




"""simple docstring"""


import warnings
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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"LayoutLMv2ImageProcessor"
 lowercase__ 					=					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


 def __init__(				self:		Tuple,      a_:		List[Any]=None,      a_:		Dict=None,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""",      __a,      )
     _snake_case							:		Dict								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Union[str, Any]								=  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__(__a,      __a		)


 def __call__(				self:		Any,      a_:		Dict,      a_:		Dict = None,      a_:		Optional[Any] = None,      a_:		int = None,      a_:		Any = None,      a_:		Dict = True,      a_:		Optional[int] = False,      a_:		Any = None,      a_:		Optional[Any] = None,      a_:		str = 0,      a_:		int = None,      a_:		Union[str, Any] = None,      a_:		List[str] = None,      a_:		List[Any] = False,      a_:		Any = False,      a_:		Any = False,      a_:		int = False,      a_:		Union[str, Any] = True,      a_:		Optional[int] = None,      **a_:		List[Any],      ):




   '''simple docstring'''



   if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes """
         """if you initialized the image processor with apply_ocr set to True."""		)

   if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""		)

   if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""		)

   # first, apply the image processor
   _snake_case							:		Tuple								=  self.image_processor(images=__a,      return_tensors=__a		)

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(__a,      __a		):
       _snake_case							:		List[Any]								=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
     _snake_case							:		Optional[Any]								=  features["""words"""]

   _snake_case							:		Tuple								=  self.tokenizer(
       text=text if text is not None else features["""words"""],      text_pair=text_pair if text_pair is not None else None,      boxes=boxes if boxes is not None else features["""boxes"""],      word_labels=__a,      add_special_tokens=__a,      padding=__a,      truncation=__a,      max_length=__a,      stride=__a,      pad_to_multiple_of=__a,      return_token_type_ids=__a,      return_attention_mask=__a,      return_overflowing_tokens=__a,      return_special_tokens_mask=__a,      return_offsets_mapping=__a,      return_length=__a,      verbose=__a,      return_tensors=__a,      **__a,      )

   # add pixel values
   _snake_case							:		str								=  features.pop("""pixel_values"""		)
   if return_overflowing_tokens is True:
     _snake_case							:		Dict								=  self.get_overflowing_images(__a,      encoded_inputs["""overflow_to_sample_mapping"""]		)
   _snake_case							:		Optional[Any]								=  images

   return encoded_inputs


 def  UpperCamelCase_   (				self:		List[Any],      a_:		Dict,      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		str								=  []
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]		)

   if len(__a		) != len(__a		):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f" {len(__a		)} and {len(__a		)}"		)

   return images_with_overflow


 def  UpperCamelCase_   (				self:		int,      *a_:		Optional[Any],      **a_:		Any		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		int,      *a_:		List[Any],      **a_:		Dict		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   return ["input_ids", "bbox", "attention_mask", "image"]


 @property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



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




"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






