
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       :				Any       =			'''true'''






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Optional[int]=82       ,					__lowercase   :	Optional[int]=16 ) ->  Optional[Any]:
			set_seed(42 )
			_snake_case		=			RegressionModel()
			_snake_case		=			deepcopy(__lowercase )
			_snake_case		=			RegressionDataset(length=__lowercase )
			_snake_case		=			DataLoader(__lowercase       ,					batch_size=__lowercase )
			model.to(accelerator.device )
			_snake_case	,					_snake_case		=			accelerator.prepare(__lowercase       ,					__lowercase )
			return model, ddp_model, dataloader






def        a_		(       __lowercase   :	Accelerator       ,					__lowercase   :	Dict=False ) ->  int:
			_snake_case		=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
			_snake_case		=			load_dataset('glue'       ,					'mrpc'       ,					split='validation' )

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

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

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

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

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






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






def        a_		(       __lowercase   :	int       ,					__lowercase   :	str       ,					__lowercase   :	Dict ) ->  Tuple:
			_snake_case		=			[]
			for batch in dataloader:
						_snake_case	,					_snake_case		=			batch.values()
						with torch.no_grad():
									_snake_case		=			model(__lowercase )
									_snake_case	,					_snake_case		=			accelerator.gather_for_metrics((logit, target) )
									logits_and_targets.append((logit, target) )
			_snake_case	,					_snake_case		=			[], []
			for logit, targ in logits_and_targets:
						logits.append(__lowercase )
						targs.append(__lowercase )
			_snake_case	,					_snake_case		=			torch.cat(__lowercase ), torch.cat(__lowercase )
			return logits, targs






def        a_		(       __lowercase   :	Accelerator       ,					__lowercase   :	Tuple=82       ,					__lowercase   :	str=False       ,					__lowercase   :	List[Any]=False       ,					__lowercase   :	Dict=16 ) ->  List[str]:
			_snake_case	,					_snake_case	,					_snake_case		=			get_basic_setup(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case		=			generate_predictions(__lowercase       ,					__lowercase       ,					__lowercase )
			assert (
			    len(__lowercase ) == num_samples
			), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowercase )}'''






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

			# Then do distributed
			_snake_case	,					_snake_case	,					_snake_case		=			setup['ddp']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									_snake_case		=			model(**__lowercase )
						_snake_case		=			outputs.logits.argmax(dim=-1 )
						_snake_case		=			batch['labels']
						_snake_case	,					_snake_case		=			accelerator.gather_for_metrics((preds, references) )
						metric.add_batch(predictions=__lowercase       ,					references=__lowercase )
			_snake_case		=			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        a_		(       ) ->  int:
			_snake_case		=			Accelerator(split_batches=__lowercase       ,					dispatch_batches=__lowercase )
			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(__lowercase       ,					__lowercase )
												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]:
									_snake_case		=			Accelerator(split_batches=__lowercase       ,					dispatch_batches=__lowercase )
									if accelerator.is_local_main_process:
												print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
									test_torch_metrics(__lowercase       ,					99 )
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('**Test last batch is not dropped when perfectly divisible**' )
			_snake_case		=			Accelerator()
			test_torch_metrics(__lowercase       ,					512 )
			accelerator.state._reset_state()






def        a_		(       __lowercase   :	Union[str, Any] ) ->  Any:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
			main()

_lowerCamelCase       :				int       =			[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCamelCase       :				str       =			[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCamelCase       :				List[str]       =			{
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}






def        a_		(       __lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  str:
			assert len(str(__lowercase ) ) > 2, "year should be in YYYY format"
			assert 1 <= month <= 12, "month should be between 1 to 12"
			assert 1 <= day <= 31, "day should be between 1 to 31"

			# Doomsday algorithm:
			_snake_case		=			year // 100
			_snake_case		=			(5 * (century % 4) + 2) % 7
			_snake_case		=			year % 100
			_snake_case		=			centurian % 12
			_snake_case		=			(
			    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
			) % 7
			_snake_case		=			(
			    DOOMSDAY_NOT_LEAP[month - 1]
			    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
			    else DOOMSDAY_LEAP[month - 1]
			)
			_snake_case		=			(dooms_day + day - day_anchor) % 7
			return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
			import doctest

			doctest.testmod()