import argparse
import os
import re


lowerCamelCase__    :		Union[str, Any]          =   """src/transformers/models/auto"""


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

def 		UpperCamelCase	(      lowercase_ , lowercase_ = False   )		->	Any:


  '''simple docstring'''






  with open(lowercase_ , """r""" , encoding="""utf-8"""   ) as f:
    lowercase__  :   Any    		=      f.read()

  lowercase__  :   Optional[int]    		=      content.split("""\n"""   )
  lowercase__  :   Dict    		=      []
  lowercase__  :   Optional[int]    		=      0
  while line_idx < len(lowercase_   ):
    if _re_intro_mapping.search(lines[line_idx]   ) is not None:
      lowercase__  :   Tuple    		=      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__  :   int    		=      []
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          lowercase__  :   str    		=      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__  :   Optional[int]    		=      sorted(lowercase_ , key=lambda lowercase_   : _re_identifier.search(lowercase_   ).groups()[0]   )
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]   )
      line_idx += 1

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

def 		UpperCamelCase	(      lowercase_ = False   )		->	int:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      [os.path.join(lowercase_ , lowercase_   ) for f in os.listdir(lowercase_   ) if f.endswith(""".py"""   )]
  lowercase__  :   int    		=      [sort_auto_mapping(lowercase_ , overwrite=lowercase_   ) for fname in fnames]

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


if __name__ == "__main__":
     lowerCamelCase__    :		Tuple          =   argparse.ArgumentParser()
     parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
     lowerCamelCase__    :		Optional[int]          =   parser.parse_args()

     sort_all_auto_mappings(not args.check_only)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






