

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					:			Optional[int]  				=			"true"






def        UpperCamelCase_(							_snake_case   :  Optional[Any]       ,     _snake_case   :  Dict=82       ,     _snake_case   :  Dict=16	):




			"""simple docstring"""






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






def        UpperCamelCase_(							_snake_case   :  Accelerator       ,     _snake_case   :  List[Any]=False	):




			"""simple docstring"""






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

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

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

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

			def collate_fn(_snake_case   :  Union[str, Any]	):
						if use_longest:
									return tokenizer.pad(_snake_case       ,     padding='longest'       ,     return_tensors='pt'	)
						return tokenizer.pad(_snake_case       ,     padding='max_length'       ,     max_length=128       ,     return_tensors='pt'	)

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






def        UpperCamelCase_(							_snake_case   :  Optional[int]       ,     _snake_case   :  Optional[int]	):




			"""simple docstring"""






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






def        UpperCamelCase_(							_snake_case   :  Optional[int]       ,     _snake_case   :  List[str]       ,     _snake_case   :  Optional[int]	):




			"""simple docstring"""






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






def        UpperCamelCase_(							_snake_case   :  Accelerator       ,     _snake_case   :  int=82       ,     _snake_case   :  Tuple=False       ,     _snake_case   :  int=False       ,     _snake_case   :  List[str]=16	):




			"""simple docstring"""






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






def        UpperCamelCase_(							_snake_case   :  bool = False       ,     _snake_case   :  bool = False	):




			"""simple docstring"""






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

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




			"""simple docstring"""






			__a        =Accelerator(split_batches=_snake_case       ,     dispatch_batches=_snake_case	)
			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(_snake_case       ,     _snake_case	)
												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]:
									__a        =Accelerator(split_batches=_snake_case       ,     dispatch_batches=_snake_case	)
									if accelerator.is_local_main_process:
												print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'	)
									test_torch_metrics(_snake_case       ,     99	)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('**Test last batch is not dropped when perfectly divisible**'	)
			__a        =Accelerator()
			test_torch_metrics(_snake_case       ,     512	)
			accelerator.state._reset_state()






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			main()


if __name__ == "__main__":
						main()









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






