






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
#
########################################################################


__UpperCamelCase      :      Optional[Any]     						=						16
__UpperCamelCase      :      int     						=						32





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = 16					):




	'''simple docstring'''







	__lowercase             =  AutoTokenizer.from_pretrained("""bert-base-cased"""					)
	__lowercase             =  load_dataset("""glue"""				,  """mrpc"""					)

	def tokenize_function(lowerCamelCase					):
		# max_length=None => use the model max length (it's actually the default)
		__lowercase             =  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():
		__lowercase             =  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
	__lowercase             =  tokenized_datasets.rename_column("""label"""				,  """labels"""					)

	def collate_fn(lowerCamelCase					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowercase             =  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":
			__lowercase             =  16
		elif accelerator.mixed_precision != "no":
			__lowercase             =  8
		else:
			__lowercase             =  None

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

	# Instantiate dataloaders.
	__lowercase             =  DataLoader(
	    tokenized_datasets["""train"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)
	__lowercase             =  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

						__UpperCamelCase      :      List[str]     						=						mocked_dataloaders  # noqa: F811





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

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

	set_seed(lowerCamelCase					)
	__lowercase       ,						__lowercase             =  get_dataloaders(lowerCamelCase				,  lowerCamelCase					)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase             =  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).
	__lowercase             =  model.to(accelerator.device					)

	# Instantiate optimizer
	__lowercase             =  AdamW(params=model.parameters()				,  lr=lowerCamelCase					)

	# Instantiate scheduler
	__lowercase             =  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.
	__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  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					)
			__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.loss
			__lowercase             =  loss / gradient_accumulation_steps
			accelerator.backward(lowerCamelCase					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		__lowercase             =  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():
				__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.logits.argmax(dim=-1					)
			__lowercase       ,						__lowercase             =  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
					__lowercase             =  predictions[: len(eval_dataloader.dataset					) - samples_seen]
					__lowercase             =  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				,  )

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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  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."""					)
	__lowercase             =  parser.parse_args()
	__lowercase             =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						main()









import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase      :      List[Any]     						=						logging.getLogger(__name__)


__UpperCamelCase      :      Optional[Any]     						=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase      :      List[str]     						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'The model checkpoint for weights initialization. Leave None if you want to train a model from'
	            ' scratch.'
	        )
	    }   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase   )}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,			)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The input training data file (a text file).'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'The input training data files (multiple files in glob format). '
	            'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
	        )
	    }   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}   ,			)

	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'}   )
	__snake_case	:bool   =       field(default=_lowerCAmelCase   ,			metadata={'help': 'Whether ot not to use whole word mask.'}   )
	__snake_case	:float   =       field(
	    default=0.15   ,			metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}   )
	__snake_case	:float   =       field(
	    default=1 / 6   ,			metadata={
	        'help': (
	            'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
	            ' modeling.'
	        )
	    }   ,			)
	__snake_case	:int   =       field(
	    default=5   ,			metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'}   )

	__snake_case	:int   =       field(
	    default=-1   ,			metadata={
	        '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).'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False				,  lowerCamelCase = None				,  ):




	'''simple docstring'''







	def _dataset(lowerCamelCase				,  lowerCamelCase=None					):
		if args.line_by_line:
			if ref_path is not None:
				if not args.whole_word_mask or not args.mlm:
					raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask"""					)
				return LineByLineWithRefDataset(
				    tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size				,  ref_path=lowerCamelCase				,  )

			return LineByLineTextDataset(tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size					)
		else:
			return TextDataset(
			    tokenizer=lowerCamelCase				,  file_path=lowerCamelCase				,  block_size=args.block_size				,  overwrite_cache=args.overwrite_cache				,  cache_dir=lowerCamelCase				,  )

	if evaluate:
		return _dataset(args.eval_data_file				,  args.eval_ref_file					)
	elif args.train_data_files:
		return ConcatDataset([_dataset(lowerCamelCase					) for f in glob(args.train_data_files					)]					)
	else:
		return _dataset(args.train_data_file				,  args.train_ref_file					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_args_into_dataclasses()

	if data_args.eval_data_file is None and training_args.do_eval:
		raise ValueError(
		    """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
		    """or remove the --do_eval argument."""					)
	if (
	    os.path.exists(training_args.output_dir					)
	    and os.listdir(training_args.output_dir					)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
		    """ --overwrite_output_dir to overcome."""					)

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,  datefmt="""%m/%d/%Y %H:%M:%S"""				,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,  )
	logger.warning(
	    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,  training_args.local_rank				,  training_args.device				,  training_args.n_gpu				,  bool(training_args.local_rank != -1					)				,  training_args.fpaa				,  )
	# 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()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("""Training/evaluation parameters %s"""				,  lowerCamelCase					)

	# Set seed
	set_seed(training_args.seed					)

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

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

	if model_args.tokenizer_name:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.tokenizer_name				,  cache_dir=model_args.cache_dir					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.model_name_or_path				,  cache_dir=model_args.cache_dir					)
	else:
		raise ValueError(
		    """You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
		    """ script, save it,and load it from here, using --tokenizer_name"""					)

	if model_args.model_name_or_path:
		__lowercase             =  AutoModelWithLMHead.from_pretrained(
		    model_args.model_name_or_path				,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)				,  config=lowerCamelCase				,  cache_dir=model_args.cache_dir				,  )
	else:
		logger.info("""Training new model from scratch"""					)
		__lowercase             =  AutoModelWithLMHead.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

	if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
		raise ValueError(
		    """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
		    """--mlm flag (masked language modeling)."""					)

	if data_args.block_size <= 0:
		__lowercase             =  tokenizer.max_len
		# Our input block size will be the max possible for the model
	else:
		__lowercase             =  min(data_args.block_size				,  tokenizer.max_len					)

	# Get datasets

	__lowercase             =  (
	    get_dataset(lowerCamelCase				,  tokenizer=lowerCamelCase				,  cache_dir=model_args.cache_dir					) if training_args.do_train else None
	)
	__lowercase             =  (
	    get_dataset(lowerCamelCase				,  tokenizer=lowerCamelCase				,  evaluate=lowerCamelCase				,  cache_dir=model_args.cache_dir					)
	    if training_args.do_eval
	    else None
	)
	if config.model_type == "xlnet":
		__lowercase             =  DataCollatorForPermutationLanguageModeling(
		    tokenizer=lowerCamelCase				,  plm_probability=data_args.plm_probability				,  max_span_length=data_args.max_span_length				,  )
	else:
		if data_args.mlm and data_args.whole_word_mask:
			__lowercase             =  DataCollatorForWholeWordMask(
			    tokenizer=lowerCamelCase				,  mlm_probability=data_args.mlm_probability					)
		else:
			__lowercase             =  DataCollatorForLanguageModeling(
			    tokenizer=lowerCamelCase				,  mlm=data_args.mlm				,  mlm_probability=data_args.mlm_probability					)

    # Initialize our Trainer
	__lowercase             =  Trainer(
	    model=lowerCamelCase				,  args=lowerCamelCase				,  data_collator=lowerCamelCase				,  train_dataset=lowerCamelCase				,  eval_dataset=lowerCamelCase				,  prediction_loss_only=lowerCamelCase				,  )

	# Training
	if training_args.do_train:
		__lowercase             =  (
		    model_args.model_name_or_path
		    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					)
		    else None
		)
		trainer.train(model_path=lowerCamelCase					)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
	__lowercase             =  {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""					)

		__lowercase             =  trainer.evaluate()

		__lowercase             =  math.exp(eval_output["""eval_loss"""]					)
		__lowercase             =  {"""perplexity""": perplexity}

		__lowercase             =  os.path.join(training_args.output_dir				,  """eval_results_lm.txt"""					)
		if trainer.is_world_master():
			with open(lowerCamelCase				,  """w"""					) as writer:
				logger.info("""***** Eval results *****"""					)
				for key in sorted(result.keys()					):
					logger.info("""  %s = %s"""				,  lowerCamelCase				,  str(result[key]					)					)
					writer.write("""%s = %s\n""" % (key, str(result[key]					))					)

		results.update(lowerCamelCase					)

	return results





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	main()


if __name__ == "__main__":
						main()

