



"""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


__snake_case							:   int      =    'true'


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


def 			_lowercase				(  __snake_case					,__snake_case=False	)						->   Optional[int]:
			__lowerCAmelCase      :  Optional[int]												=       AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
			__lowerCAmelCase      :  Dict												=       load_dataset("glue"					,"mrpc"					,split="validation"	)

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

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

			__lowerCAmelCase      :  Union[str, Any]												=       tokenized_datasets.rename_column("label"					,"labels"	)

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


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   List[str]:
			__lowerCAmelCase      :  Union[str, Any]												=       []
			for batch in dataloader:
						__lowerCAmelCase      :  Any												=       batch.values()
						with torch.no_grad():
									__lowerCAmelCase      :  str												=       model(lowerCAmelCase_	)
									__lowerCAmelCase      :  Optional[int]												=       accelerator.gather_for_metrics((logit, target)	)
									logits_and_targets.append((logit, target)	)
			__lowerCAmelCase      :  Dict												=       [], []
			for logit, targ in logits_and_targets:
						logits.append(lowerCAmelCase_	)
						targs.append(lowerCAmelCase_	)
			__lowerCAmelCase      :  Dict												=       torch.cat(lowerCAmelCase_	), torch.cat(lowerCAmelCase_	)
			return logits, targs


def 			_lowercase				(  __snake_case					,__snake_case=82					,__snake_case=False					,__snake_case=False					,__snake_case=16	)						->   List[Any]:
			__lowerCAmelCase      :  int												=       get_basic_setup(lowerCAmelCase_					,lowerCAmelCase_					,lowerCAmelCase_	)
			__lowerCAmelCase      :  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 			_lowercase				(  __snake_case = False					,__snake_case = False	)						->   List[Any]:
			__lowerCAmelCase      :  Optional[Any]												=       evaluate.load("glue"					,"mrpc"	)
			__lowerCAmelCase      :  Any												=       get_mrpc_setup(lowerCAmelCase_					,lowerCAmelCase_	)
			# First do baseline
			__lowerCAmelCase      :  str												=       setup['no']
			model.to(lowerCAmelCase_	)
			model.eval()
			for batch in dataloader:
						batch.to(lowerCAmelCase_	)
						with torch.inference_mode():
									__lowerCAmelCase      :  List[Any]												=       model(**lowerCAmelCase_	)
						__lowerCAmelCase      :  Optional[Any]												=       outputs.logits.argmax(dim=-1	)
						metric.add_batch(predictions=lowerCAmelCase_					,references=batch["labels"]	)
			__lowerCAmelCase      :  Any												=       metric.compute()

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


def 			_lowercase				(  __snake_case	)						->   int:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()




'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







							def 							__lowercase			(							self					:       List[Any]		):
									super().setUp()

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase		)	,_UpperCAmelCase		)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase		)	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									_a     :      Any        =							list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]	,'<s>'		)
									self.assertEqual(vocab_keys[1]	,'<pad>'		)
									self.assertEqual(vocab_keys[-1]	,'<mask>'		)
									self.assertEqual(len(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 6)	,batch.input_ids.shape		)
									self.assertEqual((2, 6)	,batch.attention_mask.shape		)
									_a     :      Tuple        =							batch.input_ids.tolist()[0]
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_a     :      Optional[Any]        =							[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




