

"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a							=							logging.get_logger(__name__)

_a							=							{
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     "pix2struct_text_model"
 __UpperCAmelCase     : Any       		=     ["past_key_values"]
 __UpperCAmelCase     : List[Any]       		=     {
     "hidden_size": "hidden_size",
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any]=5_0_2_4_4, UpperCAmelCase__							:      str=7_6_8, UpperCAmelCase__							:      Union[str, Any]=6_4, UpperCAmelCase__							:      Dict=2_0_4_8, UpperCAmelCase__							:      Optional[int]=1_2, UpperCAmelCase__							:      Optional[Any]=1_2, UpperCAmelCase__							:      Dict=3_2, UpperCAmelCase__							:      List[str]=1_2_8, UpperCAmelCase__							:      Optional[Any]=0.1, UpperCAmelCase__							:      Any=1E-6, UpperCAmelCase__							:      List[Any]=1.0, UpperCAmelCase__							:      int="gelu_new", UpperCAmelCase__							:      Optional[Any]=0, UpperCAmelCase__							:      Dict=False, UpperCAmelCase__							:      Tuple=0, UpperCAmelCase__							:      Tuple=1, UpperCAmelCase__							:      Tuple=False, UpperCAmelCase__							:      Optional[Any]=True, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						d_kv
    __lowercase								=						d_ff
    __lowercase								=						num_layers
    __lowercase								=						num_heads
    __lowercase								=						relative_attention_num_buckets
    __lowercase								=						relative_attention_max_distance
    __lowercase								=						dropout_rate
    __lowercase								=						layer_norm_epsilon
    __lowercase								=						initializer_factor
    __lowercase								=						use_cache

    __lowercase								=						eos_token_id
    __lowercase								=						decoder_start_token_id

    # for backwards compatibility
    __lowercase								=						dense_act_fn

    super().__init__(
        pad_token_id=UpperCAmelCase__, eos_token_id=UpperCAmelCase__, decoder_start_token_id=UpperCAmelCase__, tie_word_embeddings=UpperCAmelCase__, is_decoder=UpperCAmelCase__, **UpperCAmelCase__, )



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

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

    # get the text config dict if we are loading from Pix2StructConfig
    if config_dict.get("model_type"     ) == "pix2struct":
       __lowercase								=						config_dict["text_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     : Optional[int]       		=     "pix2struct_vision_model"


 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Optional[int]=7_6_8, UpperCAmelCase__							:      Tuple=7_6_8, UpperCAmelCase__							:      List[str]=2_0_4_8, UpperCAmelCase__							:      Any=6_4, UpperCAmelCase__							:      str=1_2, UpperCAmelCase__							:      Dict=1_2, UpperCAmelCase__							:      Union[str, Any]="gelu_new", UpperCAmelCase__							:      Optional[Any]=1E-6, UpperCAmelCase__							:      Dict=0.0, UpperCAmelCase__							:      Union[str, Any]=0.0, UpperCAmelCase__							:      Optional[Any]=1E-10, UpperCAmelCase__							:      List[str]=1.0, UpperCAmelCase__							:      Tuple=4_0_9_6, UpperCAmelCase__							:      str=3_2, UpperCAmelCase__							:      List[Any]=1_2_8, **UpperCAmelCase__							:      str, ):
    super().__init__(**UpperCAmelCase__     )

    __lowercase								=						hidden_size
    __lowercase								=						patch_embed_hidden_size
    __lowercase								=						d_ff
    __lowercase								=						dropout_rate
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						initializer_range
    __lowercase								=						initializer_factor
    __lowercase								=						attention_dropout
    __lowercase								=						layer_norm_eps
    __lowercase								=						dense_act_fn
    __lowercase								=						seq_len
    __lowercase								=						relative_attention_num_buckets
    __lowercase								=						relative_attention_max_distance
    __lowercase								=						d_kv



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

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

    # get the vision config dict if we are loading from Pix2StructConfig
    if config_dict.get("model_type"     ) == "pix2struct":
       __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     : int       		=     "pix2struct"
 __UpperCAmelCase     : int       		=     True


 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Any=None, UpperCAmelCase__							:      Union[str, Any]=None, UpperCAmelCase__							:      List[Any]=1.0, UpperCAmelCase__							:      int=0.02, UpperCAmelCase__							:      Any=False, UpperCAmelCase__							:      Tuple=False, UpperCAmelCase__							:      int=True, **UpperCAmelCase__							:      Any, ):
    super().__init__(tie_word_embeddings=UpperCAmelCase__, is_encoder_decoder=UpperCAmelCase__, **UpperCAmelCase__     )

    if text_config is None:
       __lowercase								=						{}
       logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."     )

    if vision_config is None:
       __lowercase								=						{}
       logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."     )

    __lowercase								=						PixaStructTextConfig(**UpperCAmelCase__     )
    __lowercase								=						PixaStructVisionConfig(**UpperCAmelCase__     )

    __lowercase								=						self.text_config.decoder_start_token_id
    __lowercase								=						self.text_config.pad_token_id
    __lowercase								=						self.text_config.eos_token_id

    __lowercase								=						initializer_factor
    __lowercase								=						initializer_range

    __lowercase								=						self.initializer_range
    __lowercase								=						self.initializer_range

    __lowercase								=						is_vqa


 @classmethod
 def  _lowercase			(      cls							:      Union[str, Any], UpperCAmelCase__							:      PixaStructTextConfig, UpperCAmelCase__							:      PixaStructVisionConfig, **UpperCAmelCase__							:      List[str]     ):
    return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **UpperCAmelCase__     )



 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						copy.deepcopy(self.__dict__     )
    __lowercase								=						self.text_config.to_dict()
    __lowercase								=						self.vision_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)




