





'''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


A   : int             =			16
A   : Optional[int]             =			32





def       lowercase_      (		lowercase__				,  lowercase__ = 16				,  lowercase__ = "bert-base-cased"						)   ->Optional[int]:
						_snake_case:   int  					=		AutoTokenizer.from_pretrained(UpperCamelCase__						)
						_snake_case:   List[str]  					=		load_dataset('glue'				,  'mrpc'						)

						def tokenize_function(lowercase__						):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case:   str  					=		tokenizer(examples['sentence1']				,  examples['sentence2']				,  truncation=UpperCamelCase__				,  max_length=UpperCamelCase__						)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						_snake_case:   List[Any]  					=		datasets.map(
						    UpperCamelCase__				,  batched=UpperCamelCase__				,  remove_columns=['idx', 'sentence1', 'sentence2']				,  load_from_cache_file=UpperCamelCase__						)

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

						def collate_fn(lowercase__						):
												# 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(UpperCamelCase__				,  padding='max_length'				,  max_length=128				,  return_tensors='pt'						)
												return tokenizer.pad(UpperCamelCase__				,  padding='longest'				,  return_tensors='pt'						)

						# Instantiate dataloaders.
						_snake_case:   List[Any]  					=		DataLoader(
						    tokenized_datasets['train']				,  shuffle=UpperCamelCase__				,  collate_fn=UpperCamelCase__				,  batch_size=UpperCamelCase__						)
						_snake_case:   Optional[Any]  					=		DataLoader(
						    tokenized_datasets['validation']				,  shuffle=UpperCamelCase__				,  collate_fn=UpperCamelCase__				,  batch_size=UpperCamelCase__						)

						return train_dataloader, eval_dataloader





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Optional[Any]:
						_snake_case:   int  					=		Accelerator()

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

						set_seed(UpperCamelCase__						)
						_snake_case					,						_snake_case:   Optional[Any]  					=		get_dataloaders(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__						)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_snake_case:   Optional[int]  					=		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__				,  return_dict=UpperCamelCase__						)

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

						if accelerator.state.deepspeed_plugin is not None:
												_snake_case:   Optional[int]  					=		accelerator.state.deepspeed_plugin.deepspeed_config[
												    'gradient_accumulation_steps'
												]
						else:
												_snake_case:   Optional[int]  					=		1
						_snake_case:   Optional[Any]  					=		(len(UpperCamelCase__						) * 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:   str  					=		get_linear_schedule_with_warmup(
												    optimizer=UpperCamelCase__				,  num_warmup_steps=0				,  num_training_steps=UpperCamelCase__				,  )
						else:
												_snake_case:   List[str]  					=		DummyScheduler(UpperCamelCase__				,  total_num_steps=UpperCamelCase__				,  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					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case:   int  					=		accelerator.prepare(
						    UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__						)

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

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

																		overall_step += 1

												model.eval()
												_snake_case:   str  					=		0
												for step, batch in enumerate(UpperCamelCase__						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								_snake_case:   Optional[int]  					=		model(**UpperCamelCase__						)
																		_snake_case:   Union[str, Any]  					=		outputs.logits.argmax(dim=-1						)
																		# It is slightly faster to call this once, than multiple times
																		_snake_case					,						_snake_case:   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(UpperCamelCase__						) - 1:
																														_snake_case:   Union[str, Any]  					=		predictions[: len(eval_dataloader.dataset						) - samples_seen]
																														_snake_case:   str  					=		references[: len(eval_dataloader.dataset						) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=UpperCamelCase__				,  references=UpperCamelCase__				,  )

												_snake_case:   Tuple  					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''				,  UpperCamelCase__						)
												_snake_case:   Optional[Any]  					=		eval_metric['accuracy']

												if best_performance < eval_metric["accuracy"]:
																		_snake_case:   Optional[Any]  					=		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(UpperCamelCase__				,  UpperCamelCase__						)





def       lowercase_      (		)   ->Optional[int]:
						_snake_case:   Any  					=		argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
						parser.add_argument(
						    '--model_name_or_path'				,  type=UpperCamelCase__				,  default='bert-base-cased'				,  help='Path to pretrained model or model identifier from huggingface.co/models.'				,  required=UpperCamelCase__				,  )
						parser.add_argument(
						    '--output_dir'				,  type=UpperCamelCase__				,  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=UpperCamelCase__				,  default=UpperCamelCase__				,  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=UpperCamelCase__				,  default=3				,  help='Number of train epochs.'				,  )
						_snake_case:   Dict  					=		parser.parse_args()
						_snake_case:   int  					=		{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
						training_function(UpperCamelCase__				,  UpperCamelCase__						)


if __name__ == "__main__":
		main()












'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextFeatureExtractor"
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextTokenizer"


					def __init__(					self	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											super().__init__(__snake_case       ,     __snake_case       )
											_snake_case:   Optional[int]  					=		self.feature_extractor
											_snake_case:   Dict  					=		False


					def __call__(					self	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											if self._in_target_context_manager:
																	return self.current_processor(*__snake_case       ,     **__snake_case       )

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																	_snake_case:   int  					=		kwargs.pop('raw_speech'       )
											else:
																	_snake_case:   Tuple  					=		kwargs.pop('audio'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('sampling_rate'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('text'       ,     __snake_case       )
											if len(__snake_case       ) > 0:
																	_snake_case:   Optional[Any]  					=		args[0]
																	_snake_case:   Union[str, Any]  					=		args[1:]

											if audio is None and text is None:
																	raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

											if audio is not None:
																	_snake_case:   List[Any]  					=		self.feature_extractor(__snake_case       ,     *__snake_case       ,     sampling_rate=__snake_case       ,     **__snake_case       )
											if text is not None:
																	_snake_case:   Tuple  					=		self.tokenizer(__snake_case       ,     **__snake_case       )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	_snake_case:   List[Any]  					=		encodings['input_ids']
																	return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  Optional[int]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											return self.tokenizer.batch_decode(*__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  List[str]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return self.tokenizer.decode(*__snake_case       ,     **__snake_case       )




					@contextmanager
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'       )
											_snake_case:   Any  					=		True
											_snake_case:   str  					=		self.tokenizer
											yield
											_snake_case:   Union[str, Any]  					=		self.feature_extractor
											_snake_case:   Tuple  					=		False





