





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  __A(						__UpperCAmelCase ):
     __A	  =       ["image_processor", "tokenizer"]
     __A	  =       "Pix2StructImageProcessor"
     __A	  =       ("T5Tokenizer", "T5TokenizerFast")

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						False
            super().__init__(A,       A      )

     def __call__(      self,       A=None,       A = None,       A = True,       A = False,       A = None,       A = None,       A = 2048,       A = 0,       A = None,       A = None,       A = False,       A = False,       A = False,       A = False,       A = False,       A = True,       A = None,       **A,       ):





            """simple docstring"""

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

            # Get only text
            if images is None and not self.image_processor.is_vqa:
                   _UpperCamelCase              =						self.tokenizer
                   _UpperCamelCase              =						self.tokenizer(
                       text=A,       add_special_tokens=A,       padding=A,       truncation=A,       max_length=A,       stride=A,       pad_to_multiple_of=A,       return_attention_mask=A,       return_overflowing_tokens=A,       return_special_tokens_mask=A,       return_offsets_mapping=A,       return_token_type_ids=A,       return_length=A,       verbose=A,       return_tensors=A,       **A,       )
                   return text_encoding

            if not self.image_processor.is_vqa:
                   # add pixel_values
                   _UpperCamelCase              =						self.image_processor(
                       A,       return_tensors=A,       max_patches=A,       **A      )
            else:
                   # add pixel_values and bbox
                   _UpperCamelCase              =						self.image_processor(
                       A,       return_tensors=A,       max_patches=A,       header_text=A,       **A      )

            if text is not None and not self.image_processor.is_vqa:
                   _UpperCamelCase              =						self.tokenizer(
                       text=A,       add_special_tokens=A,       padding=A,       truncation=A,       max_length=A,       stride=A,       pad_to_multiple_of=A,       return_attention_mask=A,       return_overflowing_tokens=A,       return_special_tokens_mask=A,       return_offsets_mapping=A,       return_token_type_ids=A,       return_length=A,       verbose=A,       return_tensors=A,       **A,       )

                   if "attention_mask" in text_encoding:
                          _UpperCamelCase              =						text_encoding.pop('''attention_mask'''      )
                   if "input_ids" in text_encoding:
                          _UpperCamelCase              =						text_encoding.pop('''input_ids'''      )
            else:
                   _UpperCamelCase              =						None

            if text_encoding is not None:
                   encoding_image_processor.update(A      )

            return encoding_image_processor

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

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



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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













from __future__ import annotations

lowercase    : int    	=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						graph
            # mapping node to its parent in resulting breadth first tree
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						source_vertex

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{self.source_vertex}
            _UpperCamelCase              =						None
            _UpperCamelCase              =						[self.source_vertex]  # first in first out queue

            while queue:
                   _UpperCamelCase              =						queue.pop(0      )
                   for adjacent_vertex in self.graph[vertex]:
                          if adjacent_vertex not in visited:
                                 visited.add(A      )
                                 _UpperCamelCase              =						vertex
                                 queue.append(A      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if target_vertex == self.source_vertex:
                   return self.source_vertex

            _UpperCamelCase              =						self.parent.get(A      )
            if target_vertex_parent is None:
                   _UpperCamelCase              =						(
                       F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
                   )
                   raise ValueError(A      )

            return self.shortest_path(A      ) + F'''->{target_vertex}'''


if __name__ == "__main__":
       lowercase    : Tuple    	=  Graph(graph, """G""")
       g.breath_first_search()
       print(g.shortest_path("""D"""))
       print(g.shortest_path("""G"""))
       print(g.shortest_path("""Foo"""))






