



'''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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				BarthezTokenizer
  __lowercase							:				int           =				BarthezTokenizerFast
  __lowercase							:				Optional[int]           =				True
  __lowercase							:				int           =				True




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      super().setUp()

      lowerCAmelCase__			:  int											= BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
      tokenizer.save_pretrained(self.tmpdirname				)
      tokenizer.save_pretrained(self.tmpdirname  ,legacy_format=__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= tokenizer




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  int											= """<pad>"""
      lowerCAmelCase__			:  Optional[Any]											= 1

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




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= 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				)  ,10_1122				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      self.assertEqual(self.get_tokenizer().vocab_size  ,10_1122				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      lowerCAmelCase__			:  List[Any]											= [0, 57, 3018, 7_0307, 91, 2]

      lowerCAmelCase__			:  Dict											= 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				)
      lowerCAmelCase__			:  Optional[int]											= batch.input_ids.tolist()[0]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:


      if not self.test_rust_tokenizer:
          return

      lowerCAmelCase__			:  Dict											= self.get_tokenizer()
      lowerCAmelCase__			:  List[Any]											= self.get_rust_tokenizer()

      lowerCAmelCase__			:  Any											= """I was born in 92000, and this is falsé."""

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

      lowerCAmelCase__			:  str											= tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= rust_tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				)
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= self.get_rust_tokenizer()
      lowerCAmelCase__			:  str											= tokenizer.encode(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= rust_tokenizer.encode(__UpperCAmelCase				)
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  str											= {"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 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_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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__			:  Optional[int]											= [
          """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  ,)








'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				'''instructblip_vision_model'''




  def __init__(   self  ,__UpperCAmelCase=1408  ,__UpperCAmelCase=6144  ,__UpperCAmelCase=39  ,__UpperCAmelCase=16  ,__UpperCAmelCase=224  ,__UpperCAmelCase=14  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=1E-6  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=1E-10  ,__UpperCAmelCase=True  ,**__UpperCAmelCase  ,)   ->		List[Any]:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Any											= hidden_size
      lowerCAmelCase__			:  List[str]											= intermediate_size
      lowerCAmelCase__			:  str											= num_hidden_layers
      lowerCAmelCase__			:  Tuple											= num_attention_heads
      lowerCAmelCase__			:  Union[str, Any]											= patch_size
      lowerCAmelCase__			:  int											= image_size
      lowerCAmelCase__			:  Tuple											= initializer_range
      lowerCAmelCase__			:  Optional[int]											= attention_dropout
      lowerCAmelCase__			:  List[str]											= layer_norm_eps
      lowerCAmelCase__			:  List[Any]											= hidden_act
      lowerCAmelCase__			:  int											= qkv_bias






  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		"PretrainedConfig":
      cls._set_token_in_kwargs(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= cls.get_config_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get("""model_type"""				) == "instructblip":
          lowerCAmelCase__			:  Optional[int]											= config_dict["""vision_config"""]

      if "model_type" in config_dict and hasattr(cls  ,"""model_type"""				) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
              F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

      return cls.from_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				int           =				'''instructblip_qformer'''




  def __init__(   self  ,__UpperCAmelCase=3_0522  ,__UpperCAmelCase=768  ,__UpperCAmelCase=12  ,__UpperCAmelCase=12  ,__UpperCAmelCase=3072  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=512  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=0  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=2  ,__UpperCAmelCase=1408  ,**__UpperCAmelCase  ,)   ->		Tuple:
      super().__init__(pad_token_id=__UpperCAmelCase  ,**__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= vocab_size
      lowerCAmelCase__			:  Optional[Any]											= hidden_size
      lowerCAmelCase__			:  Any											= num_hidden_layers
      lowerCAmelCase__			:  int											= num_attention_heads
      lowerCAmelCase__			:  Tuple											= hidden_act
      lowerCAmelCase__			:  int											= intermediate_size
      lowerCAmelCase__			:  Tuple											= hidden_dropout_prob
      lowerCAmelCase__			:  Optional[Any]											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Optional[Any]											= max_position_embeddings
      lowerCAmelCase__			:  Any											= initializer_range
      lowerCAmelCase__			:  Tuple											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= position_embedding_type
      lowerCAmelCase__			:  int											= cross_attention_frequency
      lowerCAmelCase__			:  List[Any]											= encoder_hidden_size






  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		"PretrainedConfig":
      cls._set_token_in_kwargs(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= cls.get_config_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get("""model_type"""				) == "instructblip":
          lowerCAmelCase__			:  Tuple											= config_dict["""qformer_config"""]

      if "model_type" in config_dict and hasattr(cls  ,"""model_type"""				) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
              F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

      return cls.from_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Any           =				'''instructblip'''
  __lowercase							:				str           =				True




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=32  ,**__UpperCAmelCase				)   ->		Any:
      super().__init__(**__UpperCAmelCase				)

      if vision_config is None:
          lowerCAmelCase__			:  Any											= {}
          logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""				)

      if qformer_config is None:
          lowerCAmelCase__			:  List[str]											= {}
          logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""				)

      if text_config is None:
          lowerCAmelCase__			:  List[Any]											= {}
          logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""				)

      lowerCAmelCase__			:  Any											= InstructBlipVisionConfig(**__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= InstructBlipQFormerConfig(**__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= text_config["""model_type"""] if """model_type""" in text_config else """opt"""
      lowerCAmelCase__			:  Any											= CONFIG_MAPPING[text_model_type](**__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= self.text_config.tie_word_embeddings
      lowerCAmelCase__			:  Any											= self.text_config.is_encoder_decoder

      lowerCAmelCase__			:  int											= num_query_tokens
      lowerCAmelCase__			:  List[str]											= self.vision_config.hidden_size
      lowerCAmelCase__			:  Optional[int]											= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      lowerCAmelCase__			:  Optional[Any]											= 1.0
      lowerCAmelCase__			:  Dict											= 0.0_2




  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase  ,)   ->		Union[str, Any]:
      return cls(
          vision_config=vision_config.to_dict()  ,qformer_config=qformer_config.to_dict()  ,text_config=text_config.to_dict()  ,**__UpperCAmelCase  ,)






  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  List[Any]											= copy.deepcopy(self.__dict__				)
      lowerCAmelCase__			:  str											= self.vision_config.to_dict()
      lowerCAmelCase__			:  Union[str, Any]											= self.qformer_config.to_dict()
      lowerCAmelCase__			:  Union[str, Any]											= self.text_config.to_dict()
      lowerCAmelCase__			:  str											= self.__class__.model_type
      return output


