
'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""image_processor""", """tokenizer"""]
							__lowercase      		=       """BlipImageProcessor"""
							__lowercase      		=       """AutoTokenizer"""


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(lowerCAmelCase_    ,   lowerCAmelCase_  )

								# add QFormer tokenizer
								_snake_case   						=		qformer_tokenizer


							def __call__(       self    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 0    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								if images is None and text is None:
									raise ValueError('You have to specify at least images or text.'  )

								_snake_case   						=		BatchFeature()

								if text is not None:
									_snake_case   						=		self.tokenizer(
									    text=lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   stride=lowerCAmelCase_    ,   pad_to_multiple_of=lowerCAmelCase_    ,   return_attention_mask=lowerCAmelCase_    ,   return_overflowing_tokens=lowerCAmelCase_    ,   return_special_tokens_mask=lowerCAmelCase_    ,   return_offsets_mapping=lowerCAmelCase_    ,   return_token_type_ids=lowerCAmelCase_    ,   return_length=lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
									encoding.update(lowerCAmelCase_  )
									_snake_case   						=		self.qformer_tokenizer(
									    text=lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   stride=lowerCAmelCase_    ,   pad_to_multiple_of=lowerCAmelCase_    ,   return_attention_mask=lowerCAmelCase_    ,   return_overflowing_tokens=lowerCAmelCase_    ,   return_special_tokens_mask=lowerCAmelCase_    ,   return_offsets_mapping=lowerCAmelCase_    ,   return_token_type_ids=lowerCAmelCase_    ,   return_length=lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
									_snake_case   						=		qformer_text_encoding.pop('input_ids'  )
									_snake_case   						=		qformer_text_encoding.pop('attention_mask'  )

								if images is not None:
									_snake_case   						=		self.image_processor(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_  )
									encoding.update(lowerCAmelCase_  )

								return encoding


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								if os.path.isfile(lowerCAmelCase_  ):
									raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'  )
								os.makedirs(lowerCAmelCase_    ,   exist_ok=lowerCAmelCase_  )
								_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'qformer_tokenizer'  )
								self.qformer_tokenizer.save_pretrained(lowerCAmelCase_  )
								return super().save_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )




							@classmethod
							def  lowerCamelCase				(       cls    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   subfolder='qformer_tokenizer'  )
								_snake_case   						=		cls._get_arguments_from_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )
								args.append(lowerCAmelCase_  )
								return cls(*lowerCAmelCase_  )








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)



