# embedding_metadata

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CODE:

class EncoderDecoderModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary**

The `EncoderDecoderModel` class is a metadata model for an encoder-decoder architecture, utilizing the "torch" backend. It initializes with no specific requirements.

**Key Details**

* Primary purpose: Metdata model for encoder-decoder architecture
* Use case: Utilizes "torch" backend
* Key implementation: Requires "torch" as a backend

**Notable Dependencies/Requirements**

* `requires_backends` function for specifying required backends
* "torch" backend dependency


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class ErnieForCausalLM(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary: ErnieForCausalLM**

*   **Purpose:** A PyTorch-based implementation of the Ernie model for causal language modeling (CLM).
*   **Functionality:** The class inherits from `DummyObject` and provides an initializer that depends on the "torch" backend.

**Key Implementation Details:**

*   Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`).
*   Verifies that the `"torch"` backend is required using `requires_backends`.

**Notable Dependencies or Requirements:**

*   Requires the `"torch"` PyTorch backend.
*   Inherited from `DummyObject`.


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class ErnieForMaskedLM(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary**

The `ErnieForMaskedLM` class is a PyTorch-based model implemented using the Metaclass `DummyObject`. It is designed for Masked Language Modeling tasks. 

**Key Implementation Details**

- Initializes with a require check to ensure the 'torch' backend is provided.
- Utilizes the `requires_backends` function, which enforces the specified backend (`['torch']`) when instantiating the class.

**Notable Dependencies/Requirements**

- Reqs the 'torch' PyTorch backend.


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class ErnieForMultipleChoice(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary:**

`ErnieForMultipleChoice`: A PyTorch-based model for multiple choice classification.

**Key Implementation:**
- Initializes with `*args` and `**kwargs`, requiring the `"torch"` backend via `requires_backends`.
- Uses the PyTorch framework (`torch`) backend.

**Dependencies/Requirements:**

* PyTorch library
* DummyObject metaclass (not explicitly listed, but assumed)
* `requires_backends` function (not shown in this code snippet)


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class ErnieForNextSentencePrediction(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary:**

`ErnieForNextSentencePrediction` is a PyTorch-based model for next sentence prediction. It utilizes the `ErnieTokenizer` and relies on the `torch` backend.

**Key Details:**

* Initializes with `requires_backends` function, enforcing `torch` as a required backend.
* Tokenizer dependency implicitly references `ErnieTokenizer`.
* No specific model architecture details are provided.

Note that this summary focuses on the critical aspects of the class, omitting unnecessary implementation details and generic descriptions.


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class ErnieForPreTraining(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary: ErnieForPreTraining**

*   **Main Purpose:** A pre-trained language model class `ErnieForPreTraining` that leverages the `torch` backend.

**Key Implementation Details:**

*   Initializes with input arguments `args` and `kwargs`.
*   Verifies that the required `torch` backend is installed using the `requires_backends` function.

**Notable Dependencies or Requirements:**

*   Requires `torch`, a popular deep learning library.
*   Utilizes the `DummyObject` metaclass due to its undefined implications on class initialization behavior.


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CODE:

class ErnieForQuestionAnswering(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary: ErnieForQuestionAnswering**

* **Main Purpose and Functionality:** ErnieForQuestionAnswering is a pre-trained model for question answering tasks.
* **Key Implementation Details:**
	+ Initializes with `torch` backend using the `requires_backends` method.
	+ Uses `DummyObject` metaclass.
* **Notable Dependencies or Requirements:**
	+ Relies on `torch` backend for implementation.


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CODE:

class ErnieForSequenceClassification(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Concise Summary**

`ErnieForSequenceClassification` Class:
------------------------

### Main Purpose and Functionality

`ErnieForSequenceClassification` is a PyTorch-based sequence classification model that leverages the Ernie Large Language Model (`DummyObject`) as its backbone. The primary purpose is to classify input sequences.

### Key Implementation Details

*   Requires the `torch` backend for execution.
*   Initializes with variable-length arguments and keyword-only arguments.

**Notable Dependencies and Requirements**

*   `[ErnieClassifier]`: Serves as a classification model interface, implying the need for a related implementation or library.


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class ErnieForTokenClassification(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary:**

`ErnieForTokenClassification` is a PyTorch-based model for token classification. Its primary purpose is to classify tokens in a sequence of text using the Ernie tokenizer backend.

**Key Implementation Details:**

* Models are initialized with the "torch" backend, which enables automatic backend switching.
* The `requires_backends` function ensures that the required "torch" backend is available before model initialization.

**Notable Dependencies/Requirements:**

* PyTorch
* DummyObject (metaclass)
* ErnieTokenizer (presumably used by this class)


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CODE:

class ErnieModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary**

The `ErnieModel` class is a PyTorch-based model class with the following characteristics:

*   Primary purpose: Implements a machine learning model utilizing the PyTorch backend.
*   Key implementation details:
    *   Verifies if the required backend (`torch`) is present using the `requires_backends` function.
    
        **Code Snippet**
        
```python
class ErnieModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
```

**Additional Notes**

*   Notable dependencies: This code depends on the `PyTorch` library and the `DummyObject` metaclass.
*   Inherited/Implemented interfaces: None explicitly mentioned in this snippet. However, it's likely that the class implements the `torch._C._Module` interface.


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class ErniePreTrainedModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Ernie Pre-Trained Model Class Summary**

**Main Purpose and Functionality:**
The `ErniePreTrainedModel` class is a pre-trained model implemented using the `DummyObject` metaclass. It serves as a base class for Ernie models.

**Key Implementation Details:**

* The model requires the `torch` backend, ensuring compatibility.
* The `__init__` method initializes the model instance with variable arguments (`args`) and keyword arguments (`kwargs`).

**Notable Dependencies or Requirements:**

* External dependency: `torch`

Note that there are no external interfaces implemented.


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class EsmFoldPreTrainedModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary**

Ermia Fold Pre-trained Model (ESM): A language model built using EsmFold algorithm.

**Key Details:**

* Initializes with support for only `torch` backend (`requires_backends`).
* Primary purpose is to leverage pre-trained weights obtained from the `EsmFold` algorithm.

**Implementation:**
```python
class EsmFoldPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
```
**Dependencies:**

* External dependency on `_DummyObject` for metaclass definition.
* Requires `requires_backends` framework function for backend validation.


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CODE:

class EsmForMaskedLM(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary**

`EsmForMaskedLM` is a PyTorch-based masked language model.

**Key Implementations**

- Applies T5-style Masked Language Modeling algorithm.
- Uses `requires_backends` method to ensure compatible backend (torch) installed.

**Dependencies/Requirements**

- Requires backend installation (`torch`).


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class EsmForProteinFolding(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Summary:** 

`EsmForProteinFolding` class: A PyTorch-based implementation of the ESM (Evolutionary Information from Structure and Motion) model for protein folding prediction.

**Key Functionality:**

* Initializes with supported backends (`torch`) and requires compatibility check
* Uses `provide` method to ensure required backend (`torch`) is available

**Notable Implementation Details:**

* `__init__` method initializes the class with optional arguments
* Requires compatible backend check using `requires_backends` function

**Dependencies:**

* PyTorch library (`torch`)
* `DummyObject` metaclass


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CODE:

class EsmForSequenceClassification(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary:**

`EsmForSequenceClassification`

*   **Purpose:** Implements a PyTorch-based sequence classification model using the ESM embeddings.

**Implementation Details:**

*   Initializes the model using a custom `__init__` method, ensuring that `torch` is supported as a required backend.
*   Calls the `requires_backends` function to enforce this requirement.

**Dependencies and Requirements:**

*   Supported backends: `torch`
*   Inherited interface not specified.


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CODE:

class EsmForTokenClassification(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Concise Summary:**

The `EsmForTokenClassification` class is a PyTorch-based implementation of the ESMTokenClassifier model using the DummyObject metaclass. 

It has a single requirement that the backend must be "torch". The constructor initializes the model with any required parameters.

Key Implementation Details:

*   Requires Torchaes to be present and initialized correctly.
*   Uses the DummyObject metaclass, likely for testing or experimental purposes.


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CODE:

class EsmModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary:**

`EsmModel`
================

### Purpose and Functionality

The `EsmModel` class represents an ESM (Efficient Spatial Model) model. It initializes the backend to be used by default, which is set to `"torch"`.

### Key Implementation Details

- Initializes with a `requires_backends` method that checks if the required_backend(s) for each arg (args is of type variable arguments)
    
### Notable Dependencies or Requirements

* Backend: `torch`
 

Note:
- `_backends` attribute can be accessed from outside the class 
-  requires_backends() is not built-in python function


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CODE:

class EsmPreTrainedModel(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary: EsmPreTrainedModel**

*   **Main Purpose:** An implementation of pre-trained models using the ESM (Emergence-based Sequence Modeling) architecture.

*   **Key Implementation Details:**
    *   The model uses a specific backend, requiring `torch` environment.
    *   The class utilizes the `requires_backends` function to enforce backend requirements in the initialization process.

*   **Notable Dependencies and Requirements:**

    *   External dependency on `PyTorch`.
        *   Related class/function name: `torch`
    *   Implemented interface: `DummyObject` (as a metaclass)
    *   Additional dependencies/inherited interfaces not explicitly mentioned.


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CODE:

class FalconForCausalLM(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):


SUMMARY:
**Class Summary: `FalconForCausalLM`**

*   **Purpose:** A custom implementation of FALCON (Flexible Application for Large Language Models) for causal LLM (Large Language Model) training using the Torch backend.

**Key Implementation Details:**

*   Requires support for both positional and keyword arguments (`self, *args, **kwargs`) as input in the initializer method.
*   `requires_backends` function check ensures the FALCON model uses only the required torch backend during initialization.

**Notable Dependencies/Requirements:**

*   Torch backend implementation (`torch`)
*   Inherited interface: Not explicitly listed; however, all dependencies are assumed to be managed by the internal metaclass or parent classes.
*   Parent metaclass: `DummyObject`


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CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data."
    )
    parser.add_argument("--do_predict", action="store_true", help="To do prediction on the question answering model")
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--test_file", type=str, default=None, help="A csv or a json file containing the Prediction data."
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=384,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded if `--pad_to_max_length` is passed."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--doc_stride",
        type=int,
        default=128,
        help="When splitting up a long document into chunks how much stride to take between chunks.",
    )
    parser.add_argument(
        "--n_best_size",
        type=int,
        default=20,
        help="The total number of n-best predictions to generate when looking for an answer.",
    )
    parser.add_argument(
        "--null_score_diff_threshold",
        type=float,
        default=0.0,
        help=(
            "The threshold used to select the null answer: if the best answer has a score that is less than "
            "the score of the null answer minus this threshold, the null answer is selected for this example. "
            "Only useful when `version_2_with_negative=True`."
        ),
    )
    parser.add_argument(
        "--version_2_with_negative",
        action="store_true",
        help="If true, some of the examples do not have an answer.",
    )
    parser.add_argument(
        "--max_answer_length",
        type=int,
        default=30,
        help=(
            "The maximum length of an answer that can be generated. This is needed because the start "
            "and end predictions are not conditioned on one another."
        ),
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--max_predict_samples",
        type=int,
        default=None,
        help="For debugging purposes or quicker training, truncate the number of prediction examples to this",
    )
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="Model type to use if training from scratch.",
        choices=MODEL_TYPES,
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if (
        args.dataset_name is None
        and args.train_file is None
        and args.validation_file is None
        and args.test_file is None
    ):
        raise ValueError("Need either a dataset name or a training/validation/test file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if args.validation_file is not None:
            extension = args.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
        if args.test_file is not None:
            extension = args.test_file.split(".")[-1]
            assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to creat
SUMMARY:
This function defines a script for fine-tuning a transformer model on a Question Answering (QA) task. The script takes in various command-line arguments that configure the training process, such as:

* Data files and locations
* Model type and parameters
* Training and validation settings
* Overwrite cache and push to hub options

The function performs sanity checks to ensure consistency and validity of input arguments. It then parses the argument using `argparse` and returns a `Namespace` object containing all the arguments.

In essence, this script allows users to customize the training process for a transformer-based QA model and provides a flexible way to perform fine-tuning with various configuration optio


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CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_qa_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
    else:
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        if args.test_file is not None:
            data_files["test"] = args.test_file
            extension = args.test_file.split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files, field="data")
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if args.config_name:
        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=True, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=True, trust_remote_code=args.trust_remote_code
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForQuestionAnswering.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForQuestionAnswering.from_config(config, trust_remote_code=args.trust_remote_code)

    # Preprocessing the datasets.
    # Preprocessing is slightly different for training and evaluation.

    column_names = raw_datasets["train"].column_names

    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    if args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the "
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )

    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)

    # Training preprocessing
    def prepare_train_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            padding="max_length" if args.pad_to_max_length else False,
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            if tokenizer.cls_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.cls_token_id)
            elif tokenizer.bos_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.bos_token_id)
            else:
                cls_index = 0

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples.sequence_ids(i)

            # One example can give several spans, this is the index of the example conta
SUMMARY:
This is a PyTorch script for training and evaluating a sequence-to-sequence model using the SQuADv2 dataset. The script covers various aspects of model training, including:

1. Setting up the training environment
2. Defining the model architecture and hyperparameters
3. Training the model on the SQuADv2 dataset
4. Evaluating the model's performance using precision and recall metrics
5. Logging progress and results
6. Saving the trained model to a local directory

The script is well-structured, with clear section headings and concise comments. However, it is quite long and might be improved with additional documentation or annotations.

Some minor issues and suggestions for


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search.py


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_qa_beam_search", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            field="data",
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = XLNetConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
    )
    tokenizer = XLNetTokenizerFast.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
    )
    model = XLNetForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
    )

    # Preprocessing the datasets.
    # Preprocessing is slightly different for training and evaluation.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    else:
        column_names = raw_datasets["test"].column_names
    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # Training preprocessing
    def prepare_train_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")
        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []
        tokenized_examples["is_impossible"] = []
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_ex
SUMMARY:
This is an example Python script that trains a Question Answering (QA) model using the Hugging Face Transformers library. The script uses a pre-trained QA model and fine-tunes it on a specific dataset.

Here's a brief summary of the code:

1. **Data preparation**: The script loads the trained pre-trained QA model, tokenizer, and dataset.
2. **Training setup**: The script sets up the training process by creating an


key: chroma:document
string_value:
CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Function Summary**

```markdown
# Function Name: `_mp_fn(index)`
# Purpose: Main entry point for multiprocessing on TPUs using XLA Spawn.

# Key Details:
# - Calls the `main()` function as the primary execution target under XLA Spawn.
```

Note: This code chunk does not provide sufficient information to determine the purpose of the `main()` call, which makes it difficult to generate a comprehensive summary. If more context is available, I can attempt to add more details.


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py


key: chroma:document
string_value:
CODE:

def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"):
    """
    Save results while prefixing metric names.

    Args:
        results: (:obj:`dict`):
            A dictionary of results.
        output_dir: (:obj:`str`):
            An output directory.
        file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
            An output file name.
        metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
            A metric name prefix.
    """
    # Prefix all keys with metric_key_prefix + '_'
    for key in list(results.keys()):
        if not key.startswith(f"{metric_key_prefix}_"):
            results[f"{metric_key_prefix}_{key}"] = results.pop(key)

    with open(os.path.join(output_dir, file_name), "w") as f:


SUMMARY:
**Summary:**

### save_prefixed_metrics Function

*   Saves prefixed metric results to a JSON file.
*   Modifies input dictionary `results` by prefixing keys with a specified name (`metric_key_prefix`) if necessary.

#### Key Details:

*   **Function Argument:** 
    ```python
def save_prefixed_metrics(results: dict, output_dir: str, file_name: str = "all_results.json", metric_key_prefix: str = "eval")
```
*   **Data Structure Manipulation:** Prefixes dictionary keys with `metric_key_prefix` using `dict.pop()` and f-strings for renaming.
*   **File Handling:** Saves modified dictionary to a JSON file with specified `output_dir` and `file_name`.

#### Dependencies:

*   External dependencies: 
    *   `os`: module used for interacting with the operating system, e.g., joining path components together (`os.path.join`).


key: chroma:document
string_value:
CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data."
    )
    parser.add_argument("--do_predict", action="store_true", help="Eval the question answering model")
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--test_file", type=str, default=None, help="A csv or a json file containing the Prediction data."
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=384,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded if `--pad_to_max_length` is passed."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=True,
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--doc_stride",
        type=int,
        default=128,
        help="When splitting up a long document into chunks how much stride to take between chunks.",
    )
    parser.add_argument(
        "--n_best_size",
        type=int,
        default=20,
        help="The total number of n-best predictions to generate when looking for an answer.",
    )
    parser.add_argument(
        "--null_score_diff_threshold",
        type=float,
        default=0.0,
        help=(
            "The threshold used to select the null answer: if the best answer has a score that is less than "
            "the score of the null answer minus this threshold, the null answer is selected for this example. "
            "Only useful when `version_2_with_negative=True`."
        ),
    )
    parser.add_argument(
        "--version_2_with_negative",
        action="store_true",
        help="If true, some of the examples do not have an answer.",
    )
    parser.add_argument(
        "--max_answer_length",
        type=int,
        default=30,
        help=(
            "The maximum length of an answer that can be generated. This is needed because the start "
            "and end predictions are not conditioned on one another."
        ),
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--max_predict_samples",
        type=int,
        default=None,
        help="For debugging purposes or quicker training, truncate the number of prediction examples to this",
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to load in all available experiment trackers from the environment and use them for logging.",
    )
    args = parser.parse_args()

    # Sanity checks
    if (
        args.dataset_name is None
        and args.train_file is None
        and args.validation_file is None
        and args.test_file is None
    ):
        raise ValueError("Need either a dataset name or a training/validation/test file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if args.validation_file is not None:
            extension = args.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
        if args.test_file is not None:
            extension = args.test_file.split(".")[-1]
            assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."



SUMMARY:
**Function Summary:**

This function, `parse_args()`, creates an ArgumentParser object and populates it with 35 optional arguments for fine-tuning a transformer model on a Question Answering task.

**Purpose and Functionality:**
The primary purpose of this function is to parse command-line arguments for the fine-tuning process. It allows users to specify various parameters such as dataset name, model configuration, training data file, preprocessing configurations, and hyperparameters.

**Key Implementation Details:**

* The function supports both `dataset_name` and `train_file` modes of input for the first set of required arguments.
* Various hyperparameters such as learning rate, batch size, number of epochs, and evaluation metrics are optional.
* Additional parameters like `doc_stride`, `n_best_size`, and `null_score_diff_threshold` control specific aspects of the question answering system.

**Overall:**

The function is designed to provide a flexible way to configure the fine-tuning process for transformer models on Question Answering datasets. It includes many options for customization, allowing users to tailor their experiments to suit their needs.


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CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_qa_beam_search_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
    else:
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        if args.test_file is not None:
            data_files["test"] = args.test_file
            extension = args.test_file.split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files, field="data")
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = XLNetConfig.from_pretrained(args.model_name_or_path)
    tokenizer = XLNetTokenizerFast.from_pretrained(args.model_name_or_path)
    model = XLNetForQuestionAnswering.from_pretrained(
        args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
    )

    # Preprocessing the datasets.
    # Preprocessing is slightly different for training and evaluation.
    column_names = raw_datasets["train"].column_names

    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    if args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the "
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )

    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)

    # Training preprocessing
    def prepare_train_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")
        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []
        tokenized_examples["is_impossible"] = []
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            if tokenizer.cls_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.cls_token_id)
            elif tokenizer.bos_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.bos_token_id)
            else:
                cls_index = 0
            tokenized_examples["cls_index"].append(cls_index)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples["token_type_ids"][i]
            for k, s in enumerate(special_tokens[i]):
                if s:
                    sequence_ids[k] = 3
            context_idx = 1 if pad_on_right else 0

            # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.
            # The cls token gets 1.0 too (for predictions of empty answers).
            tokenized_examples["p_mask"].append(
                [
                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
                    for k, s in enumerate(sequence_ids)
                ]
            )

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            answers = examples[answer_column_name][sample_index]
            # If no answers are given, set the cls_index as answer.
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
                tokenized_examples["is_impossible"].append(1.0)
            else:
                # Start/end character index of the answer in the text.
                start_char = answers["answer_start"
SUMMARY:
Here is a concise summary of the code:

This is an implementation of the DistilBERT model on the Stanford Question Answering Dataset (SQuAD). The code trains the model using Adam optimizer and cross-entropy loss, and evaluates its performance on both t


key: file_path
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/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/question-answering/trainer_seq2seq_qa.py


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CODE:

class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
    def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.eval_examples = eval_examples
        self.post_process_function = post_process_function

    # def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"):
    def evaluate(
        self,
        eval_dataset: Optional[Dataset] = None,
        eval_examples=None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
        **gen_kwargs,
    ) -> Dict[str, float]:
        gen_kwargs = gen_kwargs.copy()

        # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
        # training args
        if gen_kwargs.get("max_length") is None and self.args.generation_max_length is not None:
            gen_kwargs["max_length"] = self.args.generation_max_length
        if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
            gen_kwargs["num_beams"] = self.args.generation_num_beams
        self._gen_kwargs = gen_kwargs

        eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
        eval_dataloader = self.get_eval_dataloader(eval_dataset)
        eval_examples = self.eval_examples if eval_examples is None else eval_examples

        # Temporarily disable metric computation, we will do it in the loop here.
        compute_metrics = self.compute_metrics
        self.compute_metrics = None
        start_time = time.time()
        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
            output = eval_loop(
                eval_dataloader,
                description="Evaluation",
                # No point gathering the predictions if there are no metrics, otherwise we defer to
                # self.args.prediction_loss_only
                prediction_loss_only=True if compute_metrics is None else None,
                ignore_keys=ignore_keys,
                metric_key_prefix=metric_key_prefix,
            )
        finally:
            self.compute_metrics = compute_metrics
        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            eval_preds = self.post_process_function(eval_examples, eval_dataset, output)
            metrics = self.compute_metrics(eval_preds)

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()):
                if not key.startswith(f"{metric_key_prefix}_"):
                    metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

            metrics.update(output.metrics)
        else:
            metrics = output.metrics

        if self.args.should_log:
            # Only the main node log the results by default
            self.log(metrics)

        if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
        return metrics

    def predict(
        self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test", **gen_kwargs
    ):
        self._gen_kwargs = gen_kwargs.copy()

        predict_dataloader = self.get_test_dataloader(predict_dataset)

        # Temporarily disable metric computation, we will do it in the loop here.
        compute_metrics = self.compute_metrics
        self.compute_metrics = None
        start_time = time.time()
        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
            output = eval_loop(
                predict_dataloader,
                description="Prediction",
                # No point gathering the predictions if there are no metrics, otherwise we defer to
                # self.args.prediction_loss_only
                prediction_loss_only=True if compute_metrics is None else None,
                ignore_keys=ignore_keys,
                metric_key_prefix=metric_key_prefix,
            )
        finally:
            self.compute_metrics = compute_metrics

        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )
        if self.post_process_function is None or self.compute_metrics is None:
            return output

        predictions = self.post_process_function(predict_examples, predict_dataset, output, "predict")
        metrics = self.compute_metrics(predictions)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
        metrics.update(output.metrics)


SUMMARY:
**Summary:**

This is an implementation of the `QuestionAnsweringSeq2SeqTrainer` class in Hugging Face Transformers. It inherits from `Seq2SeqTrainer` and provides functionality for training and evaluating question-answering models using sequence-to-sequence learning.

**Main Functionality:**

1. Evaluates a model on a dataset using the `evaluate` method.
2. Generates predictions on a test dataset using the `predict` method.
3. Preprocesses and post-processes input data for evaluation or prediction.
4. Computation of metrics, such as speed, compilation time, and loss.

**Key Implementation Details:**

* `evaluate`: uses `prediction_loop` or `evaluation_loop`, disables metric computation temporarily, and updates output metrics with speed statistics.
* `predict`: uses `prediction_loop` or `evaluation_loop`, generates predictions using `post_process_function`, and computes metrics on the prediction output.

**Notable Dependencies:**

* Hugging Face Transformers library
* PyTorch library

External dependencies:

* None

Interfaces:

* `Seq2SeqTrainer`
* `Dataset`
* `PredictionOutput`

This summary provides a concise overview of the key functionality, implementation details, and dependencies of the provided class code chunk.


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CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_seq2seq_qa", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            field="data",
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    # Preprocessing the datasets.
    # We need to generate and tokenize inputs and targets.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        column_names = raw_datasets["test"].column_names
    else:
        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
        return

    # Get the column names for input/target.
    dataset_columns = question_answering_column_name_mapping.get(data_args.dataset_name, None)
    if data_args.question_column is None:
        question_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
    else:
        question_column = data_args.question_column
        if question_column not in column_names:
            raise ValueError(
                f"--question_column' value '{data_args.question_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.context_column is None:
        context_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
    else:
        context_column = data_args.context_column
        if context_column not in column_names:
            raise ValueError(
                f"--context_column' value '{data_args.context_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.answer_column is None:
        answer_column = dataset_columns[2] if dataset_columns is not None else column_names[2]
    else:
        answer_column = data_args.answer_column
        if answer_column not in column_names:
            raise ValueError(
                f"--answer_column' value '{data_args.answer_column}' needs to be one of: {', '.join(column_names)}"
            )

    # Temporarily set max_answer_length for training.
    max_answer_length = data_args.max_answer_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for "
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_s
SUMMARY:
This code is for training and evaluating a Question Answering (QA) model using the Hugging Face Transformers library. It includes dataset processing, model training, evaluation, prediction, and model pushing to a hub.

Here's a high-level overview:

1. **Dataset Preprocessing**: The code preprocesses the QA data by applying tokenization, label encoding, and padding.
2. **Model Training**: It trains the model using the `QuestionAnsweringSeq2SeqTrainer` class, which combines sequence-to-sequence training with loss-based optimization.
3. **Eval


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CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Summary:**

### Function Analysis

* Name: `_mp_fn`
* Purpose: Executes the `main()` function, likely as part of parallel processing or multiprocessing using TPUs via `xla_spawn`.

* Implementation Details:
	+ Calls the outer-scoped `main()` function directly.

### Notable Dependencies and Requirements:

* `xla_spawn` for TPU execution
* Assumed availability of an outer-scoped `main()` function.


key: file_path
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/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/summarization/run_summarization_no_trainer.py


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CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a summarization task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--ignore_pad_token_for_loss",
        type=bool,
        default=True,
        help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.",
    )
    parser.add_argument(
        "--max_source_length",
        type=int,
        default=1024,
        help=(
            "The maximum total input sequence length after "
            "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded."
        ),
    )
    parser.add_argument(
        "--source_prefix",
        type=str,
        default=None,
        help="A prefix to add before every source text (useful for T5 models).",
    )
    parser.add_argument(
        "--preprocessing_num_workers",
        type=int,
        default=None,
        help="The number of processes to use for the preprocessing.",
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--max_target_length",
        type=int,
        default=128,
        help=(
            "The maximum total sequence length for target text after "
            "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded. "
            "during ``evaluate`` and ``predict``."
        ),
    )
    parser.add_argument(
        "--val_max_target_length",
        type=int,
        default=None,
        help=(
            "The maximum total sequence length for validation "
            "target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
            "padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
            "param of ``model.generate``, which is used during ``evaluate`` and ``predict``."
        ),
    )
    parser.add_argument(
        "--num_beams",
        type=int,
        default=None,
        help=(
            "Number of beams to use for evaluation. This argument will be "
            "passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--text_column",
        type=str,
        default=None,
        help="The name of the column in the datasets containing the full texts (for summarization).",
    )
    parser.add_argument(
        "--summary_column",
        type=str,
        default=None,
        help="The name of the column in the datasets containing the summaries (for summarization).",
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="Model type to use if training from scratch.",
        choices=MODEL_TYPES,
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
        raise ValueError("Need either a dataset name or a training/validation file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if args.validation_file is not None:
            extension = args.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."



SUMMARY:
**Function Summary**

This function parses command-line arguments for the Hugging Face Transformers library. The function takes in various hyperparameters and optimization settings for training a model, including:

* Model architecture parameters
* Training data configuration
* Hyperparameter tunables (e.g., learning rate, weight decay)
* Optimization settings (e.g., scheduler type, accumulation steps)

The function returns a dictionary of parsed arguments.

**Key Parameters**

* `--train_data`: Training dataset file(s) or dataset name
* `--validation_data`: Validation dataset file(s) or dataset name
* `--output_dir`: Directory for saving the trained model
* `--learning_rate`: Initial learning rate
* `--num_train_epochs`: Total number of training epochs
* `--scheduler_type`: Optimization scheduler type (e.g., linear, cosine)
* `--gradient_accumulation_steps`: Accumulation steps for gradient updates

**Return Value**

A dictionary containing the parsed command-line arguments.


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CODE:

def main():
    args = parse_args()
    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_summarization_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
    if args.source_prefix is None and args.model_name_or_path in [
        "google-t5/t5-small",
        "google-t5/t5-base",
        "google-t5/t5-large",
        "google-t5/t5-3b",
        "google-t5/t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
            "`--source_prefix 'summarize: ' `"
        )
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
    else:
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.config_name:
        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForSeq2SeqLM.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForSeq2SeqLM.from_config(config, trust_remote_code=args.trust_remote_code)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))
    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    prefix = args.source_prefix if args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    column_names = raw_datasets["train"].column_names

    # Get the column names for input/target.
    dataset_columns = summarization_name_mapping.get(args.dataset_name, None)
    if args.text_column is None:
        text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
    else:
        text_column = args.text_column
        if text_column not in column_names:
            raise ValueError(
                f"--text_column' value '{args.text_column}' needs to be one of: {', '.join(column_names)}"
            )
    if args.summary_column is None:
        summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
    else:
        summary_column = args.summary_column
        if summary_column not in column_names:
            raise ValueError(
                f"--summary_column' value '{args.summary_column}' needs to be one of: {', '.join(column_names)}"
            )

    if args.val_max_target_length is None:
        args.val_max_target_length = args.max_target_length

    # Temporarily set max_target_length for training.
    max_target_length = args.max_target_length
    padding = "max_length" if args.pad_to_max_length else False

    def preprocess_function(examples):
        inputs = examples[text_column]
        targets = examples[summary_column]
        inputs = [prefix + inp for inp in inputs]
        model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)

        # Tokenize targets with the `text_target` keyword argument
        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)

        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and args.ignore_pad_token_for_loss:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
            ]

        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

    with accelerator.main_process_first():
        train_dataset = raw_datasets["train"].map(
            preprocess_function,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on dataset",
        )

        # Temporarily set max_
SUMMARY:
This code is the main training loop for a large language model. Here's a concise summary:

**Overview**

The code trains a pre-trained language model on a custom dataset using transformers from Hugging Face's library. The training process involves two phases: inference and validation.

**Key Components**

1. **Training Loop**: This section contains the core training logic, including loading data, configuring hyperparameters, and iterating over batches to perform one step of backpropagation through time (PTT) or asynchronous training.
2. **Inference Phase**: After a specified number of epochs, the model switches from training mode to inference mode. In this phase, the model evaluates itself on a validation set based on pre-defined evaluation metrics.
3. **Evaluation and Results**: T


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string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_summarization", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    if data_args.source_prefix is None and model_args.model_name_or_path in [
        "google-t5/t5-small",
        "google-t5/t5-base",
        "google-t5/t5-large",
        "google-t5/t5-3b",
        "google-t5/t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
            "`--source_prefix 'summarize: ' `"
        )

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files this script will use the first column for the full texts and the second column for the
    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
        if isinstance(tokenizer, MBartTokenizer):
            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.lang]
        else:
            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.lang)

    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    if (
        hasattr(model.config, "max_position_embeddings")
        and model.config.max_position_embeddings < data_args.max_source_length
    ):
        if model_args.resize_position_embeddings is None:
            logger.warning(
                "Increasing the model's number of position embedding vectors from"
                f" {model.config.max_position_embeddings} to {data_args.max_source_length}."
            )
            model.resize_position_embeddings(data_args.max_source_length)
        elif model_args.resize_position_embeddings:
            model.resize_position_embeddings(data_args.max_source_length)
        else:
            raise ValueError(
                f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has"
                f" {model.config.max_position_embeddings} position encodings. Consider either reducing"
                f" `--max_source_length` to {model.config.max_position_embeddings} or to automatically resize the"
                " model's position encodings by passing `--resize_position_embeddings`."
            )

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        column_names = raw_datasets["test"].column_names
    else:
        log
SUMMARY:
This code is for training and testing a text summarization model using the Hugging Face library. Here's a high-level overview of what it does:

1. **Data preparation**: Loads pre-processed data from a file ( dataset_path ) and splits it into training, validation, and prediction datasets.
2. **Model configuration**: Configures the model with hyperparameters such as number of beams, max length for predictions, and other settings.
3. **Trainer setup**: Creates a trainer instance to handle training, evaluation, and prediction tasks.
4. **Training loop**: Trains the model using th


key: chroma:document
string_value:
CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Summary**

`_mp_fn` is an internal function that calls the `main` function, likely for use with XLA Spawn (TPUs), but its context and purpose are unclear due to incomplete information. 

**Key Implementation Details:**

* Calls the `main` function without specifying any additional parameters or transformations.

**Notable Dependencies/Requirements:**

* Uses XLA Spawn (TPUs) context manager, hinted by `_mp_fn`.
* Reference to `main` function.
* External dependency on XLA/TF inference framework.


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_mlm_no_trainer.py


key: chroma:document
string_value:
CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--validation_split_percentage",
        default=5,
        help="The percentage of the train set used as validation set in case there's no validation split",
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="Model type to use if training from scratch.",
        choices=MODEL_TYPES,
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=None,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated."
        ),
    )
    parser.add_argument(
        "--line_by_line",
        type=bool,
        default=False,
        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
    )
    parser.add_argument(
        "--preprocessing_num_workers",
        type=int,
        default=None,
        help="The number of processes to use for the preprocessing.",
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    parser.add_argument(
        "--low_cpu_mem_usage",
        action="store_true",
        help=(
            "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
            "If passed, LLM loading time and RAM consumption will be benefited."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
        raise ValueError("Need either a dataset name or a training/validation file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            if extension not in ["csv", "json", "txt"]:
                raise ValueError("`train_file` should be a csv, json or t
SUMMARY:
**Functionality Summary:**

The `parse_args()` function creates an ArgumentParser instance to parse command-line arguments for fine-tuning transformer models. It supports various hyperparameters, dataset configuration options, and model-specific parameters.

**Key Implementation Details:**

*   The parser includes arguments for dataset configuration (`dataset_name`, `.dataset_config_name`), training data files (`train_file`, `validation_file`), hyperparameter settings (e.g., batch size, learning rate), model architecture (model name or path, model type), and additional options for caching, logging, and model deployment.

**Notable Dependencies:**

*   The `argparse.ArgumentParser` class from the Python Standard Library
*   External dependencies:
    *   **_datasets**: for loading dataset files
    *   **transformers**: for transformer models
    *   **torch**: for PyTorch

**Interfaces and Inherited Methods:**

*   none explicitly stated, but inheritance from `argparse.ArgumentParser` implies the use of its methods (e.g., `parse_args`, `print_help`) to construct a parser that handles command-line arguments.

Here is an example code snippet to demonstrate how this function might be used to create and parse command-line arguments for a transformer model fine-tuning scenario:

```python
import argparse

# Create the ArgumentParser instance, as shown in the provided function
parser = argparse.ArgumentParser(description="Fine-tuning transformer models")

# Define some common hyperparameter settings
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--learning_rate", type=float, default=5e-4)

# Define a few example dataset names and their paths
dataset_config_names = ["bert-base-uncased"]
dataset_paths = {
    "bert-base-uncased": "/data/bert-base-uncased",
}

def create_argparse_args():
    # Parse command-line arguments, including the dataset configuration option, training data files, etc.
    return parser.parse_args()

# Create argument parses with specific configs
argparse_obj = argparse.ArgumentParser()
argparse_obj.add_argument("-dc", "--dataset-config-name", type=str,
                         default=dataset_config_names[0],
                         choices=[name for name in dataset_config_names])
argparse_obj.add_argument("-tr_file", "--train-file", type=str, required=False)
argparse_obj.add_argument("-va_file", "--validation-file", type=str, required=False)
... # Other args

# Parse and return
args = create_argparse_args()

```

Note that you may choose your own options, as the provided `create_argparse_args()` method is just a demonstration of how similar such functions could be constructed using a


key: chroma:document
string_value:
CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_mlm_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[:{args.validation_split_percentage}%]",
                trust_remote_code=args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[{args.validation_split_percentage}%:]",
                trust_remote_code=args.trust_remote_code,
            )
    else:
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
        raw_datasets = load_dataset(extension, data_files=data_files)
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{args.validation_split_percentage}%]",
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{args.validation_split_percentage}%:]",
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.config_name:
        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForMaskedLM.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            low_cpu_mem_usage=args.low_cpu_mem_usage,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=args.trust_remote_code)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    column_names = raw_datasets["train"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]

    if args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 1024:
            logger.warning(
                "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
                " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
                " override this default with `--block_size xxx`."
            )
            max_seq_length = 1024
    else:
        if args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the "
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)

    if args.line_by_line:
        # When using line_by_line, we just tokenize each nonempty line.
        padding = "max_length" if args.pad_to_max_length else False

        def tokenize_function(examples):
            # Remove empty lines
            examples[text_column_name] = [
                line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
            ]
            return tokenizer(
                examples[text_column_name],
                padding=padding,
                truncation=True,
                max_length=max_seq_length,
                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
                # receives the `special_tokens_mask`.
                return_special_tokens_mask=True,
            )

        with accelerator.main_process_first():
            tokenized_datasets = raw_datasets.map(
                tokenize_function,
                batched=True,
                num_proc=args.preprocessing
SUMMARY:
Here is a concise summary of the code:

** overview**

This is a training script for a language model using the Hugging Face Transformers library. It trains the model on a dataset and evaluates its performance.

**main components**

1. **Data loading**: The script loads the dataset and shuffles it.
2. **Model initialization**: The script initializes the model with the specified configuration and architecture.
3. **Training loop**: The script runs through a series of epochs, where:
	* It trains the model on the shuffled data for a specified number of steps.
	* It evaluates the model's performance using evaluation m


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_fim_no_trainer.py


key: chroma:document
string_value:
CODE:

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

Here's a summary:

*   **Usage**: This script can be run from the command line, passing in various arguments that control its behavior.
*   **Arguments**:
    *   Training and validation file paths (csv, json, or txt format)
    *   Hyperparameters for training, such as learning rate, batch size, etc.
    *   Model configuration options
    *   Integration with the Model Hub
    *   Experiment tracking options

**Example Argument Parsers:**

```python
# Example argument parsers
if __name__ == "__main__":
    parser = TrainerArguments(
        resume_model=None,
        output_dir=None,
        push_to_hub=False,
        hub_model_id=None,
        hub_token=None,
        save_steps=700,
        save_total_limit=None,
        save_on_each_device=True,
        num_train_epochs=3.0,
        per_device train_batch_size=16,
        per_device_val_batch_size=64,
        warmup_steps=500,
        weight_decay=0.01,  # Add this argument
    )
```

**Key Features:**

*   **Training**: Automatic model training and tuning.
*   **Model Saving**: Model saving op


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CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_fim_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)
        # Set a numpy random state for FIM transformations
        np_rng = np.random.RandomState(seed=args.seed)
    else:
        # Still set a random state for FIM transformations
        np_rng = np.random.RandomState(seed=42)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
            # Clone repo locally
            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[:{args.validation_split_percentage}%]",
                trust_remote_code=args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[{args.validation_split_percentage}%:]",
                trust_remote_code=args.trust_remote_code,
            )
    else:
        data_files = {}
        dataset_args = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
        extension = args.train_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
            dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
        raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{args.validation_split_percentage}%]",
                **dataset_args,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{args.validation_split_percentage}%:]",
                **dataset_args,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.config_name:
        config = AutoConfig.from_pretrained(
            args.config_name,
            trust_remote_code=args.trust_remote_code,
        )
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            args.model_name_or_path,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForCausalLM.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            low_cpu_mem_usage=args.low_cpu_mem_usage,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)

    # Add the new FIM tokens to the tokenizer and resize model's vocab embeddings
    special_tokens = [args.fim_prefix_token, args.fim_middle_token, args.fim_suffix_token]
    if args.truncate_or_pad:
        special_tokens.append(args.fim_pad_token)

    # Get the factor by which the embedding layer should be padded based on the device
    pad_factor = 1
    if torch.cuda.is_availble():
        pad_factor = 8

    elif is_torch_tpu_available():
        pad_factor = 128

    # Add the new tokens to the tokenizer
    tokenizer.add_tokens(special_tokens)
    original_embeddings = model.get_input_embeddings()

    if is_deepspeed_zero3_enabled():
        import deepspeed

        with deepspeed.zero.GatheredParameters(original_embeddings.weight, modifier_rank=0):
            # Get the pre-expansion embeddings of the model and resize the embedding layer
            model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
            embeddings = model.get_input_embeddings()

            # Sample the embeddings for the new tokens from a multivariate normal distribution
            # We do this so that the new embeddings are close to the original embeddings and not necessarily zero
            # More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
            mean = original_embeddings.mean(dim=0)
            n = original_embeddings.size()[0]
            sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n
            dist = torch.distributions.multivariate_normal.MultivariateNormal(
                mean,
                covariance_matrix=1e-5 * sigma,
            )
            new_token_embeddings = torch.stack(
                tuple((dist.sample() for _ in range(len(special_tokens)))),
                dim=0,
            )
    else:
        original_embeddings = model.get_input_embeddings()
        # Get the pre-expansion embeddings of the model and resize the embedding layer
        model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
        embeddings = model.get_input_embeddings()

        # Sample the embeddings for the new tokens from a multivariate normal distribution
        # We do this so that the new embeddings are close to the original embeddings and not necessarily
SUMMARY:
Here is a concise summary:

This is the code for a PyTorch-based machine translation model training script. The script trains the model on an evaluation dataset, keeps track of losses and perplexities at each epoch, and saves the final model to a specified output directory when training completes. The 


key: chroma:document
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CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_plm", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                trust_remote_code=data_args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                trust_remote_code=data_args.trust_remote_code,
            )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
        raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = XLNetConfig()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "token": model_args.token,
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        model = XLNetLMHeadModel.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
        )
    else:
        logger.info("Training new model from scratch")
        model = XLNetLMHeadModel(config)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddi
SUMMARY:
This is a PyTorch code snippet that defines a script for training and evaluating a pre-trained model, likely on the Hugging Face Transformers library. The script:

1. Initializes a dataset from a specified data source (e.g., text file) and collates it into batches of permutated language inputs.
2. Setups a `Trainer` object with the trained model, training arguments, and evaluation datasets.
3. Trains the model using the trainer, saving checkpoints as needed.
4. Evaluates the model on the validation dataset, logging metrics.

Some high-level points:

* The script is designed to work with Hugging Face's Transformers library, which provides a simple framework for text processing and machine learning tasks.
* The `Trainer` object is used to manage training and evaluation of the model, including saving checkpoints and metrics.
* The script uses PyTorch's built-in tensors and data structures to process and store input data.
* The final script saves output models, logs train and eval metrics, and optionally pushes the trained model to Hugging Face's Hub.

Example use case:

To use this code as a startin


key: chroma:document
string_value:
CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Summary:**

`_mp_fn(index)` appears to be a function wrapper for distributing computation across multiple computing resources, likely relevant for TPUs (Tensor Processing Units). The main purpose is to execute the `main()` function in an multi-processing context. 

**Key Details:**

- Uses `_mp_fn(index)`, suggesting potential multi-threading or process management.
- Calls `main()`, indicating that this function serves as a wrapper around the primary computation logic.

**Notable Dependencies/Requirements:**

- Relies on XLA Spawn (TPUs), a technology introduced by Google to simplify machine learning inference on TPUs.


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_mlm", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
    # behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            streaming=data_args.streaming,
            trust_remote_code=model_args.trust_remote_code,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )

        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        torch_dtype = (
            model_args.torch_dtype
            if model_args.torch_dtype in ["auto", None]
            else getattr(torch, model_args.torch_dtype)
        )
        model = AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
            torch_dtype=torch_dtype,
            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=model_args.trust_remote_code)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if 
SUMMARY:
This is a code snippet for fine-tuning a pre-trained language model (such as BERT) on the fill-mask task using Hugging Face's Trainer API. The goal is to create a custom trained model that can be used to solve the fill-mask problem from a given dataset.

Here are the key points:

1. Loading and preparing datasets
2. Creating and configuring the model and args
3. Defining data collators, preprocessors, and metrics
4. In


key: chroma:document
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CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Summary:**

The `_mp_fn` function is part of a multi-processing framework and serves as an entry point for executing tasks in parallel. It calls the `main` function when operating on Google TPU's (`xla_spawn`). The precise purpose is:

*   **Primary Functionality:** Execute task in parallel using multiple processes (TPUs).
*   **Key Implementation Detail:** Calls `main()` as a fallback for xla_spawn environment.
*   **Notable Dependencies/Requirements:**
    *   `xla_spawn` (Google TPU)
   


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_clm", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            streaming=data_args.streaming,
            trust_remote_code=model_args.trust_remote_code,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
    else:
        data_files = {}
        dataset_args = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = (
            data_args.train_file.split(".")[-1]
            if data_args.train_file is not None
            else data_args.validation_file.split(".")[-1]
        )
        if extension == "txt":
            extension = "text"
            dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            **dataset_args,
        )
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                **dataset_args,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                **dataset_args,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        torch_dtype = (
            model_args.torch_dtype
            if model_args.torch_dtype in ["auto", None]
            else getattr(torch, model_args.torch_dtype)
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
            torch_dtype=torch_dtype,
            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
        )
    else:
        model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
        n_params = sum({p.data_ptr(): p
SUMMARY:
This code is used to train and evaluate a transformer-based language generation model using the Hugging Face Python library. Here's a high-level overview of what it does:

1. Loads pre-trained tokenizer and model from specific paths or configurations.
2. Downloads and preprocesses text data for training and evaluation (e.g., dataset names, file paths).
3. Initializes a `Trainer` object with the trained model, hyperparameters, and necessary datasets (training, evaluation) if specified.
4. Tra


key: chroma:document
string_value:
CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Function Summary: `_mp_fn`**

* Primary purpose: The `_mp_fn` function appears to be part of an implementation supporting Google Cloud Tensor Processing Units (TPUs) execution. It likely serves as the main entry point for spawning multi-threading or parallel processing on TPUs.
* Key implementation details:
	+ Calls the `main()` function, which is not defined in this code snippet.
	+ Notable dependency: Uses `xla_spawn` library, specifically designed for TPU execution.
* Notable dependencies and requirements:
	+ Inherently uses external libraries (`xla_spawn`) related to TPU execution.


key: chroma:document
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CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_fim", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Set a numpy random state for FIM transformations
    np_rng = np.random.RandomState(seed=training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            streaming=data_args.streaming,
            trust_remote_code=model_args.trust_remote_code,
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                streaming=data_args.streaming,
                trust_remote_code=model_args.trust_remote_code,
            )
    else:
        data_files = {}
        dataset_args = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = (
            data_args.train_file.split(".")[-1]
            if data_args.train_file is not None
            else data_args.validation_file.split(".")[-1]
        )
        if extension == "txt":
            extension = "text"
            dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            **dataset_args,
        )
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                **dataset_args,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                **dataset_args,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    tokenizer_kwargs = {
        "cache_dir": model_args.cache_dir,
        "use_fast": model_args.use_fast_tokenizer,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **token
SUMMARY:
This is the code for a Hugging Face Transformers training script. It's used to train and evaluate a text generation model on a dataset of your choice.

**Key Features:**

*   Supports both fine-tuning and training from scratch.
*   Handles streaming datasets with a progress bar and metric logging.
*   Trains models on different devices (GPU/TPU) if available and optimizes for performance, memory, or mixed-mode training.
*   Automatically saves checkpoints and logs metrics to help track the training progress.

**Preprocessing Steps:**

1.  **Load Data**: The script loads a dataset using `tokenized_datasets.load()`.
2.  **Perform Fine-tuning or Training from Scratch**: Before starting the actual training process, it fine-tunes or trains your model using `Trainer.train()` if you already have data. Otherwise, it starts with empty datasets and begins preprocessing your data.
3.  **Fine-tune or Train Model**: Depending on whether you want to continue or start anew, it processes your dataset before calling `Trainer.train()`.
4.  **Logging of Metrics**: As part of the training process, metrics like accuracy, perplexity, train loss, and validation metrics are logged.

**Customization Options:**

You can customize several aspects of your experiment, including:

*   The task type (e.g., "text-generation")
*   Model configuration
*   Data collator (by specifying `default_data_collator` as you can if you desire)
*   Training settings (`do_train`, `do_eval`, `max Train samples`, `max eval sampl


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CODE:

def _mp_fn(index):
    # For xla_spawn (TPUs)


SUMMARY:
**Summary**

`_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.

**Key Details**

* Purpose: Initialize multi-threading process
* Implementation: Calls `main()`
* Parameters: `index` (not used)

Note: The exact purpose and implementation of the `main()` function are not specified in this code chunk.


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/language-modeling/run_clm_no_trainer.py


key: chroma:document
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CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv, txt or a json file containing the training data."
    )
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv, txt or a json file containing the validation data."
    )
    parser.add_argument(
        "--validation_split_percentage",
        default=5,
        help="The percentage of the train set used as validation set in case there's no validation split",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="Model type to use if training from scratch.",
        choices=MODEL_TYPES,
    )
    parser.add_argument(
        "--block_size",
        type=int,
        default=None,
        help=(
            "Optional input sequence length after tokenization. The training dataset will be truncated in block of"
            " this size for training. Default to the model max input length for single sentence inputs (take into"
            " account special tokens)."
        ),
    )
    parser.add_argument(
        "--preprocessing_num_workers",
        type=int,
        default=None,
        help="The number of processes to use for the preprocessing.",
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    parser.add_argument(
        "--low_cpu_mem_usage",
        action="store_true",
        help=(
            "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
            "If passed, LLM loading time and RAM consumption will be benefited."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
        raise ValueError("Need either a dataset name or a training/validation file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            if extension not in ["csv", "json", "txt"]:
                raise ValueError("`train_file` should be a csv, json or txt file.")
        if args.validation_file is not None:
            extension = args.validation_file.split(".")[-1]
            if extension not in ["csv", "json", "txt"]:
                raise ValueError("`validation_file` should be a csv, json or txt file.")

    if args.push_to_hub:
        if args.output_dir is None:
            raise ValueError("Need an `output_dir` to create a repo when `--push_to_hub` is passed.")



SUMMARY:
**Summary**

This 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.

### Main Purpose and Functionality

The function `parse_args()` parses the command-line arguments, allowing users to configure various hyperparameters, such as:

* Model architectures (e.g., `model_name_or_path`)
* Training parameters (e.g., `num_train_epochs`, `batch_size`, `learning_rate`)
* Data loading options (e.g., `train_file`, `validation_file`)
* Output directory and push-to-Hub configuration

### Key Implementation Details

The function uses the following key algorithms, patterns, or design choices:

* Args parsing using `argparse`
* Sanity checks to ensure valid input values for user-provided files
* Hyperparameter validation for training parameters (e.g., batch sizes, learning rates)

### Notable Dependencies and Requirements

This code relies on the following external dependencies and interfaces:

* `datasets` library for loading datasets
* `huggingface.co/models` for accessing pre-trained models
* `argparse` library for defining command-line arguments


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CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_clm_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            args.dataset_name, args.dataset_config_name, trust_remote_code=args.trust_remote_code
        )
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[:{args.validation_split_percentage}%]",
                trust_remote_code=args.trust_remote_code,
            )
            raw_datasets["train"] = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                split=f"train[{args.validation_split_percentage}%:]",
                trust_remote_code=args.trust_remote_code,
            )
    else:
        data_files = {}
        dataset_args = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
            dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
        raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{args.validation_split_percentage}%]",
                **dataset_args,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{args.validation_split_percentage}%:]",
                **dataset_args,
            )

    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.config_name:
        config = AutoConfig.from_pretrained(
            args.config_name,
            trust_remote_code=args.trust_remote_code,
        )
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            args.model_name_or_path,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForCausalLM.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            low_cpu_mem_usage=args.low_cpu_mem_usage,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)

    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    column_names = raw_datasets["train"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]

    def tokenize_function(examples):
        return tokenizer(examples[text_column_name])

    with accelerator.main_process_first():
        tokenized_datasets = raw_datasets.map(
            tokenize_function,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on dataset",
        )

    if args.block_size is None:
        block_size = tokenizer.model_max_length
        if block_size > config.max_position_embeddings:
            logger.warning(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                f"Using block_size={min(1024, config.max_position_embeddings)} instead. You can change that default value by passing --block_size xxx."
            )
            block_size = min(1024, config.max_position_embeddings)
    else:
        if args.block_size > tokenizer.model_max_length:
            logger.warning(
        
SUMMARY:
This code is for training a PyTorch model using the Transformer architecture. The model is trained on an entire corpus of text data and produces a sequence-to-sequence model that can translate or generate new text based on the input prompt.

Here are some key features and behaviors:

1. **Training loop**: The model trains iteratively, updating its weights using backpropagation to minimize the loss function.
2. **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.
3. **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.
4. **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.
5. **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.

However, there are also some areas for potential improvement:

1. **Data preprocessing**


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification_no_trainer.py


key: chroma:document
string_value:
CODE:

def parse_args():
    parser = argparse.ArgumentParser(description="Fine-tune a Transformers model on an image classification dataset")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default="cifar10",
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset)."
        ),
    )
    parser.add_argument("--train_dir", type=str, default=None, help="A folder containing the training data.")
    parser.add_argument("--validation_dir", type=str, default=None, help="A folder containing the validation data.")
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--train_val_split",
        type=float,
        default=0.15,
        help="Percent to split off of train for validation",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        default="google/vit-base-patch16-224-in21k",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " This option should only be set to `True` for repositories you trust and in which you have read the"
            " code, as it will execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    parser.add_argument(
        "--ignore_mismatched_sizes",
        action="store_true",
        help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
    )
    parser.add_argument(
        "--image_column_name",
        type=str,
        default="image",
        help="The name of the dataset column containing the image data. Defaults to 'image'.",
    )
    parser.add_argument(
        "--label_column_name",
        type=str,
        default="label",
        help="The name of the dataset column containing the labels. Defaults to 'label'.",
    )
    args = parser.parse_args()

    # Sanity checks
    if args.dataset_name is None and args.train_dir is None and args.validation_dir is None:
        raise ValueError("Need either a dataset name or a training/validation folder.")

    if args.push_to_hub or args.with_tracking:
        if args.output_dir is None:
            raise ValueError(
                "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
            )

    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)



SUMMARY:
**Function Summary:**

This 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.

**Key Features:**

* Generates command-line arguments using `argparse`
* Defines over 30 hyperparameters with default values
* Supports loading pre-trained models from HuggingFace Hub or own repositories
* Enforces sanity checks for required inputs

**Function Signature:**
```python
def parse_args():
    parser = ...  # construct parser object and define arguments
    args = parser.parse_args()
    return args
```
**Notable Functions/Variables:**

* `argparse.ArgumentParser`: creates an argument parser object
* `SchedulerType` (not defined in this code snippet): represents different learning rate schedules
* `output_dir`, `train_dir`, `validation_dir`: paths to output, training, and validation directories respectively


key: chroma:document
string_value:
CODE:

def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_image_classification_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    logger.info(accelerator.state)
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own training and evaluation files (see below)
    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).

    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        dataset = load_dataset(args.dataset_name, trust_remote_code=args.trust_remote_code)
    else:
        data_files = {}
        if args.train_dir is not None:
            data_files["train"] = os.path.join(args.train_dir, "**")
        if args.validation_dir is not None:
            data_files["validation"] = os.path.join(args.validation_dir, "**")
        dataset = load_dataset(
            "imagefolder",
            data_files=data_files,
        )
        # See more about loading custom images at
        # https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder.

    dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
    if args.image_column_name not in dataset_column_names:
        raise ValueError(
            f"--image_column_name {args.image_column_name} not found in dataset '{args.dataset_name}'. "
            "Make sure to set `--image_column_name` to the correct audio column - one of "
            f"{', '.join(dataset_column_names)}."
        )
    if args.label_column_name not in dataset_column_names:
        raise ValueError(
            f"--label_column_name {args.label_column_name} not found in dataset '{args.dataset_name}'. "
            "Make sure to set `--label_column_name` to the correct text column - one of "
            f"{', '.join(dataset_column_names)}."
        )

    # If we don't have a validation split, split off a percentage of train as validation.
    args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
    if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
        split = dataset["train"].train_test_split(args.train_val_split)
        dataset["train"] = split["train"]
        dataset["validation"] = split["test"]

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    labels = dataset["train"].features[args.label_column_name].names
    label2id = {label: str(i) for i, label in enumerate(labels)}
    id2label = {str(i): label for i, label in enumerate(labels)}

    # Load pretrained model and image processor
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        args.model_name_or_path,
        num_labels=len(labels),
        id2label=id2label,
        label2id=label2id,
        finetuning_task="image-classification",
        trust_remote_code=args.trust_remote_code,
    )
    image_processor = AutoImageProcessor.from_pretrained(
        args.model_name_or_path,
        trust_remote_code=args.trust_remote_code,
    )
    model = AutoModelForImageClassification.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        ignore_mismatched_sizes=args.ignore_mismatched_sizes,
        trust_remote_code=args.trust_remote_code,
    )

    # Preprocessing the datasets

    # Define torchvision transforms to be applied to each image.
    if "shortest_edge" in image_processor.size:
        size = image_processor.size["shortest_edge"]
    else:
        size = (image_processor.size["height"], image_processor.size["width"])
    normalize = (
        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
        if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
        else Lambda(lambda x: x)
    )
    train_transforms = Compose(
        [
            RandomResizedCrop(size),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]
    )
    val_transforms = Compose(
        [
            Resize(size),
            CenterCrop(size),
            ToTensor(),
            normalize,
        ]
    )

    def preprocess_train(example_batch):
        """Apply _train_transforms across a batch."""
        example_batch["pixel_values"] = [
            train_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
        ]
        return example_batch

    def preprocess_val(example_batch):
        """Apply _val_transforms across a batch."""
        example_batch["pixel_values"] = [
            val_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
        ]
        return example_batch

    with accelerator.main_process_first():
        if args.max_train_samples is not None:
            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
        # Set the training transforms
        train_dataset = dataset["train"].with_transform(preprocess_train)
        if args.max_eval_samples is not None:
            dataset["validation"] = dataset["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples))
        # Set the validation transforms
        eval_dataset = dataset["validation"].with_transform(preprocess_val)

    # DataLoaders creation:
    def collate_fn(examples):
        pixel_values = torch.stack([example["pixel_values"] for example in examples])
        labels = torch.tensor([example[args.label_column_name] for example in examples])
        return {"pixel_values": pixel_values, "labels": labels}

    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size
    )
    eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=args.per_device_eval_batch_size)

    # Optimizer
    # Split weights in two groups, one with weight decay and the other not.
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args.weight_decay,
        },
        {
            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
            "weight_decay": 0.0,
        },
    ]
    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = 
SUMMARY:
This code is used to train a model and evaluate its performance on a dataset. Here's a brief summary:

**Training**

* The model is trained for multiple epochs
* During each epoch, the model processes batches of data from a training dataset
* The loss is calculated for each batch and backpropagated through the network
* The optimizer updates the model parameters based on the gradient

**Evaluation**

* After each epoch, the model evaluates its performance on a validation dataset
* The accuracy, loss,


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/image-classification/run_image_classification.py


key: chroma:document
string_value:
CODE:

def pil_loader(path: str):
    with open(path, "rb") as f:
        im = Image.open(f)


SUMMARY:
**pil_loader Function Summary**

### Purpose and Functionality
The `pil_loader` function loads an image from a given path and converts it to RGB color mode.

### Key Implementation Details
- Opens the specified file in binary read mode (`"rb"`).
- Uses the Python Imaging Library (PIL) to open the image, represented by the `im` variable.
- Converts the loaded image to the RGB color space using the `convert("RGB")` method.

### Notable Dependencies or Requirements

* **Python Imaging Library (PIL):** Import in the original codebase is required to utilize this function.
* **Image.open() :** Utilizes a PIL class (`<module.pil.Image>`) to create an image object.


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_image_classification", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Initialize our dataset and prepare it for the 'image-classification' task.
    if data_args.dataset_name is not None:
        dataset = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        data_files = {}
        if data_args.train_dir is not None:
            data_files["train"] = os.path.join(data_args.train_dir, "**")
        if data_args.validation_dir is not None:
            data_files["validation"] = os.path.join(data_args.validation_dir, "**")
        dataset = load_dataset(
            "imagefolder",
            data_files=data_files,
            cache_dir=model_args.cache_dir,
        )

    dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
    if data_args.image_column_name not in dataset_column_names:
        raise ValueError(
            f"--image_column_name {data_args.image_column_name} not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--image_column_name` to the correct audio column - one of "
            f"{', '.join(dataset_column_names)}."
        )
    if data_args.label_column_name not in dataset_column_names:
        raise ValueError(
            f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--label_column_name` to the correct text column - one of "
            f"{', '.join(dataset_column_names)}."
        )

    def collate_fn(examples):
        pixel_values = torch.stack([example["pixel_values"] for example in examples])
        labels = torch.tensor([example[data_args.label_column_name] for example in examples])
        return {"pixel_values": pixel_values, "labels": labels}

    # If we don't have a validation split, split off a percentage of train as validation.
    data_args.train_val_split = None if "validation" in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
        split = dataset["train"].train_test_split(data_args.train_val_split)
        dataset["train"] = split["train"]
        dataset["validation"] = split["test"]

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    labels = dataset["train"].features[data_args.label_column_name].names
    label2id, id2label = {}, {}
    for i, label in enumerate(labels):
        label2id[label] = str(i)
        id2label[str(i)] = label

    # Load the accuracy metric from the datasets package
    metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p):
        """Computes accuracy on a batch of predictions"""
        return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)

    config = AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path,
        num_labels=len(labels),
        label2id=label2id,
        id2label=id2label,
        finetuning_task="image-classification",
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
        
SUMMARY:
**Trainer Class in Hugging Face Transformers**

Below is a concise version of the Trainer class defined in the Hugging Face Transformers library.

```python
from transformers import AutoModelForImageClassification, AutoImageProcessor, Compose, RandomResizedCrop, Resize, CenterCrop, Lambda, Normalize, TimmWrapperImageProcessor

class Trainer:
    """
        The :class:`~transformers.Trainer` class is used for training or fine-tuning a model.
        
        Args:
            **args:  
                See :func:`transformers.TrainerArgs`
            
            model (Model): Model to be trained
            args (TrainingArguments): Training arguments
            
        Classes:
            :: :class:`~transformers.data.Datasets`: Pre-computed datasets.
           (`train_dataset`: The train data)
        """
    def __init__(self, 
                 model: AutoModelForImageClassification,
                 args: TrainingArguments = TrainingArguments(),
                 
                 # Dataset
                train_dataset: Datasets = None,
                eval_dataset: Optional[Datasets] = None,

                 # Data processing
                data_collator: Callable[[Sequence], Dict[str, Any]] = None,
                
                compute_metrics: Callable[[DataCollatorWithBatch], Metrics] = None,

                # Training related
                ignore_mismatched_sizes: bool = True,
                
                gradient_accumulation_steps: int = 1,

                per_device_train_batch_size: int = 8,
                per_device_eval_batch_size: Optional[int] = None,
                per_device_predict batch_size: Optional[int] = None,
                
                max_grad_norm: float = 1.0,
                
                # Gradient Clipping
                gradient_clip_value: float = None,  # default is set to infinity
        
        training_args=TrainingArguments(
            save_on_each_node=True,   
            load_best_model_at_end=False,
            metric_for_best_model="_accuracy",
            greater_is_better=True,
            num_train_epochs=int(args.train_args.num_train_epochs), 
            per_device_train_batch_size = args.per_device_train_batch_size if 'per_device_train_batch_size' in str(args) else 8,
            per_device_eval_batch_size =   args.per_device_eval_batch_size if 'per_device_eval_batch_size' in str(args) else None,
            per_device_predict_batch_size = 128, 
            total_train_steps = None,  
            optimization_impl='AdamW',  
                no_cuda = not has_cuda(),
                seed= random.randint(42, 212),  
        
            save_total_limit=2,
            warmup_steps=0 if args.dry_run else int(args.warmup_percentage * args.train_args.num_train_epochs),
            weight_decay=args.weight_decay,
            load_best_and_tray=False,
        ):
            
        self.model = model
        self.args = training_args
        # Model configuration data
  


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py


key: chroma:document
string_value:
CODE:

def list_field(default=None, metadata=None):


SUMMARY:
**Function Summary:** `	list_field`

**Primary Purpose:**
Creates and returns a Django admin field, leveraging the `field` function to initialize it with a default value and specified metadata.

**Key Implementation Details:**

*   Uses `default_factory=lambda: default` to set an attribute on the field instance.
*   Passes `metadata=metadata` as a keyword argument to customize the field's metadata.

**Notable Dependencies/Requirements:**
None explicitly stated, but it is assumed the `django.admin_fields` module or equivalent is imported.


key: chroma:document
string_value:
CODE:

def create_vocabulary_from_data(
    datasets: DatasetDict,
    word_delimiter_token: Optional[str] = None,
    unk_token: Optional[str] = None,
    pad_token: Optional[str] = None,
):
    # Given training and test labels create vocabulary
    def extract_all_chars(batch):
        all_text = " ".join(batch["target_text"])
        vocab = list(set(all_text))
        return {"vocab": [vocab], "all_text": [all_text]}

    vocabs = datasets.map(
        extract_all_chars,
        batched=True,
        batch_size=-1,
        keep_in_memory=True,
        remove_columns=datasets["train"].column_names,
    )

    # take union of all unique characters in each dataset
    vocab_set = functools.reduce(
        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
    )

    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}

    # replace white space with delimiter token
    if word_delimiter_token is not None:
        vocab_dict[word_delimiter_token] = vocab_dict[" "]
        del vocab_dict[" "]

    # add unk and pad token
    if unk_token is not None:
        vocab_dict[unk_token] = len(vocab_dict)

    if pad_token is not None:
        vocab_dict[pad_token] = len(vocab_dict)



SUMMARY:
**Concise Summary**

Creates 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:

* Unique character set (merged across all datasets)
* Replaced whit space with delimiter token (`word_delimiter_token`)
* Added unk token (`unk_token`)
* Added pad token (`pad_token`) at the end

**Key Implementation Details**

* Maps datasets to extract unique character sets
* Aggregates these character sets using functools.reduce
* Populates vocabulary dictionary with sorted, indexed unique characters
* Handles special tokens:
  - Replaces whit space with `word_delimiter_token` if specified
  - Adds `unk_token` and `pad_token` to the end of the dictionary if specified

**Notable Dependencies**

* ` datasets`: A DatasetDict object containing training and test labels
* External Dependency: `funτος.reduce`

Note: 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.


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_speech_recognition_ctc_adapter", model_args, data_args)

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # 1. First, let's load the dataset
    raw_datasets = DatasetDict()

    if training_args.do_train:
        raw_datasets["train"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.train_split_name,
            token=data_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )

        if data_args.audio_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
                " Make sure to set `--audio_column_name` to the correct audio column - one of"
                f" {', '.join(raw_datasets['train'].column_names)}."
            )

        if data_args.text_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
                "Make sure to set `--text_column_name` to the correct text column - one of "
                f"{', '.join(raw_datasets['train'].column_names)}."
            )

        if data_args.max_train_samples is not None:
            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))

    if training_args.do_eval:
        raw_datasets["eval"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.eval_split_name,
            token=data_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )

        if data_args.max_eval_samples is not None:
            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))

    # 2. We remove some special characters from the datasets
    # that make training complicated and do not help in transcribing the speech
    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
    # that could be easily picked up by the model
    chars_to_ignore_regex = (
        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
    )
    text_column_name = data_args.text_column_name

    def remove_special_characters(batch):
        if chars_to_ignore_regex is not None:
            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
        else:
            batch["target_text"] = batch[text_column_name].lower() + " "
        return batch

    with training_args.main_process_first(desc="dataset map special characters removal"):
        raw_datasets = raw_datasets.map(
            remove_special_characters,
            remove_columns=[text_column_name],
            desc="remove special characters from datasets",
        )

    # save special tokens for tokenizer
    word_delimiter_token = data_args.word_delimiter_token
    unk_token = data_args.unk_token
    pad_token = data_args.pad_token

    # 3. Next, let's load the config as we might need it to create
    # the tokenizer
    # load config
    config = AutoConfig.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        token=data_args.token,
        trust_remote_code=data_args.trust_remote_code,
    )

    # 4. Next, if no tokenizer file is defined,
    # we create the vocabulary of the model by extracting all unique characters from
    # the training and evaluation datasets
    # We need to make sure that only first rank saves vocabulary
    # make sure all processes wait until vocab is created
    tokenizer_name_or_path = model_args.tokenizer_name_or_path
    tokenizer_kwargs = {}

    vocab_dict = {}
    if tokenizer_name_or_path is not None:
        # load vocabulary of other adapter languages so that new language can be appended
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name_or_path,
            token=data_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )
        vocab_dict = tokenizer.vocab.copy()
        if tokenizer.target_lang is None:
            raise ValueError("Make sure to load a multi-lingual tokenizer with a set target language.")

        if data_args.target_language in tokenizer.vocab and not data_args.overwrite_lang_vocab:
            logger.info(
                "Adapter language already exists."
                " Skipping vocabulary creating. If you want to create a new vocabulary"
                f" for {data_args.target_language} make sure to add '--overwrite_lang_vocab'"
            )
        else:
            tokenizer_name_or_path = None

    if tokenizer_name_or_path is None:
        # save vocab in training output dir
        tokenizer_name_or_path = training_args.output_dir

        vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")

        with training_args.main_process_first():
            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
                try:
                    os.remove(vocab_file)
                except OSError:
                    # in shared file-systems it might be the case that
                    # two processes try to delete the vocab file at the some time
                    pass

        with training_args.main_process_first(desc="dataset map vocabulary creation"):
            if not os.path.isfile(vocab_file):
                os.makedirs(tokenizer_name_or_path, exist_ok=True)
                lang_dict = create_vocabulary_from_data(
                    raw_datasets,
                    word_delimiter_token=word_delimiter_token,
                    unk_token=unk_token,
                    pad_token=pad_token,
                )

                # if we doing adapter language training, save
                # vocab with adpter language
                if data_args.target_language is not None:
                    vocab_dict[data_args.target_language] = lang_dict

                # save vocab dict to be loaded into tokenizer
                with open(vocab_file, "w") as file:
                    json.dump(vocab_dict, file)

        # if tokenizer has just been created
        # it is defined by `tokenizer_class` if present in config else by `model_type`
        tokenizer_kwargs = {
            "config": config if config.tokenizer_class is not None else None,
            "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
            "unk_token": unk_token,
            "pad_token": pad_token,
            "word_delimiter_token": word_delimiter_token,
            "target_lang": data_args.target_language,
        }

    # 5. Now we can instantiate the feature extractor, tokenizer and model
    # Note for distributed traini
SUMMARY:
Here is a concise summary of the code:

This is a script for training and evaluating a Wav2Vec 2 model using the Hugging Face Transformers library. The script performs the following tasks:

1. Loads the dataset, tokenizer, and processor configurations
2. Creates a Trainer object with customizable arguments (e.g., data collator, model name)
3. Trains the model on the training dataset if `do_train` is 


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py


key: chroma:document
string_value:
CODE:

def main():
    # 1. Parse input arguments
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args)

    # 2. Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # 3. Detecting last checkpoint and eventually continue from last checkpoint
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # 4. Load dataset
    raw_datasets = DatasetDict()

    if training_args.do_train:
        raw_datasets["train"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.train_split_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )

    if training_args.do_eval:
        raw_datasets["eval"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.eval_split_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )

    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
        raise ValueError(
            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--audio_column_name` to the correct audio column - one of "
            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
        )

    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
        raise ValueError(
            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--text_column_name` to the correct text column - one of "
            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
        )

    # 5. Load pretrained model, tokenizer, and feature extractor
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    # SpecAugment for whisper models
    if getattr(config, "model_type", None) == "whisper":
        config.update({"apply_spec_augment": model_args.apply_spec_augment})

    feature_extractor = AutoFeatureExtractor.from_pretrained(
        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForSpeechSeq2Seq.from_pretrained(
        model_args.model_name_or_path,
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    if model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    if model_args.freeze_encoder:
        model.freeze_encoder()
        model.model.encoder.gradient_checkpointing = False

    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
        # We only need to set the language and task ids in a multilingual setting
        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
        model.generation_config.language = data_args.language
        model.generation_config.task = data_args.task
    elif data_args.language is not None:
        raise ValueError(
            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
            "only be set for multilingual checkpoints."
        )

    # TODO (Sanchit): deprecate these arguments in v4.41
    if model_args.forced_decoder_ids is not None:
        logger.warning(
            "The use of `forced_decoder_ids` is deprecated and will be removed in v4.41."
            "Please use the `language` and `task` arguments instead"
        )
        model.generation_config.forced_decoder_ids = model_args.forced_decoder_ids
    else:
        model.generation_config.forced_decoder_ids = None
        model.config.forced_decoder_ids = None

    if model_args.suppress_tokens is not None:
        logger.warning(
            "The use of `suppress_tokens` is deprecated and will be removed in v4.41."
            "Should you need `suppress_tokens`, please manually set them in the fine-tuning script."
        )
        model.generation_config.suppress_tokens = model_args.suppress_tokens

    # 6. Resample speech dataset if necessary
    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
    if dataset_sampling_rate != feature_extractor.sampling_rate:
        raw_datasets = raw_datasets.cast_column(
            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
        )

    # 7. Preprocessing the datasets.
    # We need to read the audio files as arrays and tokenize the targets.
    max_input_length = data_args.max_duration_in_seconds * feature_extractor.samp
SUMMARY:
This is the code for a speech recognition model, specifically an automatic speech recognition (ASR) system. The code is written in Python and uses the Hugging Face Transformers library.

The code consists of three main parts:

1. **Data Preparation**: This section includes data loading, preprocessing, and loading a metric (WER, Word Error Rate) for evaluation.
2. **Model Training**: This section trains the speech recognition model using the `Seq2SeqTrainer` class from Hugging Face's Transformers library. The training process involves:
	* Loading pre-trained models and tokens
	* Defining the custom data collator and processor
	* Initializing the trainer
	* Triggering the training loop
3. **Evaluation**: This section runs the evaluation of the trained model on a test dataset using the previously loaded metric.
4. **Model Saving and Storage**: The code saves the trained model, tokenizer, and configuration to a specified output directory.

Key parameters and hyperparameters used in this code inclu


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py


key: chroma:document
string_value:
CODE:

def list_field(default=None, metadata=None):


SUMMARY:
**Summary**

This 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.

**Key Details**

* Primary purpose: Create a database model field
* Implementation details:
	+ Uses Django's `field` function
	+ Sets a default value using a lambda function (`default_factory=lambda: default`)
	+ Passes metadata to the created field (`metadata=metadata`)
* Notable dependencies:
	+ Django (the `field` function is part of its ORM)

**RAG System Classification**

* Tag: `ORM`
* Category: `Model Field Creation`
* Priority: H
* Severity: M


key: chroma:document
string_value:
CODE:

def create_vocabulary_from_data(
    datasets: DatasetDict,
    word_delimiter_token: Optional[str] = None,
    unk_token: Optional[str] = None,
    pad_token: Optional[str] = None,
):
    # Given training and test labels create vocabulary
    def extract_all_chars(batch):
        all_text = " ".join(batch["target_text"])
        vocab = list(set(all_text))
        return {"vocab": [vocab], "all_text": [all_text]}

    vocabs = datasets.map(
        extract_all_chars,
        batched=True,
        batch_size=-1,
        keep_in_memory=True,
        remove_columns=datasets["train"].column_names,
    )

    # take union of all unique characters in each dataset
    vocab_set = functools.reduce(
        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
    )

    vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}

    # replace white space with delimiter token
    if word_delimiter_token is not None:
        vocab_dict[word_delimiter_token] = vocab_dict[" "]
        del vocab_dict[" "]

    # add unk and pad token
    if unk_token is not None:
        vocab_dict[unk_token] = len(vocab_dict)

    if pad_token is not None:
        vocab_dict[pad_token] = len(vocab_dict)



SUMMARY:
Here's a concise summary of the code:

**Function Purpose:** Creates a vocabulary dictionary from a collection of datasets, allowing for flexible tokenization and padding.

**Main Functionality:**

1. Extracts unique characters from training and test data using `extract_all_chars` function.
2. Applies filter to remove irrelevant columns from dataset.
3. Combines character sets into a single set using `functools.reduce`.
4. Replaces white space with delimiter token if specified, removes empty entry.
5. Assigns special tokens (UNK and PAD) values based on usage.

**Key Insights:**

* Utilizes `DatasetDict` as input data source.
* Incorporates string manipulation (`join`, `set`) for vocabulary generation.
* Introduces custom token mapping through conditional statements.

**Notable Dependencies/Requirements:**

* `DatasetDict`
* `functools.reduce`
* String manipulation libraries (e.g., `str.join`)


key: chroma:document
string_value:
CODE:

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_speech_recognition_ctc", model_args, data_args)

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # 1. First, let's load the dataset
    raw_datasets = DatasetDict()

    if training_args.do_train:
        raw_datasets["train"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.train_split_name,
            token=data_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )

        if data_args.audio_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
                " Make sure to set `--audio_column_name` to the correct audio column - one of"
                f" {', '.join(raw_datasets['train'].column_names)}."
            )

        if data_args.text_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
                "Make sure to set `--text_column_name` to the correct text column - one of "
                f"{', '.join(raw_datasets['train'].column_names)}."
            )

        if data_args.max_train_samples is not None:
            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))

    if training_args.do_eval:
        raw_datasets["eval"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.eval_split_name,
            token=data_args.token,
            trust_remote_code=data_args.trust_remote_code,
        )

        if data_args.max_eval_samples is not None:
            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))

    # 2. We remove some special characters from the datasets
    # that make training complicated and do not help in transcribing the speech
    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
    # that could be easily picked up by the model
    chars_to_ignore_regex = (
        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
    )
    text_column_name = data_args.text_column_name

    def remove_special_characters(batch):
        if chars_to_ignore_regex is not None:
            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
        else:
            batch["target_text"] = batch[text_column_name].lower() + " "
        return batch

    with training_args.main_process_first(desc="dataset map special characters removal"):
        raw_datasets = raw_datasets.map(
            remove_special_characters,
            remove_columns=[text_column_name],
            desc="remove special characters from datasets",
        )

    # save special tokens for tokenizer
    word_delimiter_token = data_args.word_delimiter_token
    unk_token = data_args.unk_token
    pad_token = data_args.pad_token

    # 3. Next, let's load the config as we might need it to create
    # the tokenizer
    # load config
    config = AutoConfig.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        token=data_args.token,
        trust_remote_code=data_args.trust_remote_code,
    )

    # 4. Next, if no tokenizer file is defined,
    # we create the vocabulary of the model by extracting all unique characters from
    # the training and evaluation datasets
    # We need to make sure that only first rank saves vocabulary
    # make sure all processes wait until vocab is created
    tokenizer_name_or_path = model_args.tokenizer_name_or_path
    tokenizer_kwargs = {}
    if tokenizer_name_or_path is None:
        # save vocab in training output dir
        tokenizer_name_or_path = training_args.output_dir

        vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")

        with training_args.main_process_first():
            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
                try:
                    os.remove(vocab_file)
                except OSError:
                    # in shared file-systems it might be the case that
                    # two processes try to delete the vocab file at the some time
                    pass

        with training_args.main_process_first(desc="dataset map vocabulary creation"):
            if not os.path.isfile(vocab_file):
                os.makedirs(tokenizer_name_or_path, exist_ok=True)
                vocab_dict = create_vocabulary_from_data(
                    raw_datasets,
                    word_delimiter_token=word_delimiter_token,
                    unk_token=unk_token,
                    pad_token=pad_token,
                )

                # save vocab dict to be loaded into tokenizer
                with open(vocab_file, "w") as file:
                    json.dump(vocab_dict, file)

        # if tokenizer has just been created
        # it is defined by `tokenizer_class` if present in config else by `model_type`
        tokenizer_kwargs = {
            "config": config if config.tokenizer_class is not None else None,
            "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
            "unk_token": unk_token,
            "pad_token": pad_token,
            "word_delimiter_token": word_delimiter_token,
        }

    # 5. Now we can instantiate the feature extractor, tokenizer and model
    # Note for distributed training, the .from_pretrained methods guarantee that only
    # one local process can concurrently download model & vocab.

    # load feature_extractor and tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        tokenizer_name_or_path,
        token=data_args.token,
        trust_remote_code=data_args.trust_remote_code,
        **tokenizer_kwargs,
    )
    feature_extractor = AutoFeatureExtractor.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        token=data_args.token,
        trust_remote_code=data_args.trust_remote_code,
    )

    # adapt config
    config.update(
        {
            "feat_proj_dropout": model_args.feat_proj_dropout,
            "attention_dropout": model_args.attention_dropout,
            "hidden_dropout": model_args.hidden_dropout,
            "final_dropout": model_args.final_dropout,
       
SUMMARY:
This code is for fine-tuning the Wav2Vec2 model on an automatic speech recognition task using the Hugging Face Transformers library. It consists of the following steps:

1. Data loading and preprocessing
2. Model initialization and configuration
3. Training the model with a custom data collator
4. Evaluation of the trained model
5. Saving the model and its metrics, as well as pushing it to the HUGGINGFACE MODEL Hub.

Here is a concise summary of the code:

- **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.
- **Input files**: The input files required are a file named `data.json` and either a checkpoint filename or directory n


key: chroma:document
string_value:
CODE:
# Example where we only want to only modify the docstring
class MyNewModel2Config(LlamaConfig):
    r"""
    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Gemma-7B.
    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.
    Args:
        vocab_size (`int`, *optional*, defaults to 256000):
            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GemmaModel`]
    ```python
    >>> from transformers import GemmaModel, GemmaConfig
    >>> # Initializing a Gemma gemma-7b style configuration
    >>> configuration = GemmaConfig()
    >>> # Initializing a model from the gemma-7b style configuration
    >>> model = GemmaModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config


SUMMARY:
**Summary**

`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.


**Key Implementation Details**

* Vocabulary size (`vocab_size`): an integer parameter that defines the number of different tokens represented by `inputs_ids`.
* Inheritance from `PretrainedConfig`: Configuration objects can be used to control model outputs.
* Supported methods and interfaces:
	+ Conforming to `LlamaConfig`
	+ Extending `PretrainedConfig`

**Notable Dependencies or Requirements**

* The model is inspired by the Gemma-7B, reference to a specific pre-trained transformer model on Hugging Face.

Note: I have preserved the exact class/function names and followed the technical terms used in the original code.


key: chroma:document
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CODE:
# Example where alllllll the dependencies are fetched to just copy the entire class
class MyNewModel2ForSequenceClassification(GemmaForSequenceClassification):


SUMMARY:
**Summary**

`MyNewModel2ForSequenceClassification` is a subclass of `GemmaForSequenceClassification`, extending its functionality for sequence classification tasks.

**Key Implementation Details**

* Exposes no additional methods or attributes, passing through to the parent class (`GemmaForSequenceClassification`).

**Notable Dependencies and Requirements**

* Inherited from: `GemmaForSequenceClassification`
* No external dependencies mentioned.
           


key: chroma:document
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CODE:
# Check that we can correctly change the prefix (here add Text part at the end of the name)
class Multimodal1TextModel(LlamaModel):


SUMMARY:
**Summary**

This 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.

**Key Implementation Details**

* Inherited interface: `LlamaModel`
* No distinct methods or attributes implemented

**Notable Dependencies or Requirements**

* `LlamaModel`: The superclass from which this class inherits, indicating that the `Multimodal1TextModel` relies on the properties and behavior of its parent class.
* External Dependencies: Not identified in this snippet.

**Primary Purpose**

To establish a textual extension of an existing model framework (`LlamaModel`), potentially serving as a foundation for text-modality specific functionality or applications.


key: docstring
string_value:
# Example where we only want to only add a new config argument and new arg doc
# here there is no `ARG` so we are gonna take parent doc


key: chroma:document
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CODE:
# here there is no `ARG` so we are gonna take parent doc
class MyNewModelConfig(LlamaConfig):
    r"""
    new_param (`int`, *optional*, defaults to `False`):
        A fun new parameter
    """

    def __init__(self, mlp_bias=True, new_param=0, **super_kwargs):
        super().__init__(self, **super_kwargs)
        self.mlp_bias = mlp_bias


SUMMARY:
**Summary**

*Class: MyNewModelConfig*
---------------------

The `MyNewModelConfig` class inherits from `LlamaConfig`. It introduces two primary parameters:
	+ `new_param`: An optional integer parameter with default value `False`.
	+ `mlp_bias`: A boolean parameter controlling the bias for multi-layer perceptrons.

**Implementation Details**
------------------------

* Initializes `MyNewModelConfig` with `self` as super class reference, accepting keyword arguments through `super_kwargs`.

**Notable Dependencies and Requirements**
-----------------------------------------

* The class uses inheritance from `LlamaConfig`, implying its requirement to have an environment that supports this base class.
* External dependency: `LlamaConfig`.
 
No additional dependencies are assumed.


key: chroma:document
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CODE:

class NewModelConfig(GemmaConfig):
    def __init__(
        self,
        vocab_size=256030,
        hidden_size=64,
        intermediate_size=90,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=16,
        head_dim=256,
        hidden_act="gelu_pytorch_tanh",
        hidden_activation=None,
        max_position_embeddings=1500,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        bos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=10000.0,
        attention_bias=False,
        attention_dropout=0.0,
        **kwargs,
    ):
        super().__init__(self, **kwargs)

    @property
    def num_heads(self):


SUMMARY:
**Class Summary: `NewModelConfig`**

*   **Purpose:** Configures the architecture of a new model using GemmaConfig as its base class.

**Primary Functionality:**
The 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.

**Implementation Details:**

*   **Hyperparameter Tuning:** The configuration includes various tunable hyperparameters such as `vocab_size`, `hidden_size`, `intermediate_size`, `num_hidden_layers`, and others.
*   **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.

**Notable Dependencies/Requirements:**

*   Inherited from GemmaConfig: `super().__init__(self, **kwargs)`.
*   Not explicitly listed but might be assumed or inherit from related classes/functions:
    *   `vocab_size` might require access to a vocabulary or dictionary class.
    *   `hidden_act` and `hidden_activation` could interact with related methods or functions for activation computations.

**Key Methods:**

*   `num_heads`: Returns the number of attention heads, which is stored in `self.num_attention_heads`.
*   Other configuration values can be accessed as instance attributes (e.g., `self.vocab_size`, `self.hidden_size`).


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/image_processing_new_imgproc_model.py


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CODE:

class ImgprocModelImageProcessor(BaseImageProcessor):
    r"""
    Constructs a IMGPROC_MODEL image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
            `do_resize` parameter in the `preprocess` method.
        size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
            method.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
            Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
            overridden by the `resample` parameter in the `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
            `do_rescale` parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
            overridden by the `rescale_factor` parameter in the `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
            overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
            Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_convert_rgb (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to RGB.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = True,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 384, "width": 384}
        size = get_size_dict(size, default_to_square=True)

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.do_convert_rgb = do_convert_rgb

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
        output_size = (size["height"], size["width"])
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    @filter_out_non_signature_kwargs()
    def preprocess(
        self,
        images: ImageInput,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        resample: PILImageResampling = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        do_convert_rgb: bool = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Controls the size of the image after `resize`. The shortest edge of the image is resized to
                `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
                is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
                edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean to normalize the image by if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defa
SUMMARY:
This is a PyTorch-based image processing class. The class seems to be designed for preprocessing and enhancing images in various ways, such as resizing, scaling, normalizing, and converting them to RGB format.

Here's a high-level overview of the class:

- The class has several attributes that determine how images are processed:
  - `do_rescale`: Whether or not to rescale the pixel values.
  - `rescale_factor`: The factor by which the pixel values should be scaled if rescaling is needed.
  - `do_normalize`: Whether or not to normalize the pixel values.
  - `image_mean` and `image_std`: Mean and standard deviation used for normalizing.
  - `size`: The size at which images are resized (for downsampling).
  - `resample`: The method used for resizing (e.g., nearest-neighbor, bicubic).
- It also attributes and methods that handle errors and edge cases:
  - `make_list_of_images` takes a collection of images as input.
  - `validate_preprocess_arguments` checks if all preprocessings arguments are valid.
  - It checks for invalid image type (i.e., PIL Image, np.ndarray, torch.Tensor, tf.Tensor or jax.ndarray).
- The class processes preprocessed images by:
  - C


key: chroma:document
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CODE:

class DummyRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        DummyRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):


SUMMARY:
**Summary: DummyRMSNorm Module**

The `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.

**Key Implementation Details:**

* Scales input tensor by square root of mean-variance, modified by epsilon value.
* Uses self-weight parameter to scale output.
* Employs `pow` and `mean` operations over input tensor with shape `(batch_size, seq_len, hidden_size)`.

**Notable Dependencies/Requirements:**

* PyTorch library (`torch`, `nn.Module`)
* TensorFlow-compatible data types (`float32`)


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CODE:

class DummyRotaryEmbedding(nn.Module):
    def __init__(
        self,
        config: DummyConfig,
        device=None,
    ):
        super().__init__()
        self.rope_kwargs = {}
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(
                self.config, device, seq_len=seq_len, **self.rope_kwargs
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling



SUMMARY:
**Summary:**

The `DummyRotaryEmbedding` class is a PyTorch module implementing RoPE (Rotation Embeddings) layers. It adapts to dynamic sequence lengths and applies attention scaling.

**Main Purpose and Functionality:**

This class is designed to compute frequency-based embeddings for input sequences, allowing the model to handle varying sequence lengths efficiently. The main functionality includes:

* Adaptive initialization of `inv_freq` based on sequence length
* Dynamic re-computation of `inv_freq` when sequence length grows or shrinks
* Application of attention scaling factor to post-process embeddings

**Key Implementation Details:**

* Use of `torch.autocast` for dynamic torch device type management
* Custom RoPE initialization function selection based on `rope_type`
* Usage of `position_ids` for frequency-based embedding computation
* Attention scaling factor application for post-processing embeddings

**Notable Dependencies and Requirements:**

* PyTorch module (`nn.Module`)
* External dependencies:
	+ `DummyConfig` class (config passing)
	+ `ROPE_INIT_FUNCTIONS` dictionary (rope type initialization function selection)

**Additional Notes:**

The RoPE implementation utilizes frequency-based embeddings for input sequences, enabling the model to adapt to varying sequence lengths while maintaining efficient computational complexity.


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CODE:

class DummyMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


SUMMARY:
**Class Summary: `DummyMLP`**

**Main Purpose and Functionality:** 
The `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.

**Key Implementation Details:**

* Uses feed-forward neural network (`nn.Module`) for model construction.
* Inclodes multiple linear transformations:
	+ `gate_proj`: projects input onto hidden layer
	+ `up_proj`: up-projects output from gateProj
  - `down_proj`: Down-projects and combines with act_fn(x)
* Utilizes an acturial function (`ACT2FN[config.hidden_act]`) to apply activation mapping.

**Notable Dependencies or Requirements:**

* Uses PyTorch's neural network module (`nn.Module`)
* References external dictionary (`ACT2FN`)

Please let me know if you want me to clarify any details.


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CODE:

def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 4]
    x2 = x[..., x.shape[-1] // 4 :]


SUMMARY:
**Summary**

Rotates the specified half of the hidden dimensions in input `x`. Specifically, it:

* Splits the input into two halves using index division by 4
* Rotates the second half (`x2`) to the beginning and keeps the first half (`x1`)
* Concatenates the rotated halves with opposite signs using `torch cat`

**Implementation Details**

* Uses PyTorch tensor indexing and concatenation

**Dependencies/Requirements**

* Requires `torch` library for tensor operations.


key: chroma:document
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CODE:

def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)


SUMMARY:
**Rotary Position Embedding Function Summary**

### Purpose and Functionality
Applies Rotary Position Embedding to query and key tensors.

#### Key Implementation Details

* Applies Rotatory Position Embedding using `cos(x) + rotate_half(sin(x))`
* Uses cosine (`cos`) and sine (`sin`) components of the embedding as specified arguments.
* Unsqueeze dimensions of `cos` and `sin` tensors for proper broadcasting with query (`q`) and key (`k`) tensors.

#### Notable Dependencies

* Inputs:
	+ `q`: Query tensor (torch.Tensor)
	+ `k`: Key tensor (torch.Tensor)
	+ `cos`, `sin`: Cosine and sine components of the embedding (torch.Tensor)
	+ `position_ids` (Deprecated, Unused): Optional position ID tensor
	+ `unsqueeze_dim`: Dimension to unsqueeze `cos[position_ids]` and `sin[position_ids]` for broadcasting
* Returns: Tuples of rotated query (`q_embed`) and key (`k_embed`) tensors.

#### Dependencies and Interfaces

* No external dependencies.
* Implies use of `torch.Tensor` type and vector operations.


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CODE:

def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)


SUMMARY:
**Summary:**

The `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.

**Key details:**

* Transform: Repeats along key-value heads dimension (`dim=1`)
* Repeats: `n_rep` repetitions
* Output shape: `(batch, num_attention_heads, seqlen, head_dim)`
* Equivalent to: `torch.repeat_interleave(x, dim=1, repeats=n_rep)`

**Notable dependencies/requirements:**

* `torch`: Required for tensor operations and repeat functionality.


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CODE:

def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()



SUMMARY:
**Concise Summary**

The `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:

1. Scales the key-value pairs by a factor of 2^module.num_key_value_groups.
2. Computes attention weights through matrix multiplication between query and scaled keys (dim = -1).
3. Applies causal masking if provided.
4. Softmax normalization and dropout are applied to the attention weights.
5. Computes output by multiplying scaled weights with value tensor.
6. Returns the output and scaled weights.

**Key Implementation Details**

* `torch.matmul` for matrix multiplication
* `nn.functional.softmax` for softmax normalization
* `nn.functional.dropout` for scaling weights

**Dependences and Notes**

* Inheritance: no external modules, only PyTorch's `nn.Module`
* Interfaces: implements Multi-Head Attention (uses repeated key-value groups)
* Scaling factor can be adjusted to the `module.num_key_value_groups`


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CODE:

class DummyAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: DummyConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)


SUMMARY:
**Summary:**

The `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.

**Key Features:**

* Takes a configuration object, layer index, and input states (hidden_states) as inputs.
* Projects hidden_states to query, key, and value vectors using linear layers.
* Applies position embeddings for cosine and sine values.

**Implementation Details:**

* Uses the `eager` attention implementation by default or based on the `_attn_implementation` configuration.
* Incorporates a past_key_value cache for efficient updates when caching positional information.
* Projects outputs through an output projection layer (`o_proj`) to obtain attention scores.

**Notable Dependencies and Requirements:**

* PyTorch's `nn.Module`
* `dummy_config` class (config object)
* `torch.nn.Linear`, `torch.utils.data.Tensor`


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CODE:

class DummyDecoderLayer(nn.Module):
    def __init__(self, config: DummyConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = DummyAttention(config=config, layer_idx=layer_idx)

        self.mlp = DummyMLP(config)
        self.input_layernorm = DummyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = DummyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)



SUMMARY:
**DummyDecoderLayer Class Summary**

### Purpose and Functionality
The `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.

### Key Implementation Details

* Performs self-attention using the `self_attn` method, which takes input embeddings, attention mask, and other optional arguments.
	+ Method Signature: `self.self_attn(hidden_states=..., attention_mask=..., ...)`
	+ Returns: `(hidden_states, self_attn_weights)`
* Applies linear transformation using the `mlp` method, which takes input embeddings after self-attention.
	+ Method Signature: `self.mlp(hidden_states=...)`
	+ Returns: `(hidden_states)`
* Normalizes inputs with layer normalization (`input_layernorm`) and post-attention normalization (`post_attention_layernorm`).

### Notable Dependencies and Requirements

* `config`: an instance of `DummyConfig`, which contains hyperparameters for the layer.
* External dependencies:
	+ `torch`
	+ `nn.Module` (from PyTorch)
	+ `torch.Tensor`
	+ `Optional`
	+ `torch.LongTensor`

### Inherited or Implemented Interfaces

* Implements the `nn.Module` interface from PyTorch.

Note: This summary focuses on the key implementation details and dependencies specific to the `DummyDecoderLayer` class, without providing unnecessary or generic information.


key: file_path
string_value:
/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/modular_new_imgproc_model.py


key: chroma:document
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CODE:

class ImgprocModelImageProcessor(BlipImageProcessor):
    def new_image_processing_method(self, pixel_values: torch.FloatTensor):


SUMMARY:
**Summary:**

This 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. 

**Key Details:**

- Algorithm: Simple Linear Scaling
- Method Signature: `torch.FloatTensor -> torch.FloatTensor`
- Interfaces: Inherited from `BlipImageProcessor`

**Notable Dependencies/Requirements:** 
- `torch` tensor library


key: file_path
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/home/gaby/projects/2025/transformers-4.48.1/examples/modular-transformers/configuration_my_new_model.py


key: chroma:document
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CODE:

class MyNewModelConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the MyNewModel-7B.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the MyNewModel model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MyNewModelModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. MyNewModel 1 supports up to 2048 tokens,
            MyNewModel 2 up to 4096, CodeLlama up to 16384.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'my_new_model3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'my_new_model3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`List[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`List[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'my_new_model3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'my_new_model3'. Scaling factor applied to high frequency components of the RoPE
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        head_dim (`int`, *optional*):
            The attention head dimension. If None, it will default to hidden_size // num_attention_heads

    ```python
    >>> from transformers import MyNewModelModel, MyNewModelConfig

    >>> # Initializing a MyNewModel my_new_model-7b style configuration
    >>> configuration = MyNewModelConfig()

    >>> # Initializing a model from the my_new_model-7b style configuration
    >>> model = MyNewModelModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
        new_param (`int`, *optional*, defaults to `False`):
            A fun new parameter
    """

    model_type = "my_new_model"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `MyNewModelModel`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        pretraining_tp=1
SUMMARY:
This code defines the configuration for a custom transformer model called `MyNewModel`. It consists of various hyperparameters and settings, such as:

* Model architecture (number of hidden layers, attention heads, intermediate size)
* Training settings (vocab size, max position embeddings, initial optimizer range)
* Hyperparameters (hidden act, beta test parameter)
* Performance metrics and validation scheme
* Output model type, key fields to ignore at inference, default tensor parallel plan

The 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. 

It'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


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CODE:

class DummyBertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )
        self.register_buffer(
            "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
        )

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        past_key_values_length: int = 0,
    ) -> torch.Tensor:
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
        # issue #5664
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)


SUMMARY:
**Summary:**
The `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.

**Purpose:** The main purpose of this class is to compute contextualized character-level representations by combining word embeddings with position and token type information.

**Key Implementations:**
* Embeds words using a learned embedding matrix (`word_embeddings`)
* Apps `position_embeddings` from word embeddings based on `position_ids`
* Applies `token_type_embeddings` using an embedding layer for token types
* Employs multi-head attention mechanisms through the `LayerNorm` and `dropout` layers

**Notable Dependencies:**
* PyTorch library (specifically, `torch.nn`)
* The Hugging Face Transformers library is referenced indirectly through its usage of `config` objects


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CODE:

class DummyBertSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(
            config, "position_embedding_type", "absolute"
        )
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

        self.is_decoder = config.is_decoder

    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        use_cache = past_key_value is not None
        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache:
                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
                    -1, 1
                )
            else:
                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r

            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in DummyBertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        if self.is_decoder:
            outputs = outputs + (past_key_value,)


SUMMARY:
**Class Summary**

`DummyBertSelfAttention`
======================

A PyTorch implementation of the multi-head self-attention mechanism from the BERT model architecture.

**Primary Purpose**
-------------------

This class computes the attention scores and context layer for self-supervised learning or next sentence prediction tasks.

**Key Implementation Details**
-----------------------------

* Uses a multihead decoder attention mechanism with dimensionality reduction (dot product).
* Applies position embeddings for relative position encoding.
* Supports different types of position embeddings: `absolute`, `relative_key`, and `relative_key_query`.
* Masks heads using a provided head mask tensor, if applicable.

**Notable Dependencies or Requirements**
------------------------------------------

* `torch`: PyTorch library with neural network module (`nn`).
* `math.sqrt()`: Mathematical operations to compute square roots.
* `nn.functional.softmax()` : Softmax activation function for normalizing attention scores.
* `nn.Dropout()`: Dropout probability layer to prevent overfitting.

**Method Summary**
------------------

### `__init__(config, position_embedding_type=None)` init

 Initializes the module with configuration and optional position embedding type.

### `transpose_for_scores(x: torch.Tensor) -> torch.Tensor` transpose for scores

 Transposes input tensor for multihead self-attention mechanism computation.

### `forward(hidden_states: torch.Tensor, ...)` forward pass

 Computes attention scores and context layer for self-supervised learning or next sentence prediction tasks.
 
 Returns a tuple of `(context_layer, attention_probs)` if `output_attentions` is True; otherwise returns only the first element.


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CODE:

class DummyBertSdpaSelfAttention(DummyBertSelfAttention):
    def __init__(self, config, position_embedding_type=None):
        super().__init__(config, position_embedding_type=position_embedding_type)
        self.dropout_prob = config.attention_probs_dropout_prob
        self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0")

    # Adapted from DummyBertSelfAttention
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
            # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once implemented.
            logger.warning_once(
                "DummyBertSdpaSelfAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support "
                "non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to "
                "the manual attention implementation, but specifying the manual implementation will be required from "
                "Transformers version v5.0.0 onwards. This warning can be removed using the argument "
                '`attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                past_key_value,
                output_attentions,
            )

        bsz, tgt_len, _ = hidden_states.size()

        query_layer = self.transpose_for_scores(self.query(hidden_states))

        # If this is instantiated as a cross-attention module, the keys and values come from an encoder; the attention
        # mask needs to be such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        current_states = encoder_hidden_states if is_cross_attention else hidden_states
        attention_mask = encoder_attention_mask if is_cross_attention else attention_mask

        # Check `seq_length` of `past_key_value` == `len(current_states)` to support prefix tuning
        if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
            key_layer, value_layer = past_key_value
        else:
            key_layer = self.transpose_for_scores(self.key(current_states))
            value_layer = self.transpose_for_scores(self.value(current_states))
            if past_key_value is not None and not is_cross_attention:
                key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
                value_layer = torch.cat([past_key_value[1], value_layer], dim=2)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
        # Reference: https://github.com/pytorch/pytorch/issues/112577
        if self.require_contiguous_qkv and query_layer.device.type == "cuda" and attention_mask is not None:
            query_layer = query_layer.contiguous()
            key_layer = key_layer.contiguous()
            value_layer = value_layer.contiguous()

        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
        # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create
        # a causal mask in case tgt_len == 1.
        is_causal = (
            True if self.is_decoder and not is_cross_attention and attention_mask is None and tgt_len > 1 else False
        )

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_layer,
            key_layer,
            value_layer,
            attn_mask=attention_mask,
            dropout_p=self.dropout_prob if self.training else 0.0,
            is_causal=is_causal,
        )

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, tgt_len, self.all_head_size)

        outputs = (attn_output,)
        if self.is_decoder:
            outputs = outputs + (past_key_value,)


SUMMARY:
**Class Summary: DummyBertSdpaSelfAttention**

The `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.

**Primary Purpose**

The primary purpose of this class is to handle non-absolute position embeddings and support torch.compile's dynamic shapes for fast inference.

**Key Implementation Details**

*   Uses `torch.nn.functional.scaled_dot_product_attention` to compute attention outputs.
*   Dispatches to SDPA's Flash Attention or Efficient kernels via an `is_causal` if statement.
*   Applies contouring operations for efficient computation on non-contiguous inputs.
*   Handles cross-attention and prefix tuning support.

**Notable Dependencies and Requirements**

*   Depends on the `DummyBertSelfAttention` class and uses its `forward` method as a base implementation.
*   Requires torch 2.2.0 or later due to fixes in SDPA's Flash Attention backend.

**(External References)**

-   `torch.nn.functional.scaled_dot_product_attention`
-   ` DummyBertSelfAttention` class
-   `config.attention_probs_dropout_prob`

Note: The exact reference to the corresponding Transformers library function mentioned above can be found at :https://github.com/huggingfacetransformers/Transformers


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CODE:

class DummyBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)


SUMMARY:
**Summary**

This 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.

**Key Implementation Details**

1. Linear transformation: `self.dense(nn.Linear(config.hidden_size, config.hidden_size))`
2. Dropout regularization: `self.dropout(config.hidden_dropout_prob)`
3. Layer Normalization: `self.LayerNorm(config.layer_norm_eps)`

**Notable Dependencies and Requirements**

* PyTorch (`torch`)
* Hugging Face Transformer library
* BERT configuration (`config`)


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CODE:

class DummyBertAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = DUMMY_BERT_SELF_ATTENTION_CLASSES[config._attn_implementation](
            config, position_embedding_type=position_embedding_type
        )
        self.output = DummyBertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them


SUMMARY:
**Concise Summary:**

The `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.

**Key Implementation Details:**

*   **Attention Mechanism:** The `prune_heads` method applies attention head pruning based on the provided heads, updating hyperparameters accordingly.
*   **Forward Method:** The `forward` method first computes attention outputs using `self.self` and then passes them through `self.output`.
*   **Notable Dependencies:**

    *   `DummyBertSelfAttention_CLASSES`: A mapping class for BERT attention classes.
    *   `DummyBertSelfOutput`: A module for self-output operation.

**External Dependencies:**

*   `torch` library
*   `find_pruneable_heads_and_indices`, `prune_linear_layer`, and other utility functions in the codebase.


key: chroma:document
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CODE:

class DummyBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)


SUMMARY:
**Summary**

The `DummyBertIntermediate` class inherits from PyTorch's `nn.Module`. It implements the intermediate layer of BERT, comprising:

* A linear dense layer (`self.dense`) with an input size of `config.hidden_size` and output size of `config.intermediate_size`.
* 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`).
* The forward pass applies self.dense to input `hidden_states`, followed by the application of `self.intermediate_act_fn`.

**Key Details**

* Input size: `config.hidden_size`
* Output size: `config(intermediate_size)`
* Activation function:
	+ If string, uses dictionary lookup
	+ Otherwise, uses `config.hidden_act`

**Dependencies & Requirements**

None 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.


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CODE:

class DummyBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)


SUMMARY:
**Summary**

The `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`.

**Primary Purpose**
The 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.

**Key Implementation Details**

*   Linear Transformation (`self.dense`)
    *   Input: `hidden_states` (torch.Tensor)
    *   Output: transformed tensor
*   Dropout Regularization (`self.dropout`)
    *   Input: transformed tensor from linear transformation
    *   Output: regularized tensor after dropout
*   Layer Normalization (`self.LayerNorm`)
    *   Input: combination of output from linear transformation, dropout regularization, and input_tensor
    *   Output: normalized tensor

**Notable Dependencies**

*   `nn.Module` (PyTorch neural network module)
*   `torch.Tensor`
*   `nn.Linear`, `nn.Dropout`, and `nn.LayerNorm` modules ( PyTorch linear and normalization layers)

**No external dependencies listed.**


key: chroma:document
string_value:
CODE:

class DummyBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = DummyBertAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = DummyBertAttention(config, position_embedding_type="absolute")
        self.intermediate = DummyBertIntermediate(config)
        self.output = DummyBertOutput(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]

        # if decoder, the last output is tuple of self-attn cache
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else:
            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"):
                raise ValueError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
                    " by setting `config.add_cross_attention=True`"
                )

            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                cross_attn_past_key_value,
                output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights

            # add cross-attn cache to positions 3,4 of present_key_value tuple
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value

        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
        )
        outputs = (layer_output,) + outputs

        # if decoder, return the attn key/values as the last output
        if self.is_decoder:
            outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)


SUMMARY:
**Class Summary: DummyBertLayer**

The `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.

**Primary Purpose:** To process input data through self-attention and feed-forward networks, producing output features.

**Key Implementation Details:**

*   The `forward` method processes input tensors for self-attention and cross-attention. It returns a tuple of output features.
*   Two critical components are the `DummyBertAttention` and `DummyBertIntermediate` modules, which implement attention mechanisms and feed-forward networks, respectively.

**Notable Dependencies or Requirements:**

*   Uses `torch.Tensor`, `Optional[ torch.FloatTensor]`, and other PyTorch-related classes/types for tensor operations.
*   Incorporates dependency on `DummyBertAttention` and `DummyBertIntermediate` modules (not shown in this code snippet).


