"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  16
SCREAMING_SNAKE_CASE__      						=  32





def    lowerCAmelCase__    (			_UpperCamelCase       :				Accelerator ,			_UpperCamelCase       :				int = 1_6 ,			_UpperCamelCase       :				str = "bert-base-cased"					)					->				Any:





						"""simple docstring"""






						snake_case								=			AutoTokenizer.from_pretrained(a_					)
						snake_case								=			load_dataset('glue' ,			'mrpc'					)

						def tokenize_function(_UpperCamelCase       :				List[Any]					):
												# max_length=None => use the model max length (it's actually the default)
												snake_case								=			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
						snake_case								=			datasets.map(
						    a_ ,			batched=a_ ,			remove_columns=['idx', 'sentence1', 'sentence2'] ,			load_from_cache_file=a_					)

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

						def collate_fn(_UpperCamelCase       :				Dict					):
												# 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(a_ ,			padding='max_length' ,			max_length=1_2_8 ,			return_tensors='pt'					)
												return tokenizer.pad(a_ ,			padding='longest' ,			return_tensors='pt'					)

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

						return train_dataloader, eval_dataloader





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Union[str, Any]					)					->				List[Any]:





						"""simple docstring"""






						snake_case								=			Accelerator()

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

						set_seed(a_					)
						snake_case								=			get_dataloaders(a_ ,			a_ ,			a_					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case								=			AutoModelForSequenceClassification.from_pretrained(a_ ,			return_dict=a_					)

						# Instantiate optimizer
						snake_case								=			(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						snake_case								=			optimizer_cls(params=model.parameters() ,			lr=a_					)

						if accelerator.state.deepspeed_plugin is not None:
												snake_case								=			accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												snake_case								=			1
						snake_case								=			(len(a_					) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												snake_case								=			get_linear_schedule_with_warmup(
												    optimizer=a_ ,			num_warmup_steps=0 ,			num_training_steps=a_ ,			)
						else:
												snake_case								=			DummyScheduler(a_ ,			total_num_steps=a_ ,			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.
						snake_case								=			accelerator.prepare(
						    a_ ,			a_ ,			a_ ,			a_ ,			a_					)

						# We need to keep track of how many total steps we have iterated over
						snake_case								=			0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case								=			0

						# Now we train the model
						snake_case								=			evaluate.load('glue' ,			'mrpc'					)
						snake_case								=			0
						snake_case								=			{}
						for epoch in range(a_ ,			a_					):
												model.train()
												for step, batch in enumerate(a_					):
																		snake_case								=			model(**a_					)
																		snake_case								=			outputs.loss
																		snake_case								=			loss / gradient_accumulation_steps
																		accelerator.backward(a_					)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

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

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

												if best_performance < eval_metric["accuracy"]:
																		snake_case								=			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(a_ ,			a_					)





def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
						parser.add_argument(
						    '--model_name_or_path' ,			type=a_ ,			default='bert-base-cased' ,			help='Path to pretrained model or model identifier from huggingface.co/models.' ,			required=a_ ,			)
						parser.add_argument(
						    '--output_dir' ,			type=a_ ,			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=a_ ,			default=a_ ,			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=a_ ,			default=3 ,			help='Number of train epochs.' ,			)
						snake_case								=			parser.parse_args()
						snake_case								=			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
						training_function(a_ ,			a_					)


if __name__ == "__main__":
					main()
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=None					)					->				Dict:





						"""simple docstring"""






						if expected_checksums is None:
												logger.info('Unable to verify checksums.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' for ' + verification_name if verification_name is not None else ''
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingChecksumError(
												    f"""Checksums didn't match{for_verification_name}:\n"""
												    f"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'					)
						logger.info('All the checksums matched successfully' + for_verification_name					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""simple docstring"""






						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingSplitsSizesError(str(_UpperCamelCase					)					)
						logger.info('All the splits matched successfully.'					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False


