






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


UpperCAmelCase_ :						Dict      =      '''true'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  Optional[Any]=82	,			__magic_name__					:  Any=16					)		->			Optional[Any]:






	"""simple docstring"""



	set_seed(42					)
	UpperCamelCase     :List[Any]          =	RegressionModel()
	UpperCamelCase     :Any          =	deepcopy(__magic_name__					)
	UpperCamelCase     :List[Any]          =	RegressionDataset(length=__magic_name__					)
	UpperCamelCase     :List[Any]          =	DataLoader(__magic_name__	,			batch_size=__magic_name__					)
	model.to(accelerator.device					)
	UpperCamelCase     ,      UpperCamelCase     :Tuple          =	accelerator.prepare(__magic_name__	,			__magic_name__					)
	return model, ddp_model, dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  List[Any]=False					)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :int          =	AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""					)
	UpperCamelCase     :Optional[int]          =	load_dataset("""glue"""	,			"""mrpc"""	,			split="""validation"""					)

	def tokenize_function(__magic_name__					:  int					):
		UpperCamelCase     :Tuple          =	tokenizer(examples["""sentence1"""]	,			examples["""sentence2"""]	,			truncation=__magic_name__	,			max_length=__magic_name__					)
		return outputs

	with accelerator.main_process_first():
		UpperCamelCase     :List[str]          =	dataset.map(
		    __magic_name__	,			batched=__magic_name__	,			remove_columns=["""idx""", """sentence1""", """sentence2"""]	,			)

	UpperCamelCase     :str          =	tokenized_datasets.rename_column("""label"""	,			"""labels"""					)

	def collate_fn(__magic_name__					:  Any					):
		if use_longest:
			return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Union[str, Any]					)		->			List[str]:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  str					)		->			int:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  Optional[Any]=82	,			__magic_name__					:  int=False	,			__magic_name__					:  Dict=False	,			__magic_name__					:  Tuple=16					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :int          =	get_basic_setup(__magic_name__	,			__magic_name__	,			__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Any          =	generate_predictions(__magic_name__	,			__magic_name__	,			__magic_name__					)
	assert (
	    len(__magic_name__					) == num_samples
	), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__magic_name__					)}"""

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bool = False	,			__magic_name__					:  bool = False					)		->			List[str]:






	"""simple docstring"""



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

	# Then do distributed
	UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	setup["""ddp"""]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			UpperCamelCase     :Union[str, Any]          =	model(**__magic_name__					)
		UpperCamelCase     :Union[str, Any]          =	outputs.logits.argmax(dim=-1					)
		UpperCamelCase     :Optional[Any]          =	batch["""labels"""]
		UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	accelerator.gather_for_metrics((preds, references)					)
		metric.add_batch(predictions=__magic_name__	,			references=__magic_name__					)
	UpperCamelCase     :Optional[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    SCREAMING_SNAKE_CASE_							(							)		->			List[Any]:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict					)		->			Tuple:






	"""simple docstring"""



	main()


if __name__ == "__main__":
	main()






def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE ,x % y    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return (x * y) // greatest_common_divisor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE = 20    )	-> int:
       lowerCamelCase   :      List[Any]			 =					1
       for i in range(1 ,n + 1    ):
              lowerCamelCase   :      List[str]			 =					lcm(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       return g


if __name__ == "__main__":
       print(f'''{solution() = }''')






