
"""simple docstring"""




import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__   :					Any							=   """src/diffusers"""

# Matches is_xxx_available()
A__   :					Tuple							=   re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
A__   :					Any							=   re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


A__   :					Optional[Any]							=   """
{0} = None
"""

A__   :					List[Any]							=   """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


A__   :					Dict							=   """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     _re_backend.findall(_UpperCamelCase						)
  if len(_UpperCamelCase						) == 0:
    return None

  return "_and_".join(_UpperCamelCase						)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  with open(os.path.join(_UpperCamelCase ,				'''__init__.py'''						) ,				'''r''' ,				encoding='''utf-8''' ,				newline='''\n'''						) as f:
    _lowercase:			str       =     f.readlines()

  # Get to the point we do the actual imports for type checking
  _lowercase:			Union[str, Any]       =     0
  _lowercase:			List[Any]       =     {}
  # Go through the end of the file
  while line_index < len(_UpperCamelCase						):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    _lowercase:			Union[str, Any]       =     find_backend(lines[line_index]						)
    if backend is not None:
      while not lines[line_index].startswith('''else:'''						):
        line_index += 1
      line_index += 1
      _lowercase:			List[Any]       =     []
      # Until we unindent, add backend objects to the list
      while line_index < len(_UpperCamelCase						) and len(lines[line_index]						) > 1:
        _lowercase:			List[str]       =     lines[line_index]
        _lowercase:			int       =     _re_single_line_import.search(_UpperCamelCase						)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''						)						)
        elif line.startswith(''' ''' * 8						):
          objects.append(line[8:-2]						)
        line_index += 1

      if len(_UpperCamelCase						) > 0:
        _lowercase:			Dict       =     objects
    else:
      line_index += 1

  return backend_specific_objects




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  if name.isupper():
    return DUMMY_CONSTANT.format(_UpperCamelCase						)
  elif name.islower():
    return DUMMY_FUNCTION.format(_UpperCamelCase ,				_UpperCamelCase						)
  else:
    return DUMMY_CLASS.format(_UpperCamelCase ,				_UpperCamelCase						)




def  _lowerCAmelCase		(		_UpperCamelCase=None						):






  """simple docstring"""



  if backend_specific_objects is None:
    _lowercase:			Optional[Any]       =     read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  _lowercase:			Union[str, Any]       =     {}

  for backend, objects in backend_specific_objects.items():
    _lowercase:			Tuple       =     '''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_'''						)						) + ''']'''
    _lowercase:			str       =     '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(_UpperCamelCase ,				_UpperCamelCase						) for o in objects]						)
    _lowercase:			Optional[int]       =     dummy_file

  return dummy_files




def  _lowerCAmelCase		(		_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			Any       =     create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  _lowercase:			int       =     {'''torch''': '''pt'''}

  # Locate actual dummy modules and read their content.
  _lowercase:			Dict       =     os.path.join(_UpperCamelCase ,				'''utils'''						)
  _lowercase:			str       =     {
      backend: os.path.join(_UpperCamelCase ,				f'''dummy_{short_names.get(_UpperCamelCase ,				_UpperCamelCase						)}_objects.py'''						)
      for backend in dummy_files.keys()
  }

  _lowercase:			Union[str, Any]       =     {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(_UpperCamelCase						):
      with open(_UpperCamelCase ,				'''r''' ,				encoding='''utf-8''' ,				newline='''\n'''						) as f:
        _lowercase:			Any       =     f.read()
    else:
      _lowercase:			List[str]       =     ''''''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            f'''Updating diffusers.utils.dummy_{short_names.get(_UpperCamelCase ,				_UpperCamelCase						)}_objects.py as the main '''
            '''__init__ has new objects.'''						)
        with open(dummy_file_paths[backend] ,				'''w''' ,				encoding='''utf-8''' ,				newline='''\n'''						) as f:
          f.write(dummy_files[backend]						)
      else:
        raise ValueError(
            '''The main __init__ has objects that are not present in '''
            f'''diffusers.utils.dummy_{short_names.get(_UpperCamelCase ,				_UpperCamelCase						)}_objects.py. Run `make fix-copies` '''
            '''to fix this.'''						)


if __name__ == "__main__":
  A__   :					Any							=   argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  A__   :					Optional[int]							=   parser.parse_args()

  check_dummies(args.fix_and_overwrite)




"""simple docstring"""




import copy

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


A__   :					Dict							=   logging.get_logger(__name__)

A__   :					int							=   {
    '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 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''dpt'''
       def __init__(     self ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.0 ,				A_=0.0 ,				A_=0.02 ,				A_=1E-12 ,				A_=384 ,				A_=16 ,				A_=3 ,				A_=False ,				A_=True ,				A_=[2, 5, 8, 11] ,				A_="project" ,				A_=[4, 2, 1, 0.5] ,				A_=[96, 192, 384, 768] ,				A_=256 ,				A_=-1 ,				A_=False ,				A_=True ,				A_=0.4 ,				A_=255 ,				A_=0.1 ,				A_=[1, 1024, 24, 24] ,				A_=[0, 1] ,				A_=None ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Union[str, Any]       =     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:			Dict       =     {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }
             _lowercase:			List[str]       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             _lowercase:			str       =     backbone_config
           else:
             raise ValueError(
                 f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

           _lowercase:			Dict       =     backbone_featmap_shape
           _lowercase:			Optional[int]       =     neck_ignore_stages

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

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



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




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

         if output["backbone_config"] is not None:
           _lowercase:			List[str]       =     self.backbone_config.to_dict()

         _lowercase:			List[Any]       =     self.__class__.model_type
         return output
