





'''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  UpperCAmelCase     (			snake_case_							,		unittest.TestCase   ):
      _lowercase:			Union[str, Any]						     =   BarthezTokenizer
      _lowercase:			List[str]						     =   BarthezTokenizerFast
      _lowercase:			Tuple						     =   True
      _lowercase:			str						     =   True




      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            super().setUp()

            _lowerCAmelCase							     =							BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""  )
            tokenizer.save_pretrained(self.tmpdirname  )
            tokenizer.save_pretrained(self.tmpdirname			,			legacy_format=__snake_case  )
            _lowerCAmelCase							     =							tokenizer




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							"""<pad>"""
            _lowerCAmelCase							     =							1

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




      def 				lowercase__    (			self			: List[str]  )				->      Optional[int]:
            _lowerCAmelCase							     =							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(__snake_case  )			,			10_11_22  )




      def 				lowercase__    (			self			: Any  )				->      Optional[Any]:
            self.assertEqual(self.get_tokenizer().vocab_size			,			10_11_22  )




      @require_torch
      def 				lowercase__    (			self			: int  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            _lowerCAmelCase							     =							[0, 57, 30_18, 7_03_07, 91, 2]

            _lowerCAmelCase							     =							self.tokenizer(
                __snake_case			,			max_length=len(__snake_case  )			,			padding=__snake_case			,			truncation=__snake_case			,			return_tensors="""pt"""  )
            self.assertIsInstance(__snake_case			,			__snake_case  )

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




      def 				lowercase__    (			self			: List[Any]  )				->      Any:
            if not self.test_rust_tokenizer:
                  return

            _lowerCAmelCase							     =							self.get_tokenizer()
            _lowerCAmelCase							     =							self.get_rust_tokenizer()

            _lowerCAmelCase							     =							"""I was born in 92000, and this is falsé."""

            _lowerCAmelCase							     =							tokenizer.tokenize(__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.tokenize(__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )

            _lowerCAmelCase							     =							tokenizer.encode(__snake_case			,			add_special_tokens=__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.encode(__snake_case			,			add_special_tokens=__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )

            _lowerCAmelCase							     =							self.get_rust_tokenizer()
            _lowerCAmelCase							     =							tokenizer.encode(__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.encode(__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )




      @slow
      def 				lowercase__    (			self			: str  )				->      List[str]:
            # fmt: off
            _lowerCAmelCase							     =							{"""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							     =							[
                """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=__snake_case			,			model_name="""moussaKam/mbarthez"""			,			revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""			,			sequences=__snake_case			,			)










import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       OpenAIGPTConfig,
       OpenAIGPTDoubleHeadsModel,
       OpenAIGPTForSequenceClassification,
       OpenAIGPTLMHeadModel,
       OpenAIGPTModel,
   )
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_3     ,							SCREAMING_SNAKE_CASE__				:       str=7     ,							SCREAMING_SNAKE_CASE__				:       List[str]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=9_9     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=4     ,							SCREAMING_SNAKE_CASE__				:       Tuple=3_7     ,							SCREAMING_SNAKE_CASE__				:       Any="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       int=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       int=1_6     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       Any=0.02     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=4     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							)		->			Any:
             a_					:		Tuple							=  parent
             a_					:		int							=  batch_size
             a_					:		Tuple							=  seq_length
             a_					:		List[Any]							=  is_training
             a_					:		List[str]							=  use_token_type_ids
             a_					:		Dict							=  use_labels
             a_					:		Any							=  vocab_size
             a_					:		List[str]							=  hidden_size
             a_					:		Tuple							=  num_hidden_layers
             a_					:		List[Any]							=  num_attention_heads
             a_					:		Dict							=  intermediate_size
             a_					:		Any							=  hidden_act
             a_					:		List[str]							=  hidden_dropout_prob
             a_					:		Tuple							=  attention_probs_dropout_prob
             a_					:		Optional[Any]							=  max_position_embeddings
             a_					:		List[Any]							=  type_vocab_size
             a_					:		int							=  type_sequence_label_size
             a_					:		List[Any]							=  initializer_range
             a_					:		List[str]							=  num_labels
             a_					:		Union[str, Any]							=  num_choices
             a_					:		str							=  scope
             a_					:		Tuple							=  self.vocab_size - 1
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Any:
             a_					:		Optional[int]							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )

             a_					:		Any							=  None
             if self.use_token_type_ids:
                    a_					:		Tuple							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size     )

             a_					:		List[Any]							=  None
             a_					:		Union[str, Any]							=  None
             a_					:		List[Any]							=  None
             if self.use_labels:
                    a_					:		Tuple							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )
                    a_					:		Any							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels     )
                    a_					:		List[Any]							=  ids_tensor([self.batch_size]     ,							self.num_choices     )

             a_					:		Union[str, Any]							=  OpenAIGPTConfig(
                 vocab_size=self.vocab_size     ,							n_embd=self.hidden_size     ,							n_layer=self.num_hidden_layers     ,							n_head=self.num_attention_heads     ,							n_positions=self.max_position_embeddings     ,							pad_token_id=self.pad_token_id     ,							)

             a_					:		List[str]							=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,							2     )

             return (
                 config,
                 input_ids,
                 head_mask,
                 token_type_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							*SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Union[str, Any]:
             a_					:		Dict							=  OpenAIGPTModel(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		str							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							head_mask=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  model(SCREAMING_SNAKE_CASE__     )

             self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							*SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Any:
             a_					:		str							=  OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.loss.shape     ,							()     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Dict:
             a_					:		int							=  OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		str							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.loss.shape     ,							()     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							*SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Any							=  self.num_labels
             a_					:		Dict							=  OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Dict							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )
             a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             a_					:		Optional[Any]							=  self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		
             )					:		Optional[Any]							=  config_and_inputs
             a_					:		Optional[int]							=  {
                 'input_ids': input_ids,
                 'token_type_ids': token_type_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict
@require_torch
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Tuple        =						(
          (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
          if is_torch_available()
          else ()
      )
      snake_case__				:     List[str]        =						(
          (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
      snake_case__				:     Dict        =						(
          {
              '''feature-extraction''': OpenAIGPTModel,
              '''text-classification''': OpenAIGPTForSequenceClassification,
              '''text-generation''': OpenAIGPTLMHeadModel,
              '''zero-shot''': OpenAIGPTForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Dict:
             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                    # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                    # tiny config could not be created.
                    return True

             return False
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any=False     )		->			List[str]:
             a_					:		str							=  super()._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )

             if return_labels:
                    if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                           a_					:		Optional[Any]							=  torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     ,							)
                           a_					:		str							=  inputs_dict['labels']
                           a_					:		Optional[int]							=  inputs_dict['labels']
                           a_					:		Optional[int]							=  torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.num_choices)     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     ,							)
                           a_					:		Union[str, Any]							=  torch.zeros(
                               self.model_tester.batch_size     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     )
             return inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[Any]:
             a_					:		str							=  OpenAIGPTModelTester(self     )
             a_					:		int							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							n_embd=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Tuple:
             a_					:		Dict							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             a_					:		Any							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             a_					:		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             a_					:		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE__     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			str:
             for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		str							=  OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )
@require_torch
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             a_					:		Dict							=  OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'     )
             model.to(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     )  # the president is
             a_					:		Tuple							=  [
                 4_8_1,
                 4_7_3_5,
                 5_4_4,
                 2_4_6,
                 9_6_3,
                 8_7_0,
                 7_6_2,
                 2_3_9,
                 2_4_4,
                 4_0_4_7_7,
                 2_4_4,
                 2_4_9,
                 7_1_9,
                 8_8_1,
                 4_8_7,
                 5_4_4,
                 2_4_0,
                 2_4_4,
                 6_0_3,
                 4_8_1,
             ]  # the president is a very good man. " \n " i\'m sure he is, " said the

             a_					:		Dict							=  model.generate(SCREAMING_SNAKE_CASE__     ,							do_sample=SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(output_ids[0].tolist()     ,							SCREAMING_SNAKE_CASE__     )


