


'''simple docstring'''



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


_lowercase    :    Optional[Any]			     =  logging.getLogger(__name__)


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






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







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "The input training data file (a text file)."} )
				__magic_name__       :   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"
				        )
				    }		,       )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}		,       )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}		,       )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}		,       )
				__magic_name__       :   bool							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}		,       )

				__magic_name__       :   bool							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Train with masked-language modeling loss instead of language modeling."} )
				__magic_name__       :   bool							= field(default=lowerCAmelCase		,       metadata={"help": "Whether ot not to use whole word mask."} )
				__magic_name__       :   float							= field(
				    default=0.1_5		,       metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} )
				__magic_name__       :   float							= field(
				    default=1 / 6		,       metadata={
				        "help": (
				            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
				            " modeling."
				        )
				    }		,       )
				__magic_name__       :   int							= field(
				    default=5		,       metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."} )

				__magic_name__       :   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)."
				        )
				    }		,       )
				__magic_name__       :   bool							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Overwrite the cached training and evaluation sets"} )







def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    str   ,     A     :    Tuple = False   ,     A     :    Optional[int] = None   ,     ):




						'''simple docstring'''






						def _dataset(A     :    List[str]   ,     A     :    Any=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    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
						UpperCAmelCase         =			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:
												UpperCAmelCase         =			AutoConfig.from_pretrained(model_args.config_name   ,     cache_dir=model_args.cache_dir  )
						elif model_args.model_name_or_path:
												UpperCAmelCase         =			AutoConfig.from_pretrained(model_args.model_name_or_path   ,     cache_dir=model_args.cache_dir  )
						else:
												UpperCAmelCase         =			CONFIG_MAPPING[model_args.model_type]()
												logger.warning('''You are instantiating a new config instance from scratch.'''  )

						if model_args.tokenizer_name:
												UpperCAmelCase         =			AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,     cache_dir=model_args.cache_dir  )
						elif model_args.model_name_or_path:
												UpperCAmelCase         =			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:
												UpperCAmelCase         =			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'''  )
												UpperCAmelCase         =			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:
												UpperCAmelCase         =			tokenizer.max_len
												# Our input block size will be the max possible for the model
						else:
												UpperCAmelCase         =			min(data_args.block_size   ,     tokenizer.max_len  )

						# Get datasets

						UpperCAmelCase         =			(
						    get_dataset(lowerCamelCase__   ,     tokenizer=lowerCamelCase__   ,     cache_dir=model_args.cache_dir  ) if training_args.do_train else None
						)
						UpperCAmelCase         =			(
						    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":
												UpperCAmelCase         =			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:
																		UpperCAmelCase         =			DataCollatorForWholeWordMask(
																		    tokenizer=lowerCamelCase__   ,     mlm_probability=data_args.mlm_probability  )
												else:
																		UpperCAmelCase         =			DataCollatorForLanguageModeling(
																		    tokenizer=lowerCamelCase__   ,     mlm=data_args.mlm   ,     mlm_probability=data_args.mlm_probability  )

    # Initialize our Trainer
						UpperCAmelCase         =			Trainer(
						    model=lowerCamelCase__   ,     args=lowerCamelCase__   ,     data_collator=lowerCamelCase__   ,     train_dataset=lowerCamelCase__   ,     eval_dataset=lowerCamelCase__   ,     prediction_loss_only=lowerCamelCase__   ,     )

						# Training
						if training_args.do_train:
												UpperCAmelCase         =			(
												    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
						UpperCAmelCase         =			{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''  )

												UpperCAmelCase         =			trainer.evaluate()

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

												UpperCAmelCase         =			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    lowerCamelCase__   ( A     :    int  ):




						'''simple docstring'''






						main()


if __name__ == "__main__":
	main()









'''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 the experiment tracking capability,
# 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    :    Optional[Any]			     =  16
_lowercase    :    Dict			     =  32


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    int = 16  ):




						'''simple docstring'''






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

						def tokenize_function(A     :    int  ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase         =			tokenizer(examples['''sentence1''']   ,     examples['''sentence2''']   ,     truncation=A   ,     max_length=A  )
												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():
												UpperCAmelCase         =			datasets.map(
												    A   ,     batched=A   ,     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
						UpperCAmelCase         =			tokenized_datasets.rename_column('''label'''   ,     '''labels'''  )

						def collate_fn(A     :    List[Any]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase         =			1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase         =			16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase         =			8
												else:
																		UpperCAmelCase         =			None

												return tokenizer.pad(
												    A   ,     padding='''longest'''   ,     max_length=A   ,     pad_to_multiple_of=A   ,     return_tensors='''pt'''   ,     )

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

						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    :    Union[str, Any]			     =  mocked_dataloaders  # noqa: F811


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,     A  ) == "1":
												UpperCAmelCase         =			2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												UpperCAmelCase         =			Accelerator(
												    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with='''all'''   ,     project_dir=args.project_dir  )
						else:
												UpperCAmelCase         =			Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision  )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase         =			config['''lr''']
						UpperCAmelCase         =			int(config['''num_epochs''']  )
						UpperCAmelCase         =			int(config['''seed''']  )
						UpperCAmelCase         =			int(config['''batch_size''']  )
						set_seed(A  )

						UpperCAmelCase       ,       UpperCAmelCase         =			get_dataloaders(A   ,     A  )
						UpperCAmelCase         =			evaluate.load('''glue'''   ,     '''mrpc'''  )

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

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase         =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,     return_dict=A  )

						# 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).
						UpperCAmelCase         =			model.to(accelerator.device  )

						# Instantiate optimizer
						UpperCAmelCase         =			AdamW(params=model.parameters()   ,     lr=A  )

						# Instantiate scheduler
						UpperCAmelCase         =			get_linear_schedule_with_warmup(
						    optimizer=A   ,     num_warmup_steps=1_00   ,     num_training_steps=(len(A  ) * 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.
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			accelerator.prepare(
						    A   ,     A   ,     A   ,     A   ,     A  )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												UpperCAmelCase         =			os.path.split(A  )[-1].split('''.'''  )[0]
												accelerator.init_trackers(A   ,     A  )

						# Now we train the model
						for epoch in range(A  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		UpperCAmelCase         =			0
												for step, batch in enumerate(A  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device  )
																		UpperCAmelCase         =			model(**A  )
																		UpperCAmelCase         =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		UpperCAmelCase         =			loss / gradient_accumulation_steps
																		accelerator.backward(A  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(A  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device  )
																		with torch.no_grad():
																								UpperCAmelCase         =			model(**A  )
																		UpperCAmelCase         =			outputs.logits.argmax(dim=-1  )
																		UpperCAmelCase       ,       UpperCAmelCase         =			accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
																		metric.add_batch(
																		    predictions=A   ,     references=A   ,     )

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

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(A  ),
																		        '''epoch''': epoch,
																		    }   ,     step=A   ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script.'''  )
						parser.add_argument(
						    '''--mixed_precision'''   ,     type=A   ,     default=A   ,     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.'''  )
						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.'''   ,     )
						parser.add_argument(
						    '''--project_dir'''   ,     type=A   ,     default='''logs'''   ,     help='''Location on where to store experiment tracking logs` and relevent project information'''   ,     )
						UpperCAmelCase         =			parser.parse_args()
						UpperCAmelCase         =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A   ,     A  )


if __name__ == "__main__":
	main()





