






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


SCREAMING_SNAKE_CASE__				     =			'''true'''

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Optional[int]=8_2   ,  lowerCamelCase_       :       str=1_6				):
			set_seed(4_2				)
			__a						:  Any									=     RegressionModel()
			__a						:  int									=     deepcopy(lowerCamelCase_				)
			__a						:  Tuple									=     RegressionDataset(length=lowerCamelCase_				)
			__a						:  str									=     DataLoader(lowerCamelCase_   ,  batch_size=lowerCamelCase_				)
			model.to(accelerator.device				)
			__a						,					__a						:  Union[str, Any]									=     accelerator.prepare(lowerCamelCase_   ,  lowerCamelCase_				)
			return model, ddp_model, dataloader

def        UpperCAmelCase__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       Dict=False				):
			__a						:  Dict									=     AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'				)
			__a						:  List[Any]									=     load_dataset('glue'   ,  'mrpc'   ,  split='validation'				)

			def tokenize_function(lowerCamelCase_       :       Optional[int]				):
						__a						:  List[Any]									=     tokenizer(examples['sentence1']   ,  examples['sentence2']   ,  truncation=lowerCamelCase_   ,  max_length=lowerCamelCase_				)
						return outputs

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

			__a						:  Optional[int]									=     tokenized_datasets.rename_column('label'   ,  'labels'				)

			def collate_fn(lowerCamelCase_       :       Union[str, Any]				):
						if use_longest:
									return tokenizer.pad(lowerCamelCase_   ,  padding='longest'   ,  return_tensors='pt'				)
						return tokenizer.pad(lowerCamelCase_   ,  padding='max_length'   ,  max_length=1_2_8   ,  return_tensors='pt'				)

			return DataLoader(lowerCamelCase_   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=1_6				)

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

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       Tuple=8_2   ,  lowerCamelCase_       :       List[Any]=False   ,  lowerCamelCase_       :       int=False   ,  lowerCamelCase_       :       Union[str, Any]=1_6				):
			__a						,					__a						,					__a						:  Tuple									=     get_basic_setup(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
			__a						,					__a						:  List[str]									=     generate_predictions(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
			assert (
			    len(lowerCamelCase_				) == num_samples
			), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase_				)}'''

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

			# Then do distributed
			__a						,					__a						,					__a						:  Any									=     setup['ddp']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									__a						:  str									=     model(**lowerCamelCase_				)
						__a						:  Optional[Any]									=     outputs.logits.argmax(dim=-1				)
						__a						:  int									=     batch['labels']
						__a						,					__a						:  Dict									=     accelerator.gather_for_metrics((preds, references)				)
						metric.add_batch(predictions=lowerCamelCase_   ,  references=lowerCamelCase_				)
			__a						:  Tuple									=     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__		(						):
			__a						:  str									=     Accelerator(split_batches=lowerCamelCase_   ,  dispatch_batches=lowerCamelCase_				)
			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(lowerCamelCase_   ,  lowerCamelCase_				)
												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						:  Optional[int]									=     Accelerator(split_batches=lowerCamelCase_   ,  dispatch_batches=lowerCamelCase_				)
									if accelerator.is_local_main_process:
												print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''				)
									test_torch_metrics(lowerCamelCase_   ,  9_9				)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('**Test last batch is not dropped when perfectly divisible**'				)
			__a						:  Dict									=     Accelerator()
			test_torch_metrics(lowerCamelCase_   ,  5_1_2				)
			accelerator.state._reset_state()

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict				):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()













'''simple docstring'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




