
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        __lowercase		(       a_				,							unittest.TestCase							):
					__magic_name__    :				Optional[Any]  						=						BarthezTokenizer
					__magic_name__    :				Optional[int]  						=						BarthezTokenizerFast
					__magic_name__    :				Optional[int]  						=						True
					__magic_name__    :				Tuple  						=						True


					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''
									super().setUp()

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


					def 							lowerCAmelCase_     (						self    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	'''<pad>'''
									A_													=	1

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


					def 							lowerCAmelCase_     (						self    )		->				Optional[Any]:





									'''simple docstring'''
									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(lowerCAmelCase_    )							,							1_0_1_1_2_2    )


					def 							lowerCAmelCase_     (						self    )		->				int:





									'''simple docstring'''
									self.assertEqual(self.get_tokenizer().vocab_size							,							1_0_1_1_2_2    )


					@require_torch
					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									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(
									    lowerCAmelCase_							,							max_length=len(lowerCAmelCase_    )							,							padding=lowerCAmelCase_							,							truncation=lowerCAmelCase_							,							return_tensors='''pt'''    )
									self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_    )

									self.assertEqual((2, 6)							,							batch.input_ids.shape    )
									self.assertEqual((2, 6)							,							batch.attention_mask.shape    )
									A_													=	batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCAmelCase_							,							lowerCAmelCase_    )


					def 							lowerCAmelCase_     (						self    )		->				Any:





									'''simple docstring'''
									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(lowerCAmelCase_    )
									A_													=	rust_tokenizer.tokenize(lowerCAmelCase_    )
									self.assertListEqual(lowerCAmelCase_							,							lowerCAmelCase_    )

									A_													=	tokenizer.encode(lowerCAmelCase_							,							add_special_tokens=lowerCAmelCase_    )
									A_													=	rust_tokenizer.encode(lowerCAmelCase_							,							add_special_tokens=lowerCAmelCase_    )
									self.assertListEqual(lowerCAmelCase_							,							lowerCAmelCase_    )

									A_													=	self.get_rust_tokenizer()
									A_													=	tokenizer.encode(lowerCAmelCase_    )
									A_													=	rust_tokenizer.encode(lowerCAmelCase_    )
									self.assertListEqual(lowerCAmelCase_							,							lowerCAmelCase_    )


					@slow
					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									# 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=lowerCAmelCase_							,							model_name='''moussaKam/mbarthez'''							,							revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,							sequences=lowerCAmelCase_							,							)



'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



