
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       =					'''true'''






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=82					,				__SCREAMING_SNAKE_CASE=16				):
	set_seed(42				)
	lowercase	    =       RegressionModel()
	lowercase	    =       deepcopy(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       RegressionDataset(length=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       DataLoader(__SCREAMING_SNAKE_CASE					,				batch_size=__SCREAMING_SNAKE_CASE				)
	model.to(accelerator.device				)
	lowercase    ,   lowercase	    =       accelerator.prepare(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	return model, ddp_model, dataloader






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=False				):
	lowercase	    =       AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'				)
	lowercase	    =       load_dataset('glue'					,				'mrpc'					,				split='validation'				)

	def tokenize_function(__SCREAMING_SNAKE_CASE				):
		lowercase	    =       tokenizer(examples['sentence1']					,				examples['sentence2']					,				truncation=__SCREAMING_SNAKE_CASE					,				max_length=__SCREAMING_SNAKE_CASE				)
		return outputs

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

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

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

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






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






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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=82					,				__SCREAMING_SNAKE_CASE=False					,				__SCREAMING_SNAKE_CASE=False					,				__SCREAMING_SNAKE_CASE=16				):
	lowercase    ,   lowercase    ,   lowercase	    =       get_basic_setup(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       generate_predictions(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	assert (
	    len(__SCREAMING_SNAKE_CASE				) == num_samples
	), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE				)}'''






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

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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()


'''simple docstring'''




def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if length <= 0 or not isinstance(_A ,  _A					):
				raise ValueError("""Length must be a positive integer."""					)
		return [n * (2 * n - 1) for n in range(_A					)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=1_0))




