

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


__UpperCAmelCase								=     logging.get_logger(__name__)

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



class SCREAMING_SNAKE_CASE     (  _a			):
  """simple docstring"""






  lowerCamelCase   :      str										="""instructblip_vision_model"""







  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   str=14_08	,							lowerCAmelCase			:   str=61_44	,							lowerCAmelCase			:   List[Any]=39	,							lowerCAmelCase			:   str=16	,							lowerCAmelCase			:   Optional[int]=2_24	,							lowerCAmelCase			:   Dict=14	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   int=1e-6	,							lowerCAmelCase			:   Union[str, Any]=0.0	,							lowerCAmelCase			:   Tuple=1e-10	,							lowerCAmelCase			:   List[str]=True	,							**lowerCAmelCase			:   str	,							)							->     Optional[Any]:






     """simple docstring"""







     super().__init__(**__lowerCamelCase   )

     __lowerCAmelCase  :      Dict    =						hidden_size
     __lowerCAmelCase  :      List[Any]    =						intermediate_size
     __lowerCAmelCase  :      List[Any]    =						num_hidden_layers
     __lowerCAmelCase  :      Tuple    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						patch_size
     __lowerCAmelCase  :      Optional[int]    =						image_size
     __lowerCAmelCase  :      Any    =						initializer_range
     __lowerCAmelCase  :      Any    =						attention_dropout
     __lowerCAmelCase  :      Dict    =						layer_norm_eps
     __lowerCAmelCase  :      int    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						qkv_bias







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, os.PathLike]	,							**lowerCAmelCase			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     cls._set_token_in_kwargs(__lowerCamelCase   )

     __lowerCAmelCase  :      str    =						cls.get_config_dict(__lowerCamelCase	,							**__lowerCamelCase   )

     # get the vision config dict if we are loading from InstructBlipConfig
     if config_dict.get("""model_type"""   ) == "instructblip":
        __lowerCAmelCase  :      Optional[Any]    =						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(__lowerCamelCase	,							**__lowerCamelCase   )






class SCREAMING_SNAKE_CASE     (  _a			):
  """simple docstring"""






  lowerCamelCase   :      str										="""instructblip_qformer"""







  def __init__(      self			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]=3_05_22	,							lowerCAmelCase			:   Dict=7_68	,							lowerCAmelCase			:   int=12	,							lowerCAmelCase			:   str=12	,							lowerCAmelCase			:   Optional[Any]=30_72	,							lowerCAmelCase			:   List[Any]="gelu"	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   Any=0.1	,							lowerCAmelCase			:   Union[str, Any]=5_12	,							lowerCAmelCase			:   List[Any]=0.02	,							lowerCAmelCase			:   Optional[int]=1e-12	,							lowerCAmelCase			:   Tuple=0	,							lowerCAmelCase			:   int="absolute"	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   str=14_08	,							**lowerCAmelCase			:   Tuple	,							)							->     str:






     """simple docstring"""







     super().__init__(pad_token_id=__lowerCamelCase	,							**__lowerCamelCase   )

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







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Union[str, os.PathLike]	,							**lowerCAmelCase			:   Any   )							->     List[str]:






     """simple docstring"""







     cls._set_token_in_kwargs(__lowerCamelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls.get_config_dict(__lowerCamelCase	,							**__lowerCamelCase   )

     # get the qformer config dict if we are loading from InstructBlipConfig
     if config_dict.get("""model_type"""   ) == "instructblip":
        __lowerCAmelCase  :      Optional[Any]    =						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(__lowerCamelCase	,							**__lowerCamelCase   )






class SCREAMING_SNAKE_CASE     (  _a			):
  """simple docstring"""






  lowerCamelCase   :      Tuple										="""instructblip"""
  lowerCamelCase   :      Optional[Any]										=True







  def __init__(      self			:   Any	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Dict=32	,							**lowerCAmelCase			:   int   )							->     int:






     """simple docstring"""







     super().__init__(**__lowerCamelCase   )

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

     if qformer_config is None:
        __lowerCAmelCase  :      Dict    =						{}
        logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""   )

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

     __lowerCAmelCase  :      Dict    =						InstructBlipVisionConfig(**__lowerCamelCase   )
     __lowerCAmelCase  :      str    =						InstructBlipQFormerConfig(**__lowerCamelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						text_config["""model_type"""] if """model_type""" in text_config else """opt"""
     __lowerCAmelCase  :      Optional[int]    =						CONFIG_MAPPING[text_model_type](**__lowerCamelCase   )

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

     __lowerCAmelCase  :      List[str]    =						num_query_tokens
     __lowerCAmelCase  :      Any    =						self.vision_config.hidden_size
     __lowerCAmelCase  :      int    =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     __lowerCAmelCase  :      Optional[Any]    =						1.0
     __lowerCAmelCase  :      Optional[int]    =						0.02







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Union[str, Any]	,							lowerCAmelCase			:   InstructBlipVisionConfig	,							lowerCAmelCase			:   InstructBlipQFormerConfig	,							lowerCAmelCase			:   PretrainedConfig	,							**lowerCAmelCase			:   List[Any]	,							)							->     List[str]:






     """simple docstring"""







     return cls(
         vision_config=vision_config.to_dict()	,							qformer_config=qformer_config.to_dict()	,							text_config=text_config.to_dict()	,							**__lowerCamelCase	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						copy.deepcopy(self.__dict__   )
     __lowerCAmelCase  :      Tuple    =						self.vision_config.to_dict()
     __lowerCAmelCase  :      int    =						self.qformer_config.to_dict()
     __lowerCAmelCase  :      Union[str, Any]    =						self.text_config.to_dict()
     __lowerCAmelCase  :      Any    =						self.__class__.model_type
     return output





__UpperCAmelCase								=			"""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

__UpperCAmelCase								=			[{"""type""": """code""", """content""": INSTALL_CONTENT}]
__UpperCAmelCase								=			{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


