
"""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  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Union[str, Any]				   =	BarthezTokenizer
		A_						:       Tuple				   =	BarthezTokenizerFast
		A_						:       Dict				   =	True
		A_						:       List[str]				   =	True

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

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      '<pad>'
			__lowerCAmelCase			:						Union[str, Any]									=      1

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[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(_SCREAMING_SNAKE_CASE )       ,		10_11_22 )

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

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

			__lowerCAmelCase			:						Optional[int]									=      self.tokenizer(
			    _SCREAMING_SNAKE_CASE       ,		max_length=len(_SCREAMING_SNAKE_CASE )       ,		padding=_SCREAMING_SNAKE_CASE       ,		truncation=_SCREAMING_SNAKE_CASE       ,		return_tensors='pt' )
			self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			self.assertEqual((2, 6)       ,		batch.input_ids.shape )
			self.assertEqual((2, 6)       ,		batch.attention_mask.shape )
			__lowerCAmelCase			:						List[str]									=      batch.input_ids.tolist()[0]
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			if not self.test_rust_tokenizer:
				return

			__lowerCAmelCase			:						Tuple									=      self.get_tokenizer()
			__lowerCAmelCase			:						Optional[int]									=      self.get_rust_tokenizer()

			__lowerCAmelCase			:						List[str]									=      'I was born in 92000, and this is falsé.'

			__lowerCAmelCase			:						Optional[int]									=      tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[Any]									=      tokenizer.encode(_SCREAMING_SNAKE_CASE       ,		add_special_tokens=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      rust_tokenizer.encode(_SCREAMING_SNAKE_CASE       ,		add_special_tokens=_SCREAMING_SNAKE_CASE )
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[int]									=      self.get_rust_tokenizer()
			__lowerCAmelCase			:						List[Any]									=      tokenizer.encode(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
			self.assertListEqual(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )







		@slow
		def 							__lowerCamelCase							(      self ):
			# fmt: off
			__lowerCAmelCase			:						str									=      {'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.
			__lowerCAmelCase			:						Union[str, 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=_SCREAMING_SNAKE_CASE       ,		model_name='moussaKam/mbarthez'       ,		revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,		sequences=_SCREAMING_SNAKE_CASE       ,		)



import os

import pytest
from attr import dataclass


lowerCamelCase											=       'us-east-1'  # defaults region




@dataclass
class A  :
      UpperCamelCase__							:  str
      UpperCamelCase__							:  Dict      ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
      UpperCamelCase__							:  int      ={
          'task_name': 'mnli',
          'per_device_train_batch_size': 16,
          'per_device_eval_batch_size': 16,
          'do_train': True,
          'do_eval': True,
          'do_predict': True,
          'output_dir': '/opt/ml/model',
          'overwrite_output_dir': True,
          'max_steps': 500,
          'save_steps': 5500,
      }
      UpperCamelCase__							:  Optional[Any]      ={**hyperparameters, 'max_steps': 1000}






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                        {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                    ]
             else:
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                        {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                    ]






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             return F'''{self.framework}-transfromers-test'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return F'''./tests/sagemaker/scripts/{self.framework}'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
             else:
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope='class'     )
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=SageMakerTestEnvironment(framework=request.cls.framework     )


