


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				"docs/source/en/_toctree.yml"



def        UpperCamelCase_(   lowerCamelCase_				)					->		Dict:
    _lowercase :							Optional[Any]   	=	defaultdict(lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	[]
    _lowercase :							List[Any]   	=	[]
    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(lowerCamelCase_				)

    _lowercase :							List[Any]   	=	new_doc_list
    _lowercase :							Optional[Any]   	=	[key for key, value in counts.items() if value > 1]

    _lowercase :							List[Any]   	=	[]
    for duplicate_key in duplicates:
        _lowercase :							str   	=	list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
        if len(lowerCamelCase_				) > 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]				)
    _lowercase :							Any   	=	sorted(lowerCamelCase_							,				key=lambda lowerCamelCase_				: s["title"].lower()				)

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

    overview_doc.extend(lowerCamelCase_				)

    # Sort
    return overview_doc



def        UpperCamelCase_(   lowerCamelCase_=False				)					->		List[str]:
    with open(lowerCamelCase_							,				encoding='utf-8'				) as f:
        _lowercase :							Optional[Any]   	=	yaml.safe_load(f.read()				)

    # Get to the API doc
    _lowercase :							List[str]   	=	0
    while content[api_idx]["title"] != "API":
        api_idx += 1
    _lowercase :							Any   	=	content[api_idx]['sections']

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

    _lowercase :							str   	=	api_doc[scheduler_idx]['sections']
    _lowercase :							int   	=	clean_doc_toc(lowerCamelCase_				)

    _lowercase :							List[str]   	=	False
    if new_scheduler_doc != scheduler_doc:
        _lowercase :							Optional[int]   	=	True
        if overwrite:
            _lowercase :							List[str]   	=	new_scheduler_doc

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



def        UpperCamelCase_(   lowerCamelCase_=False				)					->		List[str]:
    with open(lowerCamelCase_							,				encoding='utf-8'				) as f:
        _lowercase :							int   	=	yaml.safe_load(f.read()				)

    # Get to the API doc
    _lowercase :							str   	=	0
    while content[api_idx]["title"] != "API":
        api_idx += 1
    _lowercase :							Union[str, Any]   	=	content[api_idx]['sections']

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

    _lowercase :							Optional[Any]   	=	False
    _lowercase :							Optional[Any]   	=	api_doc[pipeline_idx]['sections']
    _lowercase :							Dict   	=	[]

    # sort sub pipeline docs
    for pipeline_doc in pipeline_docs:
        if "section" in pipeline_doc:
            _lowercase :							Union[str, Any]   	=	pipeline_doc['section']
            _lowercase :							int   	=	clean_doc_toc(lowerCamelCase_				)
            if overwrite:
                _lowercase :							List[Any]   	=	new_sub_pipeline_doc
        new_pipeline_docs.append(lowerCamelCase_				)

    # sort overall pipeline doc
    _lowercase :							List[str]   	=	clean_doc_toc(lowerCamelCase_				)

    if new_pipeline_docs != pipeline_docs:
        _lowercase :							Tuple   	=	True
        if overwrite:
            _lowercase :							Any   	=	new_pipeline_docs

    if diff:
        if overwrite:
            _lowercase :							Dict   	=	api_doc
            with open(lowerCamelCase_							,				'w'							,				encoding='utf-8'				) as f:
                f.write(yaml.dump(lowerCamelCase_							,				allow_unicode=lowerCamelCase_				)				)
        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__":
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 SCREAMING_SNAKE_CASE :   Tuple							      =				parser.parse_args()

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






import warnings

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






class _lowerCamelCase(		_a     ):
       lowercase_				:			int									=    ["""image_processor""", """tokenizer"""]
       lowercase_				:			List[str]									=    """CLIPImageProcessor"""
       lowercase_				:			Union[str, Any]									=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	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,   )
               _lowercase :							Dict   	=	kwargs.pop('feature_extractor')

           _lowercase :							int   	=	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,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Tuple:




           """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:
               _lowercase :							Optional[Any]   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if images is not None:
               _lowercase :							List[Any]   	=	self.image_processor(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if text is not None and images is not None:
               _lowercase :							int   	=	image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),   tensor_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     int:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,   **lowerCamelCase)
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	self.tokenizer.model_input_names
           _lowercase :							Dict   	=	self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """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     UpperCamelCase  (      self) ->     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


