


'''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


__lowercase     :						Optional[Any]	=     16
__lowercase     :						Dict	=     32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int] = 16						,				_SCREAMING_SNAKE_CASE				:       List[Any] = "bert-base-cased"       ):
					__a      :	Union[str, Any]    =  AutoTokenizer.from_pretrained(A_       )
					__a      :	Union[str, Any]    =  load_dataset('glue'						,				'mrpc'       )

					def tokenize_function(_SCREAMING_SNAKE_CASE				:       str       ):
										# max_length=None => use the model max length (it's actually the default)
										__a      :	Optional[int]    =  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
					__a      :	List[Any]    =  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
					__a      :	Union[str, Any]    =  tokenized_datasets.rename_column('label'						,				'labels'       )

					def collate_fn(_SCREAMING_SNAKE_CASE				:       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=128						,				return_tensors='pt'       )
										return tokenizer.pad(A_						,				padding='longest'						,				return_tensors='pt'       )

					# Instantiate dataloaders.
					__a      :	Optional[Any]    =  DataLoader(
					    tokenized_datasets['train']						,				shuffle=A_						,				collate_fn=A_						,				batch_size=A_       )
					__a      :	Tuple    =  DataLoader(
					    tokenized_datasets['validation']						,				shuffle=A_						,				collate_fn=A_						,				batch_size=A_       )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Any       ):
					# Initialize accelerator
					__a      :	Optional[Any]    =  Accelerator()

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

					set_seed(A_       )
					__a      :	Optional[int]    =  get_dataloaders(A_						,				A_						,				A_       )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a      :	List[Any]    =  AutoModelForSequenceClassification.from_pretrained(A_						,				return_dict=A_       )

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

					if accelerator.state.deepspeed_plugin is not None:
										__a      :	Any    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										__a      :	Any    =  1
					__a      :	str    =  (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
					):
										__a      :	Dict    =  get_linear_schedule_with_warmup(
										    optimizer=A_						,				num_warmup_steps=0						,				num_training_steps=A_						,				)
					else:
										__a      :	Any    =  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.
					__a      :	int    =  accelerator.prepare(
					    A_						,				A_						,				A_						,				A_						,				A_       )

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

					# Now we train the model
					__a      :	Tuple    =  evaluate.load('glue'						,				'mrpc'       )
					__a      :	Dict    =  0
					__a      :	Any    =  {}
					for epoch in range(A_						,				A_       ):
										model.train()
										for step, batch in enumerate(A_       ):
															__a      :	Optional[Any]    =  model(**A_       )
															__a      :	List[Any]    =  outputs.loss
															__a      :	str    =  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()
										__a      :	Union[str, Any]    =  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():
																				__a      :	int    =  model(**A_       )
															__a      :	int    =  outputs.logits.argmax(dim=-1       )
															# It is slightly faster to call this once, than multiple times
															__a      :	Optional[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(A_       ) - 1:
																									__a      :	List[Any]    =  predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																									__a      :	Dict    =  references[: len(eval_dataloader.dataset       ) - samples_seen]
																				else:
																									samples_seen += references.shape[0]
															metric.add_batch(
															    predictions=A_						,				references=A_						,				)

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

										if best_performance < eval_metric["accuracy"]:
															__a      :	str    =  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							():
					__a      :	Tuple    =  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.'						,				)
					__a      :	int    =  parser.parse_args()
					__a      :	Any    =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
					training_function(A_						,				A_       )


if __name__ == "__main__":
			main()







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()



