


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a : List[Any]		=				'''true'''


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=82       ,			_UpperCamelCase=16  ):


			'''simple docstring'''







			set_seed(42  )
			__lowercase 					=   RegressionModel()
			__lowercase 					=   deepcopy(_UpperCamelCase  )
			__lowercase 					=   RegressionDataset(length=_UpperCamelCase  )
			__lowercase 					=   DataLoader(_UpperCamelCase       ,			batch_size=_UpperCamelCase  )
			model.to(accelerator.device  )
			__lowercase					, __lowercase 					=   accelerator.prepare(_UpperCamelCase       ,			_UpperCamelCase  )
			return model, ddp_model, dataloader


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''  )
			__lowercase 					=   load_dataset('''glue'''       ,			'''mrpc'''       ,			split='''validation'''  )

			def tokenize_function(_UpperCamelCase  ):
						__lowercase 					=   tokenizer(examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase  )
						return outputs

			with accelerator.main_process_first():
						__lowercase 					=   dataset.map(
						    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			)

			__lowercase 					=   tokenized_datasets.rename_column('''label'''       ,			'''labels'''  )

			def collate_fn(_UpperCamelCase  ):
						if use_longest:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )

			return DataLoader(_UpperCamelCase       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=16  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   Accelerator(dispatch_batches=_UpperCamelCase       ,			split_batches=_UpperCamelCase  )
			__lowercase 					=   get_dataloader(_UpperCamelCase       ,			not dispatch_batches  )
			__lowercase 					=   AutoModelForSequenceClassification.from_pretrained(
			    '''hf-internal-testing/mrpc-bert-base-cased'''       ,			return_dict=_UpperCamelCase  )
			__lowercase					, __lowercase 					=   accelerator.prepare(_UpperCamelCase       ,			_UpperCamelCase  )
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			for batch in dataloader:
						__lowercase					, __lowercase 					=   batch.values()
						with torch.no_grad():
									__lowercase 					=   model(_UpperCamelCase  )
									__lowercase					, __lowercase 					=   accelerator.gather_for_metrics((logit, target)  )
									logits_and_targets.append((logit, target)  )
			__lowercase					, __lowercase 					=   [], []
			for logit, targ in logits_and_targets:
						logits.append(_UpperCamelCase  )
						targs.append(_UpperCamelCase  )
			__lowercase					, __lowercase 					=   torch.cat(_UpperCamelCase  ), torch.cat(_UpperCamelCase  )
			return logits, targs


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=82       ,			_UpperCamelCase=False       ,			_UpperCamelCase=False       ,			_UpperCamelCase=16  ):


			'''simple docstring'''







			__lowercase					, __lowercase					, __lowercase 					=   get_basic_setup(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )
			__lowercase					, __lowercase 					=   generate_predictions(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )
			assert (
			    len(_UpperCamelCase  ) == num_samples
			), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase  )}'


def 		lowercase_  (     _UpperCamelCase = False       ,			_UpperCamelCase = False  ):


			'''simple docstring'''







			__lowercase 					=   evaluate.load('''glue'''       ,			'''mrpc'''  )
			__lowercase					, __lowercase 					=   get_mrpc_setup(_UpperCamelCase       ,			_UpperCamelCase  )
			# First do baseline
			__lowercase					, __lowercase					, __lowercase 					=   setup['''no''']
			model.to(_UpperCamelCase  )
			model.eval()
			for batch in dataloader:
						batch.to(_UpperCamelCase  )
						with torch.inference_mode():
									__lowercase 					=   model(**_UpperCamelCase  )
						__lowercase 					=   outputs.logits.argmax(dim=-1  )
						metric.add_batch(predictions=_UpperCamelCase       ,			references=batch['''labels''']  )
			__lowercase 					=   metric.compute()

			# Then do distributed
			__lowercase					, __lowercase					, __lowercase 					=   setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									__lowercase 					=   model(**_UpperCamelCase  )
						__lowercase 					=   outputs.logits.argmax(dim=-1  )
						__lowercase 					=   batch['''labels''']
						__lowercase					, __lowercase 					=   accelerator.gather_for_metrics((preds, references)  )
						metric.add_batch(predictions=_UpperCamelCase       ,			references=_UpperCamelCase  )
			__lowercase 					=   metric.compute()

			for key in "accuracy f1".split():
						assert math.isclose(
						    baseline[key]       ,			distributed[key]  ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   Accelerator(split_batches=_UpperCamelCase       ,			dispatch_batches=_UpperCamelCase  )
			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_warning()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			# These are a bit slower so they should only be ran on the GPU or TPU
			if torch.cuda.is_available() or is_tpu_available():
						if accelerator.is_local_main_process:
									print('''**Testing gather_for_metrics**'''  )
						for split_batches in [True, False]:
									for dispatch_batches in [True, False]:
												if accelerator.is_local_main_process:
															print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'  )
												test_mrpc(_UpperCamelCase       ,			_UpperCamelCase  )
												accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test torch metrics**'''  )
			for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
									__lowercase 					=   Accelerator(split_batches=_UpperCamelCase       ,			dispatch_batches=_UpperCamelCase  )
									if accelerator.is_local_main_process:
												print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'  )
									test_torch_metrics(_UpperCamelCase       ,			99  )
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''  )
			__lowercase 					=   Accelerator()
			test_torch_metrics(_UpperCamelCase       ,			5_12  )
			accelerator.state._reset_state()


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			main()


if __name__ == "__main__":
	main()






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   {
			    '''^''': 3,
			    '''*''': 2,
			    '''/''': 2,
			    '''%''': 2,
			    '''+''': 1,
			    '''-''': 1,
			}  # Priority of each operator
			__lowercase 					=   len(_UpperCamelCase  ) if (len(_UpperCamelCase  ) > 7) else 7

			# Print table header for output
			print(
			    '''Symbol'''.center(8  )       ,			'''Stack'''.center(_UpperCamelCase  )       ,			'''Postfix'''.center(_UpperCamelCase  )       ,			sep=''' | '''       ,			)
			print('''-''' * (print_width * 3 + 7)  )

			for x in infix:
						if x.isalpha() or x.isdigit():
									post_fix.append(_UpperCamelCase  )  # if x is Alphabet / Digit, add it to Postfix
						elif x == "(":
									stack.append(_UpperCamelCase  )  # if x is "(" push to Stack
						elif x == ")":  # if x is ")" pop stack until "(" is encountered
									while stack[-1] != "(":
												post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
									stack.pop()
						else:
									if len(_UpperCamelCase  ) == 0:
												stack.append(_UpperCamelCase  )  # If stack is empty, push x to stack
									else:  # while priority of x is not > priority of element in the stack
												while len(_UpperCamelCase  ) > 0 and priority[x] <= priority[stack[-1]]:
															post_fix.append(stack.pop()  )  # pop stack & add to Postfix
												stack.append(_UpperCamelCase  )  # push x to stack

						print(
						    x.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			while len(_UpperCamelCase  ) > 0:  # while stack is not empty
						post_fix.append(stack.pop()  )  # pop stack & add to Postfix
						print(
						    ''' '''.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			return "".join(_UpperCamelCase  )  # return Postfix as str


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   list(infix[::-1]  )  # reverse the infix equation

			for i in range(len(_UpperCamelCase  )  ):
						if infix[i] == "(":
									__lowercase 					=   ''')'''  # change "(" to ")"
						elif infix[i] == ")":
									__lowercase 					=   '''('''  # change ")" to "("

			return (infix_2_postfix(''''''.join(_UpperCamelCase  )  ))[
			    ::-1
			]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	a : Union[str, Any]		=				input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
	a : Optional[Any]		=				''''''.join(Infix.split())  # Remove spaces from the input
	print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')


