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 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Any        =      BarthezTokenizer
			A__    :       Union[str, Any]        =      BarthezTokenizerFast
			A__    :       Dict        =      True
			A__    :       int        =      True



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								super().setUp()

								__a		 =       BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'   )
								tokenizer.save_pretrained(self.tmpdirname   )
								tokenizer.save_pretrained(self.tmpdirname				,						legacy_format=UpperCAmelCase   )
								__a		 =       tokenizer



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       '<pad>'
								__a		 =       1

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       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   )				,						1_0_1_1_2_2   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								self.assertEqual(self.get_tokenizer().vocab_size				,						1_0_1_1_2_2   )



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								__a		 =       [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

								__a		 =       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		 =       batch.input_ids.tolist()[0]
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								if not self.test_rust_tokenizer:
													return

								__a		 =       self.get_tokenizer()
								__a		 =       self.get_rust_tokenizer()

								__a		 =       'I was born in 92000, and this is falsé.'

								__a		 =       tokenizer.tokenize(UpperCAmelCase   )
								__a		 =       rust_tokenizer.tokenize(UpperCAmelCase   )
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       tokenizer.encode(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
								__a		 =       rust_tokenizer.encode(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       self.get_rust_tokenizer()
								__a		 =       tokenizer.encode(UpperCAmelCase   )
								__a		 =       rust_tokenizer.encode(UpperCAmelCase   )
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								# fmt: off
								__a		 =       {'input_ids': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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		 =       [
								    '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				,						)





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'						)
					return device
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




