





import warnings

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


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       int							=    ['''image_processor''', '''tokenizer''']
			__lowerCamelCase	:       Dict							=    '''ViTImageProcessor'''
			__lowerCamelCase	:       Any							=    ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






			def __init__(			self   :    int							,	__lowerCAmelCase   :    Optional[Any]=None							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    str   )	->	Optional[Any]:







					"""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."""							,	__lowerCAmelCase							,	)
							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__(__lowerCAmelCase							,	__lowerCAmelCase   )






			def __call__(			self   :    int							,	__lowerCAmelCase   :    Union[str, Any]=None							,	__lowerCAmelCase   :    Dict=None							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    Union[str, Any]=None							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Tuple:







					"""simple docstring"""






					if text is None and visual_prompt is None and images is None:
							raise ValueError("""You have to specify either text, visual prompt or images."""   )

					if text is not None and visual_prompt is not None:
							raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""   )

					if text is not None:
							A__        =   self.tokenizer(__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	**__lowerCAmelCase   )

					if visual_prompt is not None:
							A__        =   self.image_processor(__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	**__lowerCAmelCase   )

					if images is not None:
							A__        =   self.image_processor(__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	**__lowerCAmelCase   )

					if visual_prompt is not None and images is not None:
							A__        =   {
							    """pixel_values""": image_features.pixel_values,
							    """conditional_pixel_values""": prompt_features.pixel_values,
							}
							return encoding
					elif text is not None and images is not None:
							A__        =   image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					elif visual_prompt is not None:
							A__        =   {
							    """conditional_pixel_values""": prompt_features.pixel_values,
							}
							return encoding
					else:
							return BatchEncoding(data=dict(**__lowerCAmelCase   )							,	tensor_type=__lowerCAmelCase   )






			def    a_       (			self   :    str							,	*__lowerCAmelCase   :    List[Any]							,	**__lowerCAmelCase   :    Tuple   )	->	Union[str, Any]:







					"""simple docstring"""






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






			def    a_       (			self   :    List[Any]							,	*__lowerCAmelCase   :    Tuple							,	**__lowerCAmelCase   :    int   )	->	List[str]:







					"""simple docstring"""






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






			@property
			def    a_       (			self   :    Dict   )	->	List[Any]:







					"""simple docstring"""






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







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







					"""simple docstring"""






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










import argparse
from collections import defaultdict

import yaml


A    :					str =      '''docs/source/en/_toctree.yml'''







def    __lowerCamelCase					(							__a   :str    )		->				List[Any]:






		"""simple docstring"""

		A__        =   defaultdict(__a    )
		A__        =   []
		A__        =   []
		for doc in doc_list:
				if "local" in doc:
						counts[doc["local"]] += 1

				if doc["title"].lower() == "overview":
						overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}    )
				else:
						new_doc_list.append(__a    )

		A__        =   new_doc_list
		A__        =   [key for key, value in counts.items() if value > 1]

		A__        =   []
		for duplicate_key in duplicates:
				A__        =   list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}    )
				if len(__a    ) > 1:
						raise ValueError(
						    F'{duplicate_key} is present several times in the documentation table of content at '
						    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
						    """others."""    )
				# Only add this once
				new_doc.append({"""local""": duplicate_key, """title""": titles[0]}    )

		# Add none duplicate-keys
		new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]    )
		A__        =   sorted(__a    ,     key=lambda __a    : s["title"].lower()    )

		# "overview" gets special treatment and is always first
		if len(__a    ) > 1:
				raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""    )

		overview_doc.extend(__a    )

		# Sort
		return overview_doc







def    __lowerCamelCase					(							__a   :Any=False    )		->				List[str]:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf-8"""    ) as f:
				A__        =   yaml.safe_load(f.read()    )

		# Get to the API doc
		A__        =   0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A__        =   content[api_idx]["""sections"""]

		# Then to the model doc
		A__        =   0
		while api_doc[scheduler_idx]["title"] != "Schedulers":
				scheduler_idx += 1

		A__        =   api_doc[scheduler_idx]["""sections"""]
		A__        =   clean_doc_toc(__a    )

		A__        =   False
		if new_scheduler_doc != scheduler_doc:
				A__        =   True
				if overwrite:
						A__        =   new_scheduler_doc

		if diff:
				if overwrite:
						A__        =   api_doc
						with open(__a    ,     """w"""    ,     encoding="""utf-8"""    ) as f:
								f.write(yaml.dump(__a    ,     allow_unicode=__a    )    )
				else:
						raise ValueError(
						    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""    )







def    __lowerCamelCase					(							__a   :Optional[int]=False    )		->				Dict:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf-8"""    ) as f:
				A__        =   yaml.safe_load(f.read()    )

		# Get to the API doc
		A__        =   0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A__        =   content[api_idx]["""sections"""]

		# Then to the model doc
		A__        =   0
		while api_doc[pipeline_idx]["title"] != "Pipelines":
				pipeline_idx += 1

		A__        =   False
		A__        =   api_doc[pipeline_idx]["""sections"""]
		A__        =   []

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						A__        =   pipeline_doc["""section"""]
						A__        =   clean_doc_toc(__a    )
						if overwrite:
								A__        =   new_sub_pipeline_doc
				new_pipeline_docs.append(__a    )

		# sort overall pipeline doc
		A__        =   clean_doc_toc(__a    )

		if new_pipeline_docs != pipeline_docs:
				A__        =   True
				if overwrite:
						A__        =   new_pipeline_docs

		if diff:
				if overwrite:
						A__        =   api_doc
						with open(__a    ,     """w"""    ,     encoding="""utf-8"""    ) as f:
								f.write(yaml.dump(__a    ,     allow_unicode=__a    )    )
				else:
						raise ValueError(
						    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""    )


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

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)



