





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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		["""image_processor""", """tokenizer"""]
 UpperCAmelCase__													=		"""BlipImageProcessor"""
 UpperCAmelCase__													=		"""AutoTokenizer"""
 def __init__(      self  :      int			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[Any]	)			->	List[Any]:
     super().__init__(UpperCAmelCase			,     UpperCAmelCase	)

     # add QFormer tokenizer
     lowerCamelCase__    :      int					 =		qformer_tokenizer
 def __call__(      self  :      str			,     UpperCAmelCase  :      ImageInput = None			,     UpperCAmelCase  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Union[bool, str, PaddingStrategy] = False			,     UpperCAmelCase  :      Union[bool, str, TruncationStrategy] = None			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Optional[bool] = None			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      Optional[Union[str, TensorType]] = None			,     **UpperCAmelCase  :      Tuple			,     )			->	BatchFeature:
     if images is None and text is None:
         raise ValueError('You have to specify at least images or text.'	)

     lowerCamelCase__    :      Tuple					 =		BatchFeature()

     if text is not None:
         lowerCamelCase__    :      str					 =		self.tokenizer(
             text=UpperCAmelCase			,     add_special_tokens=UpperCAmelCase			,     padding=UpperCAmelCase			,     truncation=UpperCAmelCase			,     max_length=UpperCAmelCase			,     stride=UpperCAmelCase			,     pad_to_multiple_of=UpperCAmelCase			,     return_attention_mask=UpperCAmelCase			,     return_overflowing_tokens=UpperCAmelCase			,     return_special_tokens_mask=UpperCAmelCase			,     return_offsets_mapping=UpperCAmelCase			,     return_token_type_ids=UpperCAmelCase			,     return_length=UpperCAmelCase			,     verbose=UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase			,     )
         encoding.update(UpperCAmelCase	)
         lowerCamelCase__    :      Union[str, Any]					 =		self.qformer_tokenizer(
             text=UpperCAmelCase			,     add_special_tokens=UpperCAmelCase			,     padding=UpperCAmelCase			,     truncation=UpperCAmelCase			,     max_length=UpperCAmelCase			,     stride=UpperCAmelCase			,     pad_to_multiple_of=UpperCAmelCase			,     return_attention_mask=UpperCAmelCase			,     return_overflowing_tokens=UpperCAmelCase			,     return_special_tokens_mask=UpperCAmelCase			,     return_offsets_mapping=UpperCAmelCase			,     return_token_type_ids=UpperCAmelCase			,     return_length=UpperCAmelCase			,     verbose=UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase			,     )
         lowerCamelCase__    :      Any					 =		qformer_text_encoding.pop('input_ids'	)
         lowerCamelCase__    :      List[Any]					 =		qformer_text_encoding.pop('attention_mask'	)

     if images is not None:
         lowerCamelCase__    :      Optional[Any]					 =		self.image_processor(UpperCAmelCase			,     return_tensors=UpperCAmelCase	)
         encoding.update(UpperCAmelCase	)

     return encoding
 def      A_	(      self  :      Dict			,     *UpperCAmelCase  :      str			,     **UpperCAmelCase  :      Any	)			->	int:
     return self.tokenizer.batch_decode(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]			,     *UpperCAmelCase  :      List[Any]			,     **UpperCAmelCase  :      List[Any]	)			->	int:
     return self.tokenizer.decode(*UpperCAmelCase			,     **UpperCAmelCase	)
 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def      A_	(      self  :      Dict	)			->	str:
     lowerCamelCase__    :      Union[str, Any]					 =		self.tokenizer.model_input_names
     lowerCamelCase__    :      Tuple					 =		self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      List[str]			,     **UpperCAmelCase  :      Any	)			->	Optional[int]:
     if os.path.isfile(UpperCAmelCase	):
         raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""	)
     os.makedirs(UpperCAmelCase			,     exist_ok=UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		os.path.join(UpperCAmelCase			,     'qformer_tokenizer'	)
     self.qformer_tokenizer.save_pretrained(UpperCAmelCase	)
     return super().save_pretrained(UpperCAmelCase			,     **UpperCAmelCase	)







 @classmethod
 def      A_	(      cls  :      List[Any]			,     UpperCAmelCase  :      List[Any]			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     lowerCamelCase__    :      Union[str, Any]					 =		AutoTokenizer.from_pretrained(UpperCAmelCase			,     subfolder='qformer_tokenizer'	)
     lowerCamelCase__    :      Dict					 =		cls._get_arguments_from_pretrained(UpperCAmelCase			,     **UpperCAmelCase	)
     args.append(UpperCAmelCase	)
     return cls(*UpperCAmelCase	)












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





