raw_datasets[\"train\"].column_names:\n            raise ValueError(\n                f\"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'.\"\n                \" Make sure to set `--audio_column_name` to the correct audio column - one of\"\n                f\" {', '.join(raw_datasets['train'].column_names)}.\"\n            )\n\n        if data_args.text_column_name not in raw_datasets[\"train\"].column_names:\n            raise ValueError(\n                f\"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. \"\n                \"Make sure to set `--text_column_name` to the correct text column - one of \"\n                f\"{', '.join(raw_datasets['train'].column_names)}.\"\n            )\n\n        if data_args.max_train_samples is not None:\n            raw_datasets[\"train\"] = raw_datasets[\"train\"].select(range(data_args.max_train_samples))\n\n    if training_args.do_eval:\n        raw_datasets[\"eval\"] = load_dataset(\n            data_args.dataset_name,\n            data_args.dataset_config_name,\n            split=data_args.eval_split_name,\n            token=data_args.token,\n            trust_remote_code=data_args.trust_remote_code,\n        )\n\n        if data_args.max_eval_samples is not None:\n            raw_datasets[\"eval\"] = raw_datasets[\"eval\"].select(range(data_args.max_eval_samples))\n\n    # 2. We remove some special characters from the datasets\n    # that make training complicated and do not help in transcribing the speech\n    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic\n    # that could be easily picked up by the model\n    chars_to_ignore_regex = (\n        f'[{\"\".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None\n    )\n    text_column_name = data_args.text_column_name\n\n    def remove_special_characters(batch):\n        if chars_to_ignore_regex is not None:\n            batch[\"target_text\"] = re.sub(chars_to_ignore_regex, \"\", batch[text_column_name]).lower() + \" \"\n        else:\n            batch[\"target_text\"] = batch[text_column_name].lower() + \" \"\n        return batch\n\n    with training_args.main_process_first(desc=\"dataset map special characters removal\"):\n        raw_datasets = raw_datasets.map(\n            remove_special_characters,\n            remove_columns=[text_column_name],\n            desc=\"remove special characters from datasets\",\n        )\n\n    # save special tokens for tokenizer\n    word_delimiter_token = data_args.word_delimiter_token\n    unk_token = data_args.unk_token\n    pad_token = data_args.pad_token\n\n    # 3. Next, let's load the config as we might need it to create\n    # the tokenizer\n    # load config\n    config = AutoConfig.from_pretrained(\n        model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        token=data_args.token,\n        trust_remote_code=data_args.trust_remote_code,\n    )\n\n    # 4. Next, if no tokenizer file is defined,\n    # we create the vocabulary of the model by extracting all unique characters from\n    # the training and evaluation datasets\n    # We need to make sure that only first rank saves vocabulary\n    # make sure all processes wait until vocab is created\n    tokenizer_name_or_path = model_args.tokenizer_name_or_path\n    tokenizer_kwargs = {}\n    if tokenizer_name_or_path is None:\n        # save vocab in training output dir\n        tokenizer_name_or_path = training_args.output_dir\n\n        vocab_file = os.path.join(tokenizer_name_or_path, \"vocab.json\")\n\n        with training_args.main_process_first():\n            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):\n                try:\n                    os.remove(vocab_file)\n                except OSError:\n                    # in shared file-systems it might be the case that\n                    # two processes try to delete the vocab file at the some time\n                    pass\n\n        with training_args.main_process_first(desc=\"dataset map vocabulary creation\"):\n            if not os.path.isfile(vocab_file):\n                os.makedirs(tokenizer_name_or_path, exist_ok=True)\n                vocab_dict = create_vocabulary_from_data(\n                    raw_datasets,\n                    word_delimiter_token=word_delimiter_token,\n                    unk_token=unk_token,\n                    pad_token=pad_token,\n                )\n\n                # save vocab dict to be loaded into tokenizer\n                with open(vocab_file, \"w\") as file:\n                    json.dump(vocab_dict, file)\n\n        # if tokenizer has just been created\n        # it is defined by `tokenizer_class` if present in config else by `model_type`\n        tokenizer_kwargs = {\n            \"config\": config if config.tokenizer_class is not None else None,\n            \"tokenizer_type\": config.model_type if config.tokenizer_class is None else None,\n            \"unk_token\": unk_token,\n            \"pad_token\": pad_token,\n            \"word_delimiter_token\": word_delimiter_token,\n        }\n\n    # 5. Now we can instantiate the feature extractor, tokenizer and model\n    # Note for distributed training, the .from_pretrained methods guarantee that only\n    # one local process can concurrently download model & vocab.\n\n    # load feature_extractor and tokenizer\n    tokenizer = AutoTokenizer.from_pretrained(\n        tokenizer_name_or_path,\n        token=data_args.token,\n        trust_remote_code=data_args.trust_remote_code,\n        **tokenizer_kwargs,\n    )\n    feature_extractor = AutoFeatureExtractor.from_pretrained(\n        model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        token=data_args.token,\n        trust_remote_code=data_args.trust_remote_code,\n    )\n\n    # adapt config\n    config.update(\n        {\n            \"feat_proj_dropout\": model_args.feat_proj_dropout,\n            \"attention_dropout\": model_args.attention_dropout,\n            \"hidden_dropout\": model_args.hidden_dropout,\n            \"final_dropout\": model_args.final_dropout,\n       \nSUMMARY:\nThis 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 n"}


created_at: 2025-02-07 11:34:05
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n# Example where we only want to only modify the docstring\nclass MyNewModel2Config(LlamaConfig):\n    r\"\"\"\n    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma\n    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Gemma-7B.\n    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n    Args:\n        vocab_size (`int`, *optional*, defaults to 256000):\n            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`GemmaModel`]\n    ```python\n    >>> from transformers import GemmaModel, GemmaConfig\n    >>> # Initializing a Gemma gemma-7b style configuration\n    >>> configuration = GemmaConfig()\n    >>> # Initializing a model from the gemma-7b style configuration\n    >>> model = GemmaModel(configuration)\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:05
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n# Example where alllllll the dependencies are fetched to just copy the entire class\nclass MyNewModel2ForSequenceClassification(GemmaForSequenceClassification):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:05
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n# Check that we can correctly change the prefix (here add Text part at the end of the name)\nclass Multimodal1TextModel(LlamaModel):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:05
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n# here there is no `ARG` so we are gonna take parent doc\nclass MyNewModelConfig(LlamaConfig):\n    r\"\"\"\n    new_param (`int`, *optional*, defaults to `False`):\n        A fun new parameter\n    \"\"\"\n\n    def __init__(self, mlp_bias=True, new_param=0, **super_kwargs):\n        super().__init__(self, **super_kwargs)\n        self.mlp_bias = mlp_bias\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:06
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass NewModelConfig(GemmaConfig):\n    def __init__(\n        self,\n        vocab_size=256030,\n        hidden_size=64,\n        intermediate_size=90,\n        num_hidden_layers=28,\n        num_attention_heads=16,\n        num_key_value_heads=16,\n        head_dim=256,\n        hidden_act=\"gelu_pytorch_tanh\",\n        hidden_activation=None,\n        max_position_embeddings=1500,\n        initializer_range=0.02,\n        rms_norm_eps=1e-6,\n        use_cache=True,\n        pad_token_id=0,\n        eos_token_id=1,\n        bos_token_id=2,\n        tie_word_embeddings=True,\n        rope_theta=10000.0,\n        attention_bias=False,\n        attention_dropout=0.0,\n        **kwargs,\n    ):\n        super().__init__(self, **kwargs)\n\n    @property\n    def num_heads(self):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:07
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass ImgprocModelImageProcessor(BaseImageProcessor):\n    r\"\"\"\n    Constructs a IMGPROC_MODEL image processor.\n\n    Args:\n        do_resize (`bool`, *optional*, defaults to `True`):\n            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the\n            `do_resize` parameter in the `preprocess` method.\n        size (`dict`, *optional*, defaults to `{\"height\": 384, \"width\": 384}`):\n            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`\n            method.\n        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):\n            Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be\n            overridden by the `resample` parameter in the `preprocess` method.\n        do_rescale (`bool`, *optional*, defaults to `True`):\n            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the\n            `do_rescale` parameter in the `preprocess` method.\n        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):\n            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be\n            overridden by the `rescale_factor` parameter in the `preprocess` method.\n        do_normalize (`bool`, *optional*, defaults to `True`):\n            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`\n            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.\n        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):\n            Mean to use if normalizing the image. This is a float or list of floats the length of the number of\n            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be\n            overridden by the `image_mean` parameter in the `preprocess` method.\n        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):\n            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the\n            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.\n            Can be overridden by the `image_std` parameter in the `preprocess` method.\n        do_convert_rgb (`bool`, *optional*, defaults to `True`):\n            Whether to convert the image to RGB.\n    \"\"\"\n\n    model_input_names = [\"pixel_values\"]\n\n    def __init__(\n        self,\n        do_resize: bool = True,\n        size: Dict[str, int] = None,\n        resample: PILImageResampling = PILImageResampling.BICUBIC,\n        do_rescale: bool = True,\n        rescale_factor: Union[int, float] = 1 / 255,\n        do_normalize: bool = True,\n        image_mean: Optional[Union[float, List[float]]] = None,\n        image_std: Optional[Union[float, List[float]]] = None,\n        do_convert_rgb: bool = True,\n        **kwargs,\n    ) -> None:\n        super().__init__(**kwargs)\n        size = size if size is not None else {\"height\": 384, \"width\": 384}\n        size = get_size_dict(size, default_to_square=True)\n\n        self.do_resize = do_resize\n        self.size = size\n        self.resample = resample\n        self.do_rescale = do_rescale\n        self.rescale_factor = rescale_factor\n        self.do_normalize = do_normalize\n        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN\n        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD\n        self.do_convert_rgb = do_convert_rgb\n\n    def resize(\n        self,\n        image: np.ndarray,\n        size: Dict[str, int],\n        resample: PILImageResampling = PILImageResampling.BICUBIC,\n        data_format: Optional[Union[str, ChannelDimension]] = None,\n        input_data_format: Optional[Union[str, ChannelDimension]] = None,\n        **kwargs,\n    ) -> np.ndarray:\n        \"\"\"\n        Resize an image to `(size[\"height\"], size[\"width\"])`.\n\n        Args:\n            image (`np.ndarray`):\n                Image to resize.\n            size (`Dict[str, int]`):\n                Dictionary in the format `{\"height\": int, \"width\": int}` specifying the size of the output image.\n            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):\n                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.\n            data_format (`ChannelDimension` or `str`, *optional*):\n                The channel dimension format for the output image. If unset, the channel dimension format of the input\n                image is used. Can be one of:\n                - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n                - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n                - `\"none\"` or `ChannelDimension.NONE`: image in (height, width) format.\n            input_data_format (`ChannelDimension` or `str`, *optional*):\n                The channel dimension format for the input image. If unset, the channel dimension format is inferred\n                from the input image. Can be one of:\n                - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n                - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n                - `\"none\"` or `ChannelDimension.NONE`: image in (height, width) format.\n\n        Returns:\n            `np.ndarray`: The resized image.\n        \"\"\"\n        size = get_size_dict(size)\n        if \"height\" not in size or \"width\" not in size:\n            raise ValueError(f\"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}\")\n        output_size = (size[\"height\"], size[\"width\"])\n        return resize(\n            image,\n            size=output_size,\n            resample=resample,\n            data_format=data_format,\n            input_data_format=input_data_format,\n            **kwargs,\n        )\n\n    @filter_out_non_signature_kwargs()\n    def preprocess(\n        self,\n        images: ImageInput,\n        do_resize: Optional[bool] = None,\n        size: Optional[Dict[str, int]] = None,\n        resample: PILImageResampling = None,\n        do_rescale: Optional[bool] = None,\n        rescale_factor: Optional[float] = None,\n        do_normalize: Optional[bool] = None,\n        image_mean: Optional[Union[float, List[float]]] = None,\n        image_std: Optional[Union[float, List[float]]] = None,\n        return_tensors: Optional[Union[str, TensorType]] = None,\n        do_convert_rgb: bool = None,\n        data_format: ChannelDimension = ChannelDimension.FIRST,\n        input_data_format: Optional[Union[str, ChannelDimension]] = None,\n    ) -> PIL.Image.Image:\n        \"\"\"\n        Preprocess an image or batch of images.\n\n        Args:\n            images (`ImageInput`):\n                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If\n                passing in images with pixel values between 0 and 1, set `do_rescale=False`.\n            do_resize (`bool`, *optional*, defaults to `self.do_resize`):\n                Whether to resize the image.\n            size (`Dict[str, int]`, *optional*, defaults to `self.size`):\n                Controls the size of the image after `resize`. The shortest edge of the image is resized to\n                `size[\"shortest_edge\"]` whilst preserving the aspect ratio. If the longest edge of this resized image\n                is > `int(size[\"shortest_edge\"] * (1333 / 800))`, then the image is resized again to make the longest\n                edge equal to `int(size[\"shortest_edge\"] * (1333 / 800))`.\n            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):\n                Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.\n            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):\n                Whether to rescale the image values between [0 - 1].\n            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):\n                Rescale factor to rescale the image by if `do_rescale` is set to `True`.\n            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):\n                Whether to normalize the image.\n            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):\n                Image mean to normalize the image by if `do_normalize` is set to `True`.\n            image_std (`float` or `List[float]`, *optional*, defa\nSUMMARY:\nThis 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  - C"}


created_at: 2025-02-07 11:34:07
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyRMSNorm(nn.Module):\n    def __init__(self, hidden_size, eps=1e-6):\n        \"\"\"\n        DummyRMSNorm is equivalent to T5LayerNorm\n        \"\"\"\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(hidden_size))\n        self.variance_epsilon = eps\n\n    def forward(self, hidden_states):\n        input_dtype = hidden_states.dtype\n        hidden_states = hidden_states.to(torch.float32)\n        variance = hidden_states.pow(2).mean(-1, keepdim=True)\n        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)\n        return self.weight * hidden_states.to(input_dtype)\n\n    def extra_repr(self):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:07
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyRotaryEmbedding(nn.Module):\n    def __init__(\n        self,\n        config: DummyConfig,\n        device=None,\n    ):\n        super().__init__()\n        self.rope_kwargs = {}\n        # BC: \"rope_type\" was originally \"type\"\n        if hasattr(config, \"rope_scaling\") and config.rope_scaling is not None:\n            self.rope_type = config.rope_scaling.get(\"rope_type\", config.rope_scaling.get(\"type\"))\n        else:\n            self.rope_type = \"default\"\n        self.max_seq_len_cached = config.max_position_embeddings\n        self.original_max_seq_len = config.max_position_embeddings\n\n        self.config = config\n        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]\n\n        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)\n        self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n        self.original_inv_freq = self.inv_freq\n\n    def _dynamic_frequency_update(self, position_ids, device):\n        \"\"\"\n        dynamic RoPE layers should recompute `inv_freq` in the following situations:\n        1 - growing beyond the cached sequence length (allow scaling)\n        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)\n        \"\"\"\n        seq_len = torch.max(position_ids) + 1\n        if seq_len > self.max_seq_len_cached:  # growth\n            inv_freq, self.attention_scaling = self.rope_init_fn(\n                self.config, device, seq_len=seq_len, **self.rope_kwargs\n            )\n            self.register_buffer(\"inv_freq\", inv_freq, persistent=False)  # TODO joao: may break with compilation\n            self.max_seq_len_cached = seq_len\n\n        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset\n            self.register_buffer(\"inv_freq\", self.original_inv_freq, persistent=False)\n            self.max_seq_len_cached = self.original_max_seq_len\n\n    @torch.no_grad()\n    def forward(self, x, position_ids):\n        if \"dynamic\" in self.rope_type:\n            self._dynamic_frequency_update(position_ids, device=x.device)\n\n        # Core RoPE block\n        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)\n        position_ids_expanded = position_ids[:, None, :].float()\n        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)\n        device_type = x.device.type\n        device_type = device_type if isinstance(device_type, str) and device_type != \"mps\" else \"cpu\"\n        with torch.autocast(device_type=device_type, enabled=False):\n            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)\n            emb = torch.cat((freqs, freqs), dim=-1)\n            cos = emb.cos()\n            sin = emb.sin()\n\n        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention\n        cos = cos * self.attention_scaling\n        sin = sin * self.attention_scaling\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:08
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyMLP(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.config = config\n        self.hidden_size = config.hidden_size\n        self.intermediate_size = config.intermediate_size\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        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)\n        self.act_fn = ACT2FN[config.hidden_act]\n\n    def forward(self, x):\n        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:08
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef rotate_half(x):\n    \"\"\"Rotates half the hidden dims of the input.\"\"\"\n    x1 = x[..., : x.shape[-1] // 4]\n    x2 = x[..., x.shape[-1] // 4 :]\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:08
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):\n    \"\"\"Applies Rotary Position Embedding to the query and key tensors.\n\n    Args:\n        q (`torch.Tensor`): The query tensor.\n        k (`torch.Tensor`): The key tensor.\n        cos (`torch.Tensor`): The cosine part of the rotary embedding.\n        sin (`torch.Tensor`): The sine part of the rotary embedding.\n        position_ids (`torch.Tensor`, *optional*):\n            Deprecated and unused.\n        unsqueeze_dim (`int`, *optional*, defaults to 1):\n            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and\n            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note\n            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and\n            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes\n            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have\n            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.\n    Returns:\n        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.\n    \"\"\"\n    cos = cos.unsqueeze(unsqueeze_dim)\n    sin = sin.unsqueeze(unsqueeze_dim)\n    q_embed = (q * cos) + (rotate_half(q) * sin)\n    k_embed = (k * cos) + (rotate_half(k) * sin)\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:09
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:\n    \"\"\"\n    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,\n    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)\n    \"\"\"\n    batch, num_key_value_heads, slen, head_dim = hidden_states.shape\n    if n_rep == 1:\n        return hidden_states\n    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:09
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\ndef eager_attention_forward(\n    module: nn.Module,\n    query: torch.Tensor,\n    key: torch.Tensor,\n    value: torch.Tensor,\n    attention_mask: Optional[torch.Tensor],\n    scaling: float,\n    dropout: float = 0.0,\n    **kwargs,\n):\n    key_states = repeat_kv(key, module.num_key_value_groups)\n    value_states = repeat_kv(value, module.num_key_value_groups)\n\n    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling\n    if attention_mask is not None:\n        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]\n        attn_weights = attn_weights + causal_mask\n\n    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)\n    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)\n    attn_output = torch.matmul(attn_weights, value_states)\n    attn_output = attn_output.transpose(1, 2).contiguous()\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:09
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyAttention(nn.Module):\n    \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n    def __init__(self, config: DummyConfig, layer_idx: int):\n        super().__init__()\n        self.config = config\n        self.layer_idx = layer_idx\n        self.head_dim = getattr(config, \"head_dim\", config.hidden_size // config.num_attention_heads)\n        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads\n        self.scaling = self.head_dim**-0.5\n        self.attention_dropout = config.attention_dropout\n        self.is_causal = True\n\n        self.q_proj = nn.Linear(\n            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias\n        )\n        self.k_proj = nn.Linear(\n            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias\n        )\n        self.v_proj = nn.Linear(\n            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias\n        )\n        self.o_proj = nn.Linear(\n            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias\n        )\n\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        position_embeddings: Tuple[torch.Tensor, torch.Tensor],\n        attention_mask: Optional[torch.Tensor],\n        past_key_value: Optional[Cache] = None,\n        cache_position: Optional[torch.LongTensor] = None,\n        **kwargs: Unpack[FlashAttentionKwargs],\n    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n        input_shape = hidden_states.shape[:-1]\n        hidden_shape = (*input_shape, -1, self.head_dim)\n\n        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)\n        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)\n        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)\n\n        cos, sin = position_embeddings\n        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)\n\n        if past_key_value is not None:\n            # sin and cos are specific to RoPE models; cache_position needed for the static cache\n            cache_kwargs = {\"sin\": sin, \"cos\": cos, \"cache_position\": cache_position}\n            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n        attention_interface: Callable = eager_attention_forward\n        if self.config._attn_implementation != \"eager\":\n            if self.config._attn_implementation == \"sdpa\" and kwargs.get(\"output_attentions\", False):\n                logger.warning_once(\n                    \"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to \"\n                    'eager attention. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.'\n                )\n            else:\n                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]\n\n        attn_output, attn_weights = attention_interface(\n            self,\n            query_states,\n            key_states,\n            value_states,\n            attention_mask,\n            dropout=0.0 if not self.training else self.attention_dropout,\n            scaling=self.scaling,\n            **kwargs,\n        )\n\n        attn_output = attn_output.reshape(*input_shape, -1).contiguous()\n        attn_output = self.o_proj(attn_output)\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:10
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyDecoderLayer(nn.Module):\n    def __init__(self, config: DummyConfig, layer_idx: int):\n        super().__init__()\n        self.hidden_size = config.hidden_size\n\n        self.self_attn = DummyAttention(config=config, layer_idx=layer_idx)\n\n        self.mlp = DummyMLP(config)\n        self.input_layernorm = DummyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n        self.post_attention_layernorm = DummyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        attention_mask: Optional[torch.Tensor] = None,\n        position_ids: Optional[torch.LongTensor] = None,\n        past_key_value: Optional[Cache] = None,\n        output_attentions: Optional[bool] = False,\n        use_cache: Optional[bool] = False,\n        cache_position: Optional[torch.LongTensor] = None,\n        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC\n        **kwargs: Unpack[FlashAttentionKwargs],\n    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:\n        residual = hidden_states\n\n        hidden_states = self.input_layernorm(hidden_states)\n\n        # Self Attention\n        hidden_states, self_attn_weights = self.self_attn(\n            hidden_states=hidden_states,\n            attention_mask=attention_mask,\n            position_ids=position_ids,\n            past_key_value=past_key_value,\n            output_attentions=output_attentions,\n            use_cache=use_cache,\n            cache_position=cache_position,\n            position_embeddings=position_embeddings,\n            **kwargs,\n        )\n        hidden_states = residual + hidden_states\n\n        # Fully Connected\n        residual = hidden_states\n        hidden_states = self.post_attention_layernorm(hidden_states)\n        hidden_states = self.mlp(hidden_states)\n        hidden_states = residual + hidden_states\n\n        outputs = (hidden_states,)\n        if output_attentions:\n            outputs += (self_attn_weights,)\n\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:10
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass ImgprocModelImageProcessor(BlipImageProcessor):\n    def new_image_processing_method(self, pixel_values: torch.FloatTensor):\n\n\nSUMMARY:\n**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: 2025-02-07 11:34:19
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass MyNewModelConfig(PretrainedConfig):\n    r\"\"\"\n    This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel\n    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the MyNewModel-7B.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n\n    Args:\n        vocab_size (`int`, *optional*, defaults to 32000):\n            Vocabulary size of the MyNewModel model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`MyNewModelModel`]\n        hidden_size (`int`, *optional*, defaults to 4096):\n            Dimension of the hidden representations.\n        intermediate_size (`int`, *optional*, defaults to 11008):\n            Dimension of the MLP representations.\n        num_hidden_layers (`int`, *optional*, defaults to 32):\n            Number of hidden layers in the Transformer decoder.\n        num_attention_heads (`int`, *optional*, defaults to 32):\n            Number of attention heads for each attention layer in the Transformer decoder.\n        num_key_value_heads (`int`, *optional*):\n            This is the number of key_value heads that should be used to implement Grouped Query Attention. If\n            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if\n            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When\n            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed\n            by meanpooling all the original heads within that group. For more details checkout [this\n            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to\n            `num_attention_heads`.\n        hidden_act (`str` or `function`, *optional*, defaults to `\"silu\"`):\n            The non-linear activation function (function or string) in the decoder.\n        max_position_embeddings (`int`, *optional*, defaults to 2048):\n            The maximum sequence length that this model might ever be used with. MyNewModel 1 supports up to 2048 tokens,\n            MyNewModel 2 up to 4096, CodeLlama up to 16384.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        rms_norm_eps (`float`, *optional*, defaults to 1e-06):\n            The epsilon used by the rms normalization layers.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models). Only\n            relevant if `config.is_decoder=True`.\n        pad_token_id (`int`, *optional*):\n            Padding token id.\n        bos_token_id (`int`, *optional*, defaults to 1):\n            Beginning of stream token id.\n        eos_token_id (`int`, *optional*, defaults to 2):\n            End of stream token id.\n        pretraining_tp (`int`, *optional*, defaults to 1):\n            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this\n            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to\n            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining\n            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).\n        tie_word_embeddings (`bool`, *optional*, defaults to `False`):\n            Whether to tie weight embeddings\n        rope_theta (`float`, *optional*, defaults to 10000.0):\n            The base period of the RoPE embeddings.\n        rope_scaling (`Dict`, *optional*):\n            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type\n            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value\n            accordingly.\n            Expected contents:\n                `rope_type` (`str`):\n                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',\n                    'my_new_model3'], with 'default' being the original RoPE implementation.\n                `factor` (`float`, *optional*):\n                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In\n                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *\n                    original maximum pre-trained length.\n                `original_max_position_embeddings` (`int`, *optional*):\n                    Used with 'dynamic', 'longrope' and 'my_new_model3'. The original max position embeddings used during\n                    pretraining.\n                `attention_factor` (`float`, *optional*):\n                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention\n                    computation. If unspecified, it defaults to value recommended by the implementation, using the\n                    `factor` field to infer the suggested value.\n                `beta_fast` (`float`, *optional*):\n                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear\n                    ramp function. If unspecified, it defaults to 32.\n                `beta_slow` (`float`, *optional*):\n                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear\n                    ramp function. If unspecified, it defaults to 1.\n                `short_factor` (`List[float]`, *optional*):\n                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<\n                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden\n                    size divided by the number of attention heads divided by 2\n                `long_factor` (`List[float]`, *optional*):\n                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<\n                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden\n                    size divided by the number of attention heads divided by 2\n                `low_freq_factor` (`float`, *optional*):\n                    Only used with 'my_new_model3'. Scaling factor applied to low frequency components of the RoPE\n                `high_freq_factor` (`float`, *optional*):\n                    Only used with 'my_new_model3'. Scaling factor applied to high frequency components of the RoPE\n        attention_bias (`bool`, *optional*, defaults to `False`):\n            Whether to use a bias in the query, key, value and output projection layers during self-attention.\n        attention_dropout (`float`, *optional*, defaults to 0.0):\n            The dropout ratio for the attention probabilities.\n        mlp_bias (`bool`, *optional*, defaults to `False`):\n            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.\n        head_dim (`int`, *optional*):\n            The attention head dimension. If None, it will default to hidden_size // num_attention_heads\n\n    ```python\n    >>> from transformers import MyNewModelModel, MyNewModelConfig\n\n    >>> # Initializing a MyNewModel my_new_model-7b style configuration\n    >>> configuration = MyNewModelConfig()\n\n    >>> # Initializing a model from the my_new_model-7b style configuration\n    >>> model = MyNewModelModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```\n        new_param (`int`, *optional*, defaults to `False`):\n            A fun new parameter\n    \"\"\"\n\n    model_type = \"my_new_model\"\n    keys_to_ignore_at_inference = [\"past_key_values\"]\n    # Default tensor parallel plan for base model `MyNewModelModel`\n    base_model_tp_plan = {\n        \"layers.*.self_attn.q_proj\": \"colwise\",\n        \"layers.*.self_attn.k_proj\": \"colwise\",\n        \"layers.*.self_attn.v_proj\": \"colwise\",\n        \"layers.*.self_attn.o_proj\": \"rowwise\",\n        \"layers.*.mlp.gate_proj\": \"colwise\",\n        \"layers.*.mlp.up_proj\": \"colwise\",\n        \"layers.*.mlp.down_proj\": \"rowwise\",\n    }\n\n    def __init__(\n        self,\n        vocab_size=32000,\n        hidden_size=4096,\n        intermediate_size=11008,\n        num_hidden_layers=32,\n        num_attention_heads=32,\n        num_key_value_heads=None,\n        hidden_act=\"silu\",\n        max_position_embeddings=2048,\n        initializer_range=0.02,\n        rms_norm_eps=1e-6,\n        use_cache=True,\n        pad_token_id=None,\n        bos_token_id=1,\n        eos_token_id=2,\n        pretraining_tp=1\nSUMMARY:\nThis 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 Fac"}


created_at: 2025-02-07 11:34:19
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertEmbeddings(nn.Module):\n    \"\"\"Construct the embeddings from word, position and token_type embeddings.\"\"\"\n\n    def __init__(self, config):\n        super().__init__()\n        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)\n\n        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n        # any TensorFlow checkpoint file\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n        # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        self.register_buffer(\n            \"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False\n        )\n        self.register_buffer(\n            \"token_type_ids\", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False\n        )\n\n    def forward(\n        self,\n        input_ids: Optional[torch.LongTensor] = None,\n        token_type_ids: Optional[torch.LongTensor] = None,\n        position_ids: Optional[torch.LongTensor] = None,\n        inputs_embeds: Optional[torch.FloatTensor] = None,\n        past_key_values_length: int = 0,\n    ) -> torch.Tensor:\n        if input_ids is not None:\n            input_shape = input_ids.size()\n        else:\n            input_shape = inputs_embeds.size()[:-1]\n\n        seq_length = input_shape[1]\n\n        if position_ids is None:\n            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]\n\n        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs\n        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves\n        # issue #5664\n        if token_type_ids is None:\n            if hasattr(self, \"token_type_ids\"):\n                buffered_token_type_ids = self.token_type_ids[:, :seq_length]\n                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)\n                token_type_ids = buffered_token_type_ids_expanded\n            else:\n                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)\n\n        if inputs_embeds is None:\n            inputs_embeds = self.word_embeddings(input_ids)\n        token_type_embeddings = self.token_type_embeddings(token_type_ids)\n\n        embeddings = inputs_embeds + token_type_embeddings\n        if self.position_embedding_type == \"absolute\":\n            position_embeddings = self.position_embeddings(position_ids)\n            embeddings += position_embeddings\n        embeddings = self.LayerNorm(embeddings)\n        embeddings = self.dropout(embeddings)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:22
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertSelfAttention(nn.Module):\n    def __init__(self, config, position_embedding_type=None):\n        super().__init__()\n        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, \"embedding_size\"):\n            raise ValueError(\n                f\"The hidden size ({config.hidden_size}) is not a multiple of the number of attention \"\n                f\"heads ({config.num_attention_heads})\"\n            )\n\n        self.num_attention_heads = config.num_attention_heads\n        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n        self.query = nn.Linear(config.hidden_size, self.all_head_size)\n        self.key = nn.Linear(config.hidden_size, self.all_head_size)\n        self.value = nn.Linear(config.hidden_size, self.all_head_size)\n\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n        self.position_embedding_type = position_embedding_type or getattr(\n            config, \"position_embedding_type\", \"absolute\"\n        )\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            self.max_position_embeddings = config.max_position_embeddings\n            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)\n\n        self.is_decoder = config.is_decoder\n\n    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(new_x_shape)\n        return x.permute(0, 2, 1, 3)\n\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        attention_mask: Optional[torch.FloatTensor] = None,\n        head_mask: Optional[torch.FloatTensor] = None,\n        encoder_hidden_states: Optional[torch.FloatTensor] = None,\n        encoder_attention_mask: Optional[torch.FloatTensor] = None,\n        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n        output_attentions: Optional[bool] = False,\n    ) -> Tuple[torch.Tensor]:\n        mixed_query_layer = self.query(hidden_states)\n\n        # If this is instantiated as a cross-attention module, the keys\n        # and values come from an encoder; the attention mask needs to be\n        # such that the encoder's padding tokens are not attended to.\n        is_cross_attention = encoder_hidden_states is not None\n\n        if is_cross_attention and past_key_value is not None:\n            # reuse k,v, cross_attentions\n            key_layer = past_key_value[0]\n            value_layer = past_key_value[1]\n            attention_mask = encoder_attention_mask\n        elif is_cross_attention:\n            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))\n            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))\n            attention_mask = encoder_attention_mask\n        elif past_key_value is not None:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)\n            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)\n        else:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n\n        use_cache = past_key_value is not None\n        if self.is_decoder:\n            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.\n            # Further calls to cross_attention layer can then reuse all cross-attention\n            # key/value_states (first \"if\" case)\n            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of\n            # all previous decoder key/value_states. Further calls to uni-directional self-attention\n            # can concat previous decoder key/value_states to current projected key/value_states (third \"elif\" case)\n            # if encoder bi-directional self-attention `past_key_value` is always `None`\n            past_key_value = (key_layer, value_layer)\n\n        # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            query_length, key_length = query_layer.shape[2], key_layer.shape[2]\n            if use_cache:\n                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(\n                    -1, 1\n                )\n            else:\n                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)\n            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)\n            distance = position_ids_l - position_ids_r\n\n            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)\n            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility\n\n            if self.position_embedding_type == \"relative_key\":\n                relative_position_scores = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores\n            elif self.position_embedding_type == \"relative_key_query\":\n                relative_position_scores_query = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                relative_position_scores_key = torch.einsum(\"bhrd,lrd->bhlr\", key_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key\n\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        if attention_mask is not None:\n            # Apply the attention mask is (precomputed for all layers in DummyBertModel forward() function)\n            attention_scores = attention_scores + attention_mask\n\n        # Normalize the attention scores to probabilities.\n        attention_probs = nn.functional.softmax(attention_scores, dim=-1)\n\n        # This is actually dropping out entire tokens to attend to, which might\n        # seem a bit unusual, but is taken from the original Transformer paper.\n        attention_probs = self.dropout(attention_probs)\n\n        # Mask heads if we want to\n        if head_mask is not None:\n            attention_probs = attention_probs * head_mask\n\n        context_layer = torch.matmul(attention_probs, value_layer)\n\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(new_context_layer_shape)\n\n        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)\n\n        if self.is_decoder:\n            outputs = outputs + (past_key_value,)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:23
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertSdpaSelfAttention(DummyBertSelfAttention):\n    def __init__(self, config, position_embedding_type=None):\n        super().__init__(config, position_embedding_type=position_embedding_type)\n        self.dropout_prob = config.attention_probs_dropout_prob\n        self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse(\"2.2.0\")\n\n    # Adapted from DummyBertSelfAttention\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        attention_mask: Optional[torch.Tensor] = None,\n        head_mask: Optional[torch.FloatTensor] = None,\n        encoder_hidden_states: Optional[torch.FloatTensor] = None,\n        encoder_attention_mask: Optional[torch.FloatTensor] = None,\n        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n        output_attentions: Optional[bool] = False,\n    ) -> Tuple[torch.Tensor]:\n        if self.position_embedding_type != \"absolute\" or output_attentions or head_mask is not None:\n            # TODO: Improve this warning with e.g. `model.config._attn_implementation = \"manual\"` once implemented.\n            logger.warning_once(\n                \"DummyBertSdpaSelfAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support \"\n                \"non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to \"\n                \"the manual attention implementation, but specifying the manual implementation will be required from \"\n                \"Transformers version v5.0.0 onwards. This warning can be removed using the argument \"\n                '`attn_implementation=\"eager\"` when loading the model.'\n            )\n            return super().forward(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                past_key_value,\n                output_attentions,\n            )\n\n        bsz, tgt_len, _ = hidden_states.size()\n\n        query_layer = self.transpose_for_scores(self.query(hidden_states))\n\n        # If this is instantiated as a cross-attention module, the keys and values come from an encoder; the attention\n        # mask needs to be such that the encoder's padding tokens are not attended to.\n        is_cross_attention = encoder_hidden_states is not None\n\n        current_states = encoder_hidden_states if is_cross_attention else hidden_states\n        attention_mask = encoder_attention_mask if is_cross_attention else attention_mask\n\n        # Check `seq_length` of `past_key_value` == `len(current_states)` to support prefix tuning\n        if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:\n            key_layer, value_layer = past_key_value\n        else:\n            key_layer = self.transpose_for_scores(self.key(current_states))\n            value_layer = self.transpose_for_scores(self.value(current_states))\n            if past_key_value is not None and not is_cross_attention:\n                key_layer = torch.cat([past_key_value[0], key_layer], dim=2)\n                value_layer = torch.cat([past_key_value[1], value_layer], dim=2)\n\n        if self.is_decoder:\n            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.\n            # Further calls to cross_attention layer can then reuse all cross-attention\n            # key/value_states (first \"if\" case)\n            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of\n            # all previous decoder key/value_states. Further calls to uni-directional self-attention\n            # can concat previous decoder key/value_states to current projected key/value_states (third \"elif\" case)\n            # if encoder bi-directional self-attention `past_key_value` is always `None`\n            past_key_value = (key_layer, value_layer)\n\n        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom\n        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.\n        # Reference: https://github.com/pytorch/pytorch/issues/112577\n        if self.require_contiguous_qkv and query_layer.device.type == \"cuda\" and attention_mask is not None:\n            query_layer = query_layer.contiguous()\n            key_layer = key_layer.contiguous()\n            value_layer = value_layer.contiguous()\n\n        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment\n        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.\n        # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create\n        # a causal mask in case tgt_len == 1.\n        is_causal = (\n            True if self.is_decoder and not is_cross_attention and attention_mask is None and tgt_len > 1 else False\n        )\n\n        attn_output = torch.nn.functional.scaled_dot_product_attention(\n            query_layer,\n            key_layer,\n            value_layer,\n            attn_mask=attention_mask,\n            dropout_p=self.dropout_prob if self.training else 0.0,\n            is_causal=is_causal,\n        )\n\n        attn_output = attn_output.transpose(1, 2)\n        attn_output = attn_output.reshape(bsz, tgt_len, self.all_head_size)\n\n        outputs = (attn_output,)\n        if self.is_decoder:\n            outputs = outputs + (past_key_value,)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:23
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertSelfOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:25
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertAttention(nn.Module):\n    def __init__(self, config, position_embedding_type=None):\n        super().__init__()\n        self.self = DUMMY_BERT_SELF_ATTENTION_CLASSES[config._attn_implementation](\n            config, position_embedding_type=position_embedding_type\n        )\n        self.output = DummyBertSelfOutput(config)\n        self.pruned_heads = set()\n\n    def prune_heads(self, heads):\n        if len(heads) == 0:\n            return\n        heads, index = find_pruneable_heads_and_indices(\n            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads\n        )\n\n        # Prune linear layers\n        self.self.query = prune_linear_layer(self.self.query, index)\n        self.self.key = prune_linear_layer(self.self.key, index)\n        self.self.value = prune_linear_layer(self.self.value, index)\n        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)\n\n        # Update hyper params and store pruned heads\n        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)\n        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads\n        self.pruned_heads = self.pruned_heads.union(heads)\n\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        attention_mask: Optional[torch.FloatTensor] = None,\n        head_mask: Optional[torch.FloatTensor] = None,\n        encoder_hidden_states: Optional[torch.FloatTensor] = None,\n        encoder_attention_mask: Optional[torch.FloatTensor] = None,\n        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n        output_attentions: Optional[bool] = False,\n    ) -> Tuple[torch.Tensor]:\n        self_outputs = self.self(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            encoder_hidden_states,\n            encoder_attention_mask,\n            past_key_value,\n            output_attentions,\n        )\n        attention_output = self.output(self_outputs[0], hidden_states)\n        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:25
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertIntermediate(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n        if isinstance(config.hidden_act, str):\n            self.intermediate_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.intermediate_act_fn = config.hidden_act\n\n    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.intermediate_act_fn(hidden_states)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:25
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n\n\nSUMMARY:\n**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: 2025-02-07 11:35:26
topic: persistent://default/default/1aba5826-fc8e-473f-b9b4-e86dd3a2404a
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": "CODE:\n\nclass DummyBertLayer(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.chunk_size_feed_forward = config.chunk_size_feed_forward\n        self.seq_len_dim = 1\n        self.attention = DummyBertAttention(config)\n        self.is_decoder = config.is_decoder\n        self.add_cross_attention = config.add_cross_attention\n        if self.add_cross_attention:\n            if not self.is_decoder:\n                raise ValueError(f\"{self} should be used as a decoder model if cross attention is added\")\n            self.crossattention = DummyBertAttention(config, position_embedding_type=\"absolute\")\n        self.intermediate = DummyBertIntermediate(config)\n        self.output = DummyBertOutput(config)\n\n    def forward(\n        self,\n        hidden_states: torch.Tensor,\n        attention_mask: Optional[torch.FloatTensor] = None,\n        head_mask: Optional[torch.FloatTensor] = None,\n        encoder_hidden_states: Optional[torch.FloatTensor] = None,\n        encoder_attention_mask: Optional[torch.FloatTensor] = None,\n        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n        output_attentions: Optional[bool] = False,\n    ) -> Tuple[torch.Tensor]:\n        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2\n        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None\n        self_attention_outputs = self.attention(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            output_attentions=output_attentions,\n            past_key_value=self_attn_past_key_value,\n        )\n        attention_output = self_attention_outputs[0]\n\n        # if decoder, the last output is tuple of self-attn cache\n        if self.is_decoder:\n            outputs = self_attention_outputs[1:-1]\n            present_key_value = self_attention_outputs[-1]\n        else:\n            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights\n\n        cross_attn_present_key_value = None\n        if self.is_decoder and encoder_hidden_states is not None:\n            if not hasattr(self, \"crossattention\"):\n                raise ValueError(\n                    f\"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers\"\n                    \" by setting `config.add_cross_attention=True`\"\n                )\n\n            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple\n            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None\n            cross_attention_outputs = self.crossattention(\n                attention_output,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                cross_attn_past_key_value,\n                output_attentions,\n            )\n            attention_output = cross_attention_outputs[0]\n            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights\n\n            # add cross-attn cache to positions 3,4 of present_key_value tuple\n            cross_attn_present_key_value = cross_attention_outputs[-1]\n            present_key_value = present_key_value + cross_attn_present_key_value\n\n        layer_output = apply_chunking_to_forward(\n            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output\n        )\n        outputs = (layer_output,) + outputs\n\n        # if decoder, return the attn key/values as the last output\n        if self.is_decoder:\n            outputs = outputs + (present_key_value,)\n\n        return outputs\n\n    def feed_forward_chunk(self, attention_output):\n        intermediate_output = self.intermediate(attention_output)\n        layer_output = self.output(intermediate_output, attention_output)\n\n\nSUMMARY:\n**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)."}


# collections

id: 1aba5826-fc8e-473f-b9b4-e86dd3a2404a
name: code_chunks_batch_voyage
database_id: 00000000-0000-0000-0000-000000000000
config_json_str:
{"hnsw_configuration": {"space": "l2", "ef_construction": 100, "ef_search": 100, "num_threads": 24, "M": 16, "resize_factor": 1.2, "batch_size": 100, "sync_threshold": 1000, "_type": "HNSWConfigurationInternal"}, "_type": "CollectionConfigurationInternal"}


# embeddings

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created_at: 2025-02-07 11:10:50
embedding_id:
/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py:7-8


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_tensorflow_text_objects.py:4-8


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:4-8


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:11-15


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:18-22


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:25-29


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:32-36


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:39-43


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:46-50


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/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_sentencepiece_objects.py:81-85


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