

'''simple docstring'''





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


A											=     'true'





def a(lowercase__			, lowercase__=82			, lowercase__=16	):





	'''simple docstring'''
	set_seed(42	)
	snake_case_      		= RegressionModel()
	snake_case_      		= deepcopy(lowerCAmelCase_	)
	snake_case_      		= RegressionDataset(length=lowerCAmelCase_	)
	snake_case_      		= DataLoader(lowerCAmelCase_			, batch_size=lowerCAmelCase_	)
	model.to(accelerator.device	)
	snake_case_      		= accelerator.prepare(lowerCAmelCase_			, lowerCAmelCase_	)
	return model, ddp_model, dataloader





def a(lowercase__			, lowercase__=False	):





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

	def tokenize_function(lowercase__	):
		snake_case_      		= tokenizer(examples['sentence1']			, examples['sentence2']			, truncation=lowerCAmelCase_			, max_length=lowerCAmelCase_	)
		return outputs

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

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

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

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





def a(lowercase__			, lowercase__	):





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





def a(lowercase__			, lowercase__			, lowercase__	):





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





def a(lowercase__			, lowercase__=82			, lowercase__=False			, lowercase__=False			, lowercase__=16	):





	'''simple docstring'''
	snake_case_      		= get_basic_setup(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_	)
	snake_case_      		= generate_predictions(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_	)
	assert (
	    len(lowerCAmelCase_	) == num_samples
	), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_	)}"""





def a(lowercase__ = False			, lowercase__ = False	):





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

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





	'''simple docstring'''
	snake_case_      		= 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]:
			snake_case_      		= 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_			, 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(lowerCAmelCase_			, 512	)
	accelerator.state._reset_state()





def a(lowercase__	):





	'''simple docstring'''
	main()


if __name__ == "__main__":
		main()



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
