



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 		snake_case						(	UpperCamelCase_  ):



   '''simple docstring'''






   snake_case_					: str          =		["""image_processor""", """tokenizer"""]
   snake_case_					: int          =		"""AutoImageProcessor"""
   snake_case_					: int          =		"""AutoTokenizer"""

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

    """simple docstring"""







    _snake_case    :  str					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		__a				,		)
     _snake_case    :  Any					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Any					=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(__a				,		__a)
    _snake_case    :  List[str]					=					self.image_processor
    _snake_case    :  int					=					False

   def __call__(					self							:			List[str]				,		*lowerCAmelCase							:			Optional[int]				,		**lowerCAmelCase							:			Union[str, Any])				->				List[Any]:

    """simple docstring"""







    if self._in_target_context_manager:
     return self.current_processor(*__a				,		**__a)

    _snake_case    :  Tuple					=					kwargs.pop("""images"""				,		__a)
    _snake_case    :  str					=					kwargs.pop("""text"""				,		__a)
    if len(__a) > 0:
     _snake_case    :  Union[str, Any]					=					args[0]
     _snake_case    :  Optional[Any]					=					args[1:]

    if images is None and text is None:
     raise ValueError("""You need to specify either an `images` or `text` input to process.""")

    if images is not None:
     _snake_case    :  int					=					self.image_processor(__a				,		*__a				,		**__a)
    if text is not None:
     _snake_case    :  Tuple					=					self.tokenizer(__a				,		**__a)

    if text is None:
     return inputs
    elif images is None:
     return encodings
    else:
     _snake_case    :  List[Any]					=					encodings['input_ids']
     return inputs

   def      UpperCamelCase_  (					self							:			str				,		*lowerCAmelCase							:			Optional[int]				,		**lowerCAmelCase							:			Tuple)				->				Tuple:

    """simple docstring"""







    return self.tokenizer.batch_decode(*__a				,		**__a)

   def      UpperCamelCase_  (					self							:			Optional[int]				,		*lowerCAmelCase							:			int				,		**lowerCAmelCase							:			List[Any])				->				int:

    """simple docstring"""







    return self.tokenizer.decode(*__a				,		**__a)

   @contextmanager
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Any:

    """simple docstring"""







    warnings.warn(
        """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
        """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
        """your images inputs, or in a separate call.""")
    _snake_case    :  List[str]					=					True
    _snake_case    :  Optional[Any]					=					self.tokenizer
    yield
    _snake_case    :  Dict					=					self.image_processor
    _snake_case    :  List[Any]					=					False

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Tuple=False				,		lowerCAmelCase							:			Tuple=None)				->				Optional[Any]:

    """simple docstring"""







    if added_vocab is None:
     _snake_case    :  int					=					self.tokenizer.get_added_vocab()

    _snake_case    :  Any					=					{}

    while tokens:
     _snake_case    :  Any					=					re.search(r"""<s_(.*?)>"""				,		__a				,		re.IGNORECASE)
     if start_token is None:
      break
     _snake_case    :  int					=					start_token.group(1)
     _snake_case    :  Tuple					=					re.search(rF'''</s_{key}>'''				,		__a				,		re.IGNORECASE)
     _snake_case    :  Any					=					start_token.group()
     if end_token is None:
      _snake_case    :  int					=					tokens.replace(__a				,		"""""")
     else:
      _snake_case    :  str					=					end_token.group()
      _snake_case    :  Optional[int]					=					re.escape(__a)
      _snake_case    :  Tuple					=					re.escape(__a)
      _snake_case    :  Optional[int]					=					re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''				,		__a				,		re.IGNORECASE)
      if content is not None:
       _snake_case    :  Union[str, Any]					=					content.group(1).strip()
       if r"<s_" in content and r"</s_" in content:  # non-leaf node
        _snake_case    :  Union[str, Any]					=					self.tokenajson(__a				,		is_inner_value=__a				,		added_vocab=__a)
        if value:
         if len(__a) == 1:
          _snake_case    :  Optional[Any]					=					value[0]
         _snake_case    :  Union[str, Any]					=					value
       else:  # leaf nodes
        _snake_case    :  Any					=					[]
        for leaf in content.split(r"""<sep/>"""):
         _snake_case    :  List[Any]					=					leaf.strip()
         if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
          _snake_case    :  Optional[Any]					=					leaf[1:-2]  # for categorical special tokens
         output[key].append(__a)
        if len(output[key]) == 1:
         _snake_case    :  Optional[Any]					=					output[key][0]

      _snake_case    :  str					=					tokens[tokens.find(__a) + len(__a) :].strip()
      if tokens[:6] == r"<sep/>":  # non-leaf nodes
       return [output] + self.tokenajson(tokens[6:]				,		is_inner_value=__a				,		added_vocab=__a)

    if len(__a):
     return [output] if is_inner_value else output
    else:
     return [] if is_inner_value else {"text_sequence": tokens}

   @property
   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		__a				,		)
    return self.image_processor_class


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,		__a				,		)
    return self.image_processor





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__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
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__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# 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(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  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(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_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(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
