





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				:    Tuple       =   16
__A				:    Tuple       =   32





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ = 16			,			UpperCamelCase__ = "bert-base-cased" )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				AutoTokenizer.from_pretrained(UpperCAmelCase_ )
			UpperCAmelCase   		=				load_dataset('''glue'''			,			'''mrpc''' )

			def tokenize_function(UpperCamelCase__ ):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase   		=				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
			UpperCAmelCase   		=				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
			UpperCAmelCase   		=				tokenized_datasets.rename_column('''label'''			,			'''labels''' )

			def collate_fn(UpperCamelCase__ ):
						# 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.
			UpperCAmelCase   		=				DataLoader(
			    tokenized_datasets['''train''']			,			shuffle=UpperCAmelCase_			,			collate_fn=UpperCAmelCase_			,			batch_size=UpperCAmelCase_ )
			UpperCAmelCase   		=				DataLoader(
			    tokenized_datasets['''validation''']			,			shuffle=UpperCAmelCase_			,			collate_fn=UpperCAmelCase_			,			batch_size=UpperCAmelCase_ )

			return train_dataloader, eval_dataloader





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				Accelerator()

			# 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'''] )
			UpperCAmelCase   		=				args.model_name_or_path

			set_seed(UpperCAmelCase_ )
			UpperCAmelCase		,			UpperCAmelCase   		=				get_dataloaders(UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_ )

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

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

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase   		=				accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase   		=				1
			UpperCAmelCase   		=				(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
			):
						UpperCAmelCase   		=				get_linear_schedule_with_warmup(
						    optimizer=UpperCAmelCase_			,			num_warmup_steps=0			,			num_training_steps=UpperCAmelCase_			,			)
			else:
						UpperCAmelCase   		=				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.
			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				accelerator.prepare(
			    UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_ )

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

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

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

						if best_performance < eval_metric["accuracy"]:
									UpperCAmelCase   		=				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 						__SCREAMING_SNAKE_CASE  (				)  ->				Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				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.'''			,			)
			UpperCAmelCase   		=				parser.parse_args()
			UpperCAmelCase   		=				{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
			training_function(UpperCAmelCase_			,			UpperCAmelCase_ )


if __name__ == "__main__":
				main()







'''simple docstring'''

from __future__ import annotations

from fractions import Fraction





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						int		):
 return (
     num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
 )





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  11
 lowerCamelCase_      =  int("1" + "0" * digit_len		)
 for num in range(UpperCAmelCase_					,				UpperCAmelCase_		):
  while den <= 99:
   if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
    if is_digit_cancelling(UpperCAmelCase_					,				UpperCAmelCase_		):
     solutions.append(F'''{num}/{den}'''		)
   den += 1
  num += 1
  lowerCamelCase_      =  10
 return solutions





def __snake_case					(      UpperCAmelCase_						:						int = 2		):
 lowerCamelCase_      =  1.0
 for fraction in fraction_list(UpperCAmelCase_		):
  lowerCamelCase_      =  Fraction(UpperCAmelCase_		)
  result *= frac.denominator / frac.numerator
 return int(UpperCAmelCase_		)


if __name__ == "__main__":
     print(solution())



