


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


lowerCamelCase						=		'''true'''






def 					lowerCamelCase_   (	_a     ,       _a=82     ,       _a=16	):




		"""simple docstring"""






		set_seed(42	)
		lowerCAmelCase__       :      Any   					= RegressionModel()
		lowerCAmelCase__       :      List[Any]   					= deepcopy(_a	)
		lowerCAmelCase__       :      List[Any]   					= RegressionDataset(length=_a	)
		lowerCAmelCase__       :      Optional[int]   					= DataLoader(_a     ,       batch_size=_a	)
		model.to(accelerator.device	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= accelerator.prepare(_a     ,       _a	)
		return model, ddp_model, dataloader






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






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

		def tokenize_function(_a	):
				lowerCAmelCase__       :      Dict   					= tokenizer(examples['''sentence1''']     ,       examples['''sentence2''']     ,       truncation=_a     ,       max_length=_a	)
				return outputs

		with accelerator.main_process_first():
				lowerCAmelCase__       :      str   					= dataset.map(
				    _a     ,       batched=_a     ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,       )

		lowerCAmelCase__       :      List[str]   					= tokenized_datasets.rename_column('''label'''     ,       '''labels'''	)

		def collate_fn(_a	):
				if use_longest:
						return tokenizer.pad(_a     ,       padding='''longest'''     ,       return_tensors='''pt'''	)
				return tokenizer.pad(_a     ,       padding='''max_length'''     ,       max_length=128     ,       return_tensors='''pt'''	)

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






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= []
		for batch in dataloader:
				lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= batch.values()
				with torch.no_grad():
						lowerCAmelCase__       :      Optional[int]   					= model(_a	)
						lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= accelerator.gather_for_metrics((logit, target)	)
						logits_and_targets.append((logit, target)	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= [], []
		for logit, targ in logits_and_targets:
				logits.append(_a	)
				targs.append(_a	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= torch.cat(_a	), torch.cat(_a	)
		return logits, targs






def 					lowerCamelCase_   (	_a     ,       _a=82     ,       _a=False     ,       _a=False     ,       _a=16	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= get_basic_setup(_a     ,       _a     ,       _a	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= generate_predictions(_a     ,       _a     ,       _a	)
		assert (
		    len(_a	) == num_samples
		), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a	)}'






def 					lowerCamelCase_   (	_a = False     ,       _a = False	):




		"""simple docstring"""






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

		# Then do distributed
		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= setup['''ddp''']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						lowerCAmelCase__       :      str   					= model(**_a	)
				lowerCAmelCase__       :      Any   					= outputs.logits.argmax(dim=-1	)
				lowerCAmelCase__       :      int   					= batch['''labels''']
				lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= accelerator.gather_for_metrics((preds, references)	)
				metric.add_batch(predictions=_a     ,       references=_a	)
		lowerCAmelCase__       :      Any   					= 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 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= Accelerator(split_batches=_a     ,       dispatch_batches=_a	)
		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(_a     ,       _a	)
								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]:
						lowerCAmelCase__       :      Union[str, Any]   					= Accelerator(split_batches=_a     ,       dispatch_batches=_a	)
						if accelerator.is_local_main_process:
								print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'	)
						test_torch_metrics(_a     ,       99	)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test last batch is not dropped when perfectly divisible**'''	)
		lowerCAmelCase__       :      Any   					= Accelerator()
		test_torch_metrics(_a     ,       512	)
		accelerator.state._reset_state()






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		main()


if __name__ == "__main__":
		main()








import math





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 10)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 10)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)




