



'''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


__lowerCAmelCase									=   '''true'''



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=82						,			lowerCAmelCase_=16  )			->      Union[str, Any]:
		set_seed(42  )
		_a     :      Union[str, Any]        =							RegressionModel()
		_a     :      Dict        =							deepcopy(lowerCAmelCase_  )
		_a     :      Any        =							RegressionDataset(length=lowerCAmelCase_  )
		_a     :      List[str]        =							DataLoader(lowerCAmelCase_						,			batch_size=lowerCAmelCase_  )
		model.to(accelerator.device  )
		_a       ,					_a     :      int        =							accelerator.prepare(lowerCAmelCase_						,			lowerCAmelCase_  )
		return model, ddp_model, dataloader



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Optional[int]:
		_a     :      Optional[int]        =							AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'  )
		_a     :      Dict        =							load_dataset('glue'						,			'mrpc'						,			split='validation'  )

		def tokenize_function(lowerCAmelCase_  ):
				_a     :      Optional[Any]        =							tokenizer(examples['sentence1']						,			examples['sentence2']						,			truncation=lowerCAmelCase_						,			max_length=lowerCAmelCase_  )
				return outputs

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

		_a     :      Union[str, Any]        =							tokenized_datasets.rename_column('label'						,			'labels'  )

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      Union[str, Any]        =							[]
		for batch in dataloader:
				_a       ,					_a     :      Any        =							batch.values()
				with torch.no_grad():
						_a     :      str        =							model(lowerCAmelCase_  )
						_a       ,					_a     :      Optional[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     :      Dict        =							torch.cat(lowerCAmelCase_  ), torch.cat(lowerCAmelCase_  )
		return logits, targs



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=82						,			lowerCAmelCase_=False						,			lowerCAmelCase_=False						,			lowerCAmelCase_=16  )			->      List[Any]:
		_a       ,					_a       ,					_a     :      int        =							get_basic_setup(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		_a       ,					_a     :      Union[str, Any]        =							generate_predictions(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		assert (
		    len(lowerCAmelCase_  ) == num_samples
		), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_  )}"""



def      __lowerCamelCase     (      lowerCAmelCase_ = False						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      Optional[Any]        =							evaluate.load('glue'						,			'mrpc'  )
		_a       ,					_a     :      Any        =							get_mrpc_setup(lowerCAmelCase_						,			lowerCAmelCase_  )
		# First do baseline
		_a       ,					_a       ,					_a     :      str        =							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     :      Any        =							metric.compute()

		# Then do distributed
		_a       ,					_a       ,					_a     :      int        =							setup['ddp']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						_a     :      List[str]        =							model(**lowerCAmelCase_  )
				_a     :      int        =							outputs.logits.argmax(dim=-1  )
				_a     :      Optional[int]        =							batch['labels']
				_a       ,					_a     :      Dict        =							accelerator.gather_for_metrics((preds, references)  )
				metric.add_batch(predictions=lowerCAmelCase_						,			references=lowerCAmelCase_  )
		_a     :      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      __lowerCamelCase     (      )			->      str:
		_a     :      Optional[int]        =							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[Any]        =							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**'  )
		_a     :      int        =							Accelerator()
		test_torch_metrics(lowerCAmelCase_						,			512  )
		accelerator.state._reset_state()



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
							main()









'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




