




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A_						=				{'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				['''LayoutXLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

     A_						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase							 =					16
__lowercase							 =					32



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 16  ,			SCREAMING_SNAKE_CASE = "bert-base-cased"	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		load_dataset('''glue'''  ,			'''mrpc'''	)

    def tokenize_function(SCREAMING_SNAKE_CASE	):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase     :int			  =		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
    __UpperCamelCase     :Tuple			  =		datasets.map(
        SCREAMING_SNAKE_CASE  ,			batched=SCREAMING_SNAKE_CASE  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			load_from_cache_file=SCREAMING_SNAKE_CASE	)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __UpperCamelCase     :List[str]			  =		tokenized_datasets.rename_column('''label'''  ,			'''labels'''	)

    def collate_fn(SCREAMING_SNAKE_CASE	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''	)
        return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''longest'''  ,			return_tensors='''pt'''	)

    # Instantiate dataloaders.
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(
        tokenized_datasets['''train''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		DataLoader(
        tokenized_datasets['''validation''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)

    return train_dataloader, eval_dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase     :int			  =		config['''lr''']
    __UpperCamelCase     :str			  =		int(config['''num_epochs''']	)
    __UpperCamelCase     :Any			  =		int(config['''seed''']	)
    __UpperCamelCase     :Dict			  =		int(config['''batch_size''']	)
    __UpperCamelCase     :Optional[Any]			  =		args.model_name_or_path

    set_seed(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		get_dataloaders(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase     :Any			  =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE  ,			return_dict=SCREAMING_SNAKE_CASE	)

    # Instantiate optimizer
    __UpperCamelCase     :List[str]			  =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase     :Optional[Any]			  =		optimizer_cls(params=model.parameters()  ,			lr=SCREAMING_SNAKE_CASE	)

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase     :Dict			  =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __UpperCamelCase     :Dict			  =		1
    __UpperCamelCase     :Tuple			  =		(len(SCREAMING_SNAKE_CASE	) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        __UpperCamelCase     :str			  =		get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE  ,			num_warmup_steps=0  ,			num_training_steps=SCREAMING_SNAKE_CASE  ,			)
    else:
        __UpperCamelCase     :Dict			  =		DummyScheduler(SCREAMING_SNAKE_CASE  ,			total_num_steps=SCREAMING_SNAKE_CASE  ,			warmup_num_steps=0	)

    # 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.
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :int			  =		accelerator.prepare(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase     :List[Any]			  =		0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase     :Dict			  =		0

    # Now we train the model
    __UpperCamelCase     :Any			  =		evaluate.load('''glue'''  ,			'''mrpc'''	)
    __UpperCamelCase     :Union[str, Any]			  =		0
    __UpperCamelCase     :Optional[int]			  =		{}
    for epoch in range(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        model.train()
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :Optional[Any]			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Tuple			  =		outputs.loss
            __UpperCamelCase     :str			  =		loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        __UpperCamelCase     :Any			  =		0
        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():
                __UpperCamelCase     :Any			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		outputs.logits.argmax(dim=-1	)
            # It is slightly faster to call this once, than multiple times
            __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		accelerator.gather(
                (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(SCREAMING_SNAKE_CASE	) - 1:
                    __UpperCamelCase     :List[str]			  =		predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    __UpperCamelCase     :Optional[int]			  =		references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE  ,			references=SCREAMING_SNAKE_CASE  ,			)

        __UpperCamelCase     :Dict			  =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            __UpperCamelCase     :int			  =		eval_metric['''accuracy''']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir  ,			'''all_results.json'''	)  ,			'''w'''	) as f:
            json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
    parser.add_argument(
        '''--model_name_or_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''bert-base-cased'''  ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,			required=SCREAMING_SNAKE_CASE  ,			)
    parser.add_argument(
        '''--output_dir'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
    parser.add_argument(
        '''--performance_lower_bound'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''  ,			)
    parser.add_argument(
        '''--num_epochs'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=3  ,			help='''Number of train epochs.'''  ,			)
    __UpperCamelCase     :List[str]			  =		parser.parse_args()
    __UpperCamelCase     :Tuple			  =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





