

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
lowerCAmelCase__								=		'''src/diffusers'''

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


lowerCAmelCase__								=		'''
{0} = None
'''

lowerCAmelCase__								=		'''
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})
'''


lowerCAmelCase__								=		'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				List[str]:




 '''simple docstring'''




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

 return "_and_".join(_UpperCAmelCase	)






def 					lowerCamelCase_     (							)						->				List[str]:




 '''simple docstring'''




 with open(os.path.join(_UpperCAmelCase							,       '__init__.py'	)							,       'r'							,       encoding='utf-8'							,       newline='\n'	) as f:
  _UpperCamelCase   :		Optional[Any]			=						f.readlines()

 # Get to the point we do the actual imports for type checking
 _UpperCamelCase   :		List[str]			=						0
 _UpperCamelCase   :		str			=						{}
 # 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
  _UpperCamelCase   :		List[Any]			=						find_backend(lines[line_index]	)
  if backend is not None:
   while not lines[line_index].startswith('else:'	):
    line_index += 1
   line_index += 1
   _UpperCamelCase   :		Optional[Any]			=						[]
   # Until we unindent, add backend objects to the list
   while line_index < len(_UpperCAmelCase	) and len(lines[line_index]	) > 1:
    _UpperCamelCase   :		Tuple			=						lines[line_index]
    _UpperCamelCase   :		str			=						_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:
    _UpperCamelCase   :		Tuple			=						objects
  else:
   line_index += 1

 return backend_specific_objects






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Tuple	)						->				Tuple:




 '''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_							:     int=None	)						->				Union[str, Any]:




 '''simple docstring'''




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

 for backend, objects in backend_specific_objects.items():
  _UpperCamelCase   :		int			=						'[' + ', '.join(F'''"{b}"''' for b in backend.split('_and_'	)	) + ']'
  _UpperCamelCase   :		Union[str, 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(_UpperCAmelCase							,       _UpperCAmelCase	) for o in objects]	)
  _UpperCamelCase   :		Tuple			=						dummy_file

 return dummy_files






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]=False	)						->				str:




 '''simple docstring'''




 _UpperCamelCase   :		List[str]			=						create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _UpperCamelCase   :		Any			=						{'torch': 'pt'}

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

 _UpperCamelCase   :		Optional[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:
    _UpperCamelCase   :		Dict			=						f.read()
  else:
   _UpperCamelCase   :		Optional[int]			=						''

 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__":
       lowerCAmelCase__								=		argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowerCAmelCase__								=		parser.parse_args()

       check_dummies(args.fix_and_overwrite)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

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

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




