
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     :	Tuple    = 'src/diffusers'

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


A     :	List[Any]    = '\n{0} = None\n'

A     :	Optional[Any]    = '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


A     :	Union[str, Any]    = '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


def     __lowerCAmelCase			(			a__  )       ->  Dict:
				__a										=    _re_backend.findall(a__  )
				if len(a__  ) == 0:
								return None

				return "_and_".join(a__  )


def     __lowerCAmelCase			(			)       ->  Dict:
				with open(os.path.join(a__						,    '''__init__.py'''  )						,    '''r'''						,    encoding='''utf-8'''						,    newline='''\n'''  ) as f:
								__a										=    f.readlines()

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

				return backend_specific_objects


def     __lowerCAmelCase			(			a__						,    a__  )       ->  Tuple:
				if name.isupper():
								return DUMMY_CONSTANT.format(a__  )
				elif name.islower():
								return DUMMY_FUNCTION.format(a__						,    a__  )
				else:
								return DUMMY_CLASS.format(a__						,    a__  )


def     __lowerCAmelCase			(			a__=None  )       ->  Dict:
				if backend_specific_objects is None:
								__a										=    read_init()
				# For special correspondence backend to module name as used in the function requires_modulename
				__a										=    {}

				for backend, objects in backend_specific_objects.items():
								__a										=    '''[''' + ''', '''.join(F"""\"{b}\"""" for b in backend.split('''_and_'''  )  ) + ''']'''
								__a										=    '''# 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(a__						,    a__  ) for o in objects]  )
								__a										=    dummy_file

				return dummy_files


def     __lowerCAmelCase			(			a__=False  )       ->  Union[str, Any]:
				__a										=    create_dummy_files()
				# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
				__a										=    {'''torch''': '''pt'''}

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

				__a										=    {}
				for backend, file_path in dummy_file_paths.items():
								if os.path.isfile(a__  ):
												with open(a__						,    '''r'''						,    encoding='''utf-8'''						,    newline='''\n'''  ) as f:
																__a										=    f.read()
								else:
												__a										=    ''''''

				for backend in dummy_files.keys():
								if dummy_files[backend] != actual_dummies[backend]:
												if overwrite:
																print(
																    F"""Updating diffusers.utils.dummy_{short_names.get(a__						,    a__  )}_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(a__						,    a__  )}_objects.py. Run `make fix-copies` """
																    '''to fix this.'''  )


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

					check_dummies(args.fix_and_overwrite)

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




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

								__a										=    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__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

								if text is not None:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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