

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    (				UpperCAmelCase__    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  BarthezTokenizer
		a__					       =  BarthezTokenizerFast
		a__					       =  True
		a__					       =  True


		def 		A__					(				self):
			super().setUp()

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


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						'<pad>'
			_UpperCamelCase   :		Any			=						1

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


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, 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(__lowerCAmelCase)	,       10_11_22)


		def 		A__					(				self):
			self.assertEqual(self.get_tokenizer().vocab_size	,       10_11_22)


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
			_UpperCamelCase   :		int			=						[0, 57, 30_18, 7_03_07, 91, 2]

			_UpperCamelCase   :		str			=						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)
			_UpperCamelCase   :		Tuple			=						batch.input_ids.tolist()[0]
			self.assertListEqual(__lowerCAmelCase	,       __lowerCAmelCase)


		def 		A__					(				self):






			if not self.test_rust_tokenizer:
				return

			_UpperCamelCase   :		Union[str, Any]			=						self.get_tokenizer()
			_UpperCamelCase   :		Dict			=						self.get_rust_tokenizer()

			_UpperCamelCase   :		Optional[int]			=						'I was born in 92000, and this is falsé.'

			_UpperCamelCase   :		Tuple			=						tokenizer.tokenize(__lowerCAmelCase)
			_UpperCamelCase   :		Dict			=						rust_tokenizer.tokenize(__lowerCAmelCase)
			self.assertListEqual(__lowerCAmelCase	,       __lowerCAmelCase)

			_UpperCamelCase   :		Optional[int]			=						tokenizer.encode(__lowerCAmelCase	,       add_special_tokens=__lowerCAmelCase)
			_UpperCamelCase   :		str			=						rust_tokenizer.encode(__lowerCAmelCase	,       add_special_tokens=__lowerCAmelCase)
			self.assertListEqual(__lowerCAmelCase	,       __lowerCAmelCase)

			_UpperCamelCase   :		int			=						self.get_rust_tokenizer()
			_UpperCamelCase   :		int			=						tokenizer.encode(__lowerCAmelCase)
			_UpperCamelCase   :		List[str]			=						rust_tokenizer.encode(__lowerCAmelCase)
			self.assertListEqual(__lowerCAmelCase	,       __lowerCAmelCase)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						{'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 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.
			_UpperCamelCase   :		Dict			=						[
			    '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	,       )







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase__								=		abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				Union[str, Any]:




	'''simple docstring'''




	config.addinivalue_line(
	    'markers'							,       'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'	)
	config.addinivalue_line(
	    'markers'							,       'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'	)
	config.addinivalue_line('markers'							,       'is_pipeline_test: mark test to run only when pipelines are tested'	)
	config.addinivalue_line('markers'							,       'is_staging_test: mark test to run only in the staging environment'	)
	config.addinivalue_line('markers'							,       'accelerate_tests: mark test that require accelerate'	)
	config.addinivalue_line('markers'							,       'tool_tests: mark the tool tests that are run on their specific schedule'	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]	)						->				List[Any]:




	'''simple docstring'''




	from transformers.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				List[Any]:




	'''simple docstring'''




	from transformers.testing_utils import pytest_terminal_summary_main

	_UpperCamelCase   :		str			=						terminalreporter.config.getoption('--make-reports'	)
	if make_reports:
		pytest_terminal_summary_main(UpperCAmelCase_							,       id=UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Dict	)						->				Tuple:




	'''simple docstring'''




	if exitstatus == 5:
		_UpperCamelCase   :		List[Any]			=						0


# Doctest custom flag to ignore output.
lowerCAmelCase__								=		doctest.register_optionflag("""IGNORE_RESULT""")

lowerCAmelCase__								=		doctest.OutputChecker







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			if IGNORE_RESULT & optionflags:
				return True
			return OutputChecker.check_output(self	,       __snake_case	,       __snake_case	,       __snake_case)

lowerCAmelCase__								=		CustomOutputChecker
lowerCAmelCase__								=		HfDoctestModule







lowerCAmelCase__								=		HfDocTestParser




