




"""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_					:     List[Any]					    =  16
a_					:     Dict					    =  32





def     UpperCAmelCase					(      A__:     Dict				,					A__:     str = 16				,					A__:     Tuple = "bert-base-cased"  )	->  int:
			__lowerCamelCase       :						List[str]													=					AutoTokenizer.from_pretrained(snake_case__  )
			__lowerCamelCase       :						Union[str, Any]													=					load_dataset('glue'				,					'mrpc'  )

			def tokenize_function(A__:     Tuple  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCamelCase       :						Dict													=					tokenizer(examples['sentence1']				,					examples['sentence2']				,					truncation=snake_case__				,					max_length=snake_case__  )
						return outputs

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

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

			def collate_fn(A__:     Optional[int]  ):
						# 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(snake_case__				,					padding='max_length'				,					max_length=128				,					return_tensors='pt'  )
						return tokenizer.pad(snake_case__				,					padding='longest'				,					return_tensors='pt'  )

			# Instantiate dataloaders.
			__lowerCamelCase       :						Optional[int]													=					DataLoader(
			    tokenized_datasets['train']				,					shuffle=snake_case__				,					collate_fn=snake_case__				,					batch_size=snake_case__  )
			__lowerCamelCase       :						int													=					DataLoader(
			    tokenized_datasets['validation']				,					shuffle=snake_case__				,					collate_fn=snake_case__				,					batch_size=snake_case__  )

			return train_dataloader, eval_dataloader





def     UpperCAmelCase					(      A__:     Optional[int]				,					A__:     Optional[int]  )	->  str:
			__lowerCamelCase       :						Optional[Any]													=					Accelerator()

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

			set_seed(snake_case__  )
			__lowerCamelCase       :						List[str]													=					get_dataloaders(snake_case__				,					snake_case__				,					snake_case__  )

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

			# Instantiate optimizer
			__lowerCamelCase       :						str													=					(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			__lowerCamelCase       :						Dict													=					optimizer_cls(params=model.parameters()				,					lr=snake_case__  )

			if accelerator.state.deepspeed_plugin is not None:
						__lowerCamelCase       :						int													=					accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						__lowerCamelCase       :						str													=					1
			__lowerCamelCase       :						str													=					(len(snake_case__  ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						__lowerCamelCase       :						Tuple													=					get_linear_schedule_with_warmup(
						    optimizer=snake_case__				,					num_warmup_steps=0				,					num_training_steps=snake_case__				,					)
			else:
						__lowerCamelCase       :						Union[str, Any]													=					DummyScheduler(snake_case__				,					total_num_steps=snake_case__				,					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.
			__lowerCamelCase       :						Union[str, Any]													=					accelerator.prepare(
			    snake_case__				,					snake_case__				,					snake_case__				,					snake_case__				,					snake_case__  )

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

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

									overall_step += 1

						model.eval()
						__lowerCamelCase       :						Tuple													=					0
						for step, batch in enumerate(snake_case__  ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device  )
									with torch.no_grad():
												__lowerCamelCase       :						Optional[int]													=					model(**snake_case__  )
									__lowerCamelCase       :						str													=					outputs.logits.argmax(dim=-1  )
									# It is slightly faster to call this once, than multiple times
									__lowerCamelCase       :						Optional[int]													=					accelerator.gather(
									    (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(snake_case__  ) - 1:
															__lowerCamelCase       :						int													=					predictions[: len(eval_dataloader.dataset  ) - samples_seen]
															__lowerCamelCase       :						Tuple													=					references[: len(eval_dataloader.dataset  ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=snake_case__				,					references=snake_case__				,					)

						__lowerCamelCase       :						List[str]													=					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''				,					snake_case__  )
						__lowerCamelCase       :						str													=					eval_metric['''accuracy''']

						if best_performance < eval_metric["accuracy"]:
									__lowerCamelCase       :						List[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(snake_case__				,					snake_case__  )





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


if __name__ == "__main__":
					main()





'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




