

"""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


_a							=							logging.get_logger(__name__)

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




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     "instructblip_vision_model"


 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      List[str]=1_4_0_8, UpperCAmelCase__							:      List[Any]=6_1_4_4, UpperCAmelCase__							:      List[Any]=3_9, UpperCAmelCase__							:      List[str]=1_6, UpperCAmelCase__							:      str=2_2_4, UpperCAmelCase__							:      List[Any]=1_4, UpperCAmelCase__							:      Optional[Any]="gelu", UpperCAmelCase__							:      Tuple=1E-6, UpperCAmelCase__							:      Tuple=0.0, UpperCAmelCase__							:      Dict=1E-10, UpperCAmelCase__							:      Union[str, Any]=True, **UpperCAmelCase__							:      Optional[Any], ):
    super().__init__(**UpperCAmelCase__     )

    __lowercase								=						hidden_size
    __lowercase								=						intermediate_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						patch_size
    __lowercase								=						image_size
    __lowercase								=						initializer_range
    __lowercase								=						attention_dropout
    __lowercase								=						layer_norm_eps
    __lowercase								=						hidden_act
    __lowercase								=						qkv_bias



 @classmethod
 def  _lowercase			(      cls							:      Optional[Any], UpperCAmelCase__							:      Union[str, os.PathLike], **UpperCAmelCase__							:      Any     ):
    cls._set_token_in_kwargs(UpperCAmelCase__     )

    __lowercase		,__lowercase								=						cls.get_config_dict(UpperCAmelCase__, **UpperCAmelCase__     )

    # get the vision config dict if we are loading from InstructBlipConfig
    if config_dict.get("model_type"     ) == "instructblip":
       __lowercase								=						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  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[str]       		=     "instructblip_qformer"


 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      int=3_0_5_2_2, UpperCAmelCase__							:      str=7_6_8, UpperCAmelCase__							:      Dict=1_2, UpperCAmelCase__							:      int=1_2, UpperCAmelCase__							:      int=3_0_7_2, UpperCAmelCase__							:      int="gelu", UpperCAmelCase__							:      Any=0.1, UpperCAmelCase__							:      str=0.1, UpperCAmelCase__							:      List[str]=5_1_2, UpperCAmelCase__							:      Tuple=0.02, UpperCAmelCase__							:      List[Any]=1E-12, UpperCAmelCase__							:      List[Any]=0, UpperCAmelCase__							:      Union[str, Any]="absolute", UpperCAmelCase__							:      Union[str, Any]=2, UpperCAmelCase__							:      Union[str, Any]=1_4_0_8, **UpperCAmelCase__							:      Optional[int], ):
    super().__init__(pad_token_id=UpperCAmelCase__, **UpperCAmelCase__     )

    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						hidden_act
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						initializer_range
    __lowercase								=						layer_norm_eps
    __lowercase								=						position_embedding_type
    __lowercase								=						cross_attention_frequency
    __lowercase								=						encoder_hidden_size



 @classmethod
 def  _lowercase			(      cls							:      Dict, UpperCAmelCase__							:      Union[str, os.PathLike], **UpperCAmelCase__							:      Optional[int]     ):
    cls._set_token_in_kwargs(UpperCAmelCase__     )

    __lowercase		,__lowercase								=						cls.get_config_dict(UpperCAmelCase__, **UpperCAmelCase__     )

    # get the qformer config dict if we are loading from InstructBlipConfig
    if config_dict.get("model_type"     ) == "instructblip":
       __lowercase								=						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  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     "instructblip"
 __UpperCAmelCase     : str       		=     True


 def __init__(      self							:      Tuple, UpperCAmelCase__							:      Optional[Any]=None, UpperCAmelCase__							:      List[str]=None, UpperCAmelCase__							:      Dict=None, UpperCAmelCase__							:      int=3_2, **UpperCAmelCase__							:      Tuple     ):
    super().__init__(**UpperCAmelCase__     )

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

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

    if text_config is None:
       __lowercase								=						{}
       logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."     )

    __lowercase								=						InstructBlipVisionConfig(**UpperCAmelCase__     )
    __lowercase								=						InstructBlipQFormerConfig(**UpperCAmelCase__     )
    __lowercase								=						text_config["model_type"] if "model_type" in text_config else "opt"
    __lowercase								=						CONFIG_MAPPING[text_model_type](**UpperCAmelCase__     )

    __lowercase								=						self.text_config.tie_word_embeddings
    __lowercase								=						self.text_config.is_encoder_decoder

    __lowercase								=						num_query_tokens
    __lowercase								=						self.vision_config.hidden_size
    __lowercase								=						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
    __lowercase								=						1.0
    __lowercase								=						0.02


 @classmethod
 def  _lowercase			(      cls							:      Tuple, UpperCAmelCase__							:      InstructBlipVisionConfig, UpperCAmelCase__							:      InstructBlipQFormerConfig, UpperCAmelCase__							:      PretrainedConfig, **UpperCAmelCase__							:      Tuple, ):
    return cls(
        vision_config=vision_config.to_dict(), qformer_config=qformer_config.to_dict(), text_config=text_config.to_dict(), **UpperCAmelCase__, )



 def  _lowercase			(      self							:      str     ):
    __lowercase								=						copy.deepcopy(self.__dict__     )
    __lowercase								=						self.vision_config.to_dict()
    __lowercase								=						self.qformer_config.to_dict()
    __lowercase								=						self.text_config.to_dict()
    __lowercase								=						self.__class__.model_type
    return output







"""simple docstring"""





import argparse
import os
import re


_a							=							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a							=							re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_a							=							re.compile(r'\s*\(\s*"(\S[^"]+)"')




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   with open(UpperCamelCase_,      "r",      encoding="utf-8") as f:
      __lowercase								=						f.read()

   __lowercase								=						content.split("\n")
   __lowercase								=						[]
   __lowercase								=						0
   while line_idx < len(UpperCamelCase_):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         __lowercase								=						len(re.search(r"^(\s*)\S",      lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("):
            new_lines.append(lines[line_idx])
            line_idx += 1

         __lowercase								=						[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               __lowercase								=						line_idx
               while not lines[line_idx].startswith(" " * indent + ")"):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         __lowercase								=						sorted(UpperCamelCase_,      key=lambda UpperCamelCase_: _re_identifier.search(UpperCamelCase_).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(UpperCamelCase_,      "w",      encoding="utf-8") as f:
         f.write("\n".join(UpperCamelCase_))
   elif "\n".join(UpperCamelCase_) != content:
      return True




def   _A  (  UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[os.path.join(UpperCamelCase_,      UpperCamelCase_) for f in os.listdir(UpperCamelCase_) if f.endswith(".py")]
   __lowercase								=						[sort_auto_mapping(UpperCamelCase_,      overwrite=UpperCamelCase_) for fname in fnames]

   if not overwrite and any(UpperCamelCase_):
      __lowercase								=						[f for f, d in zip(UpperCamelCase_,      UpperCamelCase_) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_)}. Run `make style` to fix"""
          " this.")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _a							=							parser.parse_args()

   sort_all_auto_mappings(not args.check_only)




