

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


UpperCamelCase__  :      Tuple			=    16
UpperCamelCase__  :      List[str]			=    32


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Accelerator ,   _SCREAMING_SNAKE_CASE					:		int = 16       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						AutoTokenizer.from_pretrained('bert-base-cased'       )
    SCREAMING_SNAKE_CASE_									=						load_dataset('glue' ,   'mrpc'       )

    def tokenize_function(_SCREAMING_SNAKE_CASE					:		Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_									=						tokenizer(examples['sentence1'] ,   examples['sentence2'] ,   truncation=_SCREAMING_SNAKE_CASE ,   max_length=_SCREAMING_SNAKE_CASE       )
        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(
            _SCREAMING_SNAKE_CASE ,   batched=_SCREAMING_SNAKE_CASE ,   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(_SCREAMING_SNAKE_CASE					:		str       ):
        # 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(
            _SCREAMING_SNAKE_CASE ,   padding='longest' ,   max_length=_SCREAMING_SNAKE_CASE ,   pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,   return_tensors='pt' ,   )

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

    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

       UpperCamelCase__  :      Tuple			=    mocked_dataloaders  # noqa: F811


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    if os.environ.get('TESTING_MOCKED_DATALOADERS' ,   _SCREAMING_SNAKE_CASE       ) == "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'       )

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=_SCREAMING_SNAKE_CASE       )
    def inner_training_loop(_SCREAMING_SNAKE_CASE					:		Any       ):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(_SCREAMING_SNAKE_CASE       )

        # 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=_SCREAMING_SNAKE_CASE       )

        # 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=_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						get_dataloaders(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

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

        # 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(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

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

            model.eval()
            for step, batch in enumerate(_SCREAMING_SNAKE_CASE       ):
                # 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(**_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						outputs.logits.argmax(dim=-1       )
                SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						accelerator.gather_for_metrics((predictions, batch['labels'])       )
                metric.add_batch(
                    predictions=_SCREAMING_SNAKE_CASE ,   references=_SCREAMING_SNAKE_CASE ,   )

            SCREAMING_SNAKE_CASE_									=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:""" ,   _SCREAMING_SNAKE_CASE       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						argparse.ArgumentParser(description='Simple example of training script.'       )
    parser.add_argument(
        '--mixed_precision' ,   type=_SCREAMING_SNAKE_CASE ,   default=_SCREAMING_SNAKE_CASE ,   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(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

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


