





'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase_					= 16
lowercase_					= 32





def lowerCamelCase						(     __lowerCamelCase :    Accelerator					,       __lowerCamelCase :    int = 16    )						->int:
    _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""bert-base-cased"""    )
    _SCREAMING_SNAKE_CASE									=			load_dataset("""glue"""					,       """mrpc"""    )

    def tokenize_function(__lowerCamelCase :    int    ):
        # max_length=None => use the model max length (it's actually the default)
        _SCREAMING_SNAKE_CASE									=			tokenizer(examples["""sentence1"""]					,       examples["""sentence2"""]					,       truncation=__lowerCamelCase					,       max_length=__lowerCamelCase    )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        _SCREAMING_SNAKE_CASE									=			datasets.map(
            __lowerCamelCase					,       batched=__lowerCamelCase					,       remove_columns=["""idx""", """sentence1""", """sentence2"""]					,       )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _SCREAMING_SNAKE_CASE									=			tokenized_datasets.rename_column("""label"""					,       """labels"""    )

    def collate_fn(__lowerCamelCase :    Any    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _SCREAMING_SNAKE_CASE									=			128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            _SCREAMING_SNAKE_CASE									=			16
        elif accelerator.mixed_precision != "no":
            _SCREAMING_SNAKE_CASE									=			8
        else:
            _SCREAMING_SNAKE_CASE									=			None

        return tokenizer.pad(
            __lowerCamelCase					,       padding="""longest"""					,       max_length=__lowerCamelCase					,       pad_to_multiple_of=__lowerCamelCase					,       return_tensors="""pt"""					,       )

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""train"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""validation"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase    )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowercase_					= mocked_dataloaders  # noqa: F811





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Any    )						->Optional[Any]:
    # For testing only
    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,       __lowerCamelCase    ) == "1":
        _SCREAMING_SNAKE_CASE									=			2
    # Initialize accelerator
    _SCREAMING_SNAKE_CASE									=			Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _SCREAMING_SNAKE_CASE									=			config["""lr"""]
    _SCREAMING_SNAKE_CASE									=			int(config["""num_epochs"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""seed"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""batch_size"""]    )

    _SCREAMING_SNAKE_CASE									=			evaluate.load("""glue"""					,       """mrpc"""    )

    # If the batch size is too big we use gradient accumulation
    _SCREAMING_SNAKE_CASE									=			1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _SCREAMING_SNAKE_CASE									=			batch_size // MAX_GPU_BATCH_SIZE
        _SCREAMING_SNAKE_CASE									=			MAX_GPU_BATCH_SIZE

    set_seed(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			get_dataloaders(__lowerCamelCase					,       __lowerCamelCase    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _SCREAMING_SNAKE_CASE									=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,       return_dict=__lowerCamelCase    )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _SCREAMING_SNAKE_CASE									=			model.to(accelerator.device    )

    # Instantiate optimizer
    _SCREAMING_SNAKE_CASE									=			AdamW(params=model.parameters()					,       lr=__lowerCamelCase    )

    # Instantiate scheduler
    _SCREAMING_SNAKE_CASE									=			get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase					,       num_warmup_steps=100					,       num_training_steps=(len(__lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps					,       )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    # Now we train the model
    for epoch in range(__lowerCamelCase    ):
        model.train()
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			outputs.loss
            _SCREAMING_SNAKE_CASE									=			loss / gradient_accumulation_steps
            accelerator.backward(__lowerCamelCase    )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        _SCREAMING_SNAKE_CASE									=			0
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			outputs.logits.argmax(dim=-1    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.gather((predictions, batch["""labels"""])    )
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(__lowerCamelCase    ) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    _SCREAMING_SNAKE_CASE									=			predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                    _SCREAMING_SNAKE_CASE									=			references[: len(eval_dataloader.dataset    ) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=__lowerCamelCase					,       references=__lowerCamelCase					,       )

        _SCREAMING_SNAKE_CASE									=			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'					,       __lowerCamelCase    )





def lowerCamelCase						(     )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			argparse.ArgumentParser(description="""Simple example of training script."""    )
    parser.add_argument(
        """--mixed_precision"""					,       type=__lowerCamelCase					,       default=__lowerCamelCase					,       choices=["""no""", """fp16""", """bf16""", """fp8"""]					,       help="""Whether to use mixed precision. Choose"""
        """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
        """and an Nvidia Ampere GPU."""					,       )
    parser.add_argument("""--cpu"""					,       action="""store_true"""					,       help="""If passed, will train on the CPU."""    )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


if __name__ == "__main__":
      main()








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




