

"""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 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	["image_processor", "tokenizer"]
 __UpperCAmelCase	:					List[Any]   		=	"ViltImageProcessor"
 __UpperCAmelCase	:					Optional[int]   		=	("BertTokenizer", "BertTokenizerFast")


 def __init__(			self							:			Any,					UpperCAmelCase__							:			List[Any]=None,					UpperCAmelCase__							:			Optional[int]=None,					**UpperCAmelCase__							:			str	):
    __lowercase								=						None
    if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead.",					UpperCAmelCase__,					)
       __lowercase								=						kwargs.pop("feature_extractor"	)

    __lowercase								=						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__(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						self.image_processor


 def __call__(			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, Any],					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__							:			Optional[bool] = None,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Union[str, TensorType]] = None,					**UpperCAmelCase__							:			Tuple,					):
    __lowercase								=						self.tokenizer(
        text=UpperCAmelCase__,					add_special_tokens=UpperCAmelCase__,					padding=UpperCAmelCase__,					truncation=UpperCAmelCase__,					max_length=UpperCAmelCase__,					stride=UpperCAmelCase__,					pad_to_multiple_of=UpperCAmelCase__,					return_token_type_ids=UpperCAmelCase__,					return_attention_mask=UpperCAmelCase__,					return_overflowing_tokens=UpperCAmelCase__,					return_special_tokens_mask=UpperCAmelCase__,					return_offsets_mapping=UpperCAmelCase__,					return_length=UpperCAmelCase__,					verbose=UpperCAmelCase__,					return_tensors=UpperCAmelCase__,					**UpperCAmelCase__,					)
    # add pixel_values + pixel_mask
    __lowercase								=						self.image_processor(UpperCAmelCase__,					return_tensors=UpperCAmelCase__	)
    encoding.update(UpperCAmelCase__	)

    return encoding


 def 					_lowercase (			self							:			List[str],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Any	):
    return self.tokenizer.batch_decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any],					*UpperCAmelCase__							:			Optional[int],					**UpperCAmelCase__							:			List[Any]	):
    return self.tokenizer.decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


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


 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",					UpperCAmelCase__,					)
    return self.image_processor_class



 @property
 def 					_lowercase (			self							:			Dict	):
    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",					UpperCAmelCase__,					)
    return self.image_processor







"""simple docstring"""





from __future__ import annotations

from typing import Any




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Any,					UpperCAmelCase__							:			int	):
    __lowercase								=						num_of_nodes
    __lowercase								=						[]
    __lowercase								=						{}


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):
    self.m_edges.append([u_node, v_node, weight]	)


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			int	):

    if self.m_component[u_node] == u_node:
       return u_node
    return self.find_component(self.m_component[u_node]	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			int	):

    if self.m_component[u_node] != u_node:
       for k in self.m_component:
          __lowercase								=						self.find_component(UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			list[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):

    if component_size[u_node] <= component_size[v_node]:
       __lowercase								=						v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(UpperCAmelCase__	)

    elif component_size[u_node] >= component_size[v_node]:
       __lowercase								=						self.find_component(UpperCAmelCase__	)
       component_size[u_node] += component_size[v_node]
       self.set_component(UpperCAmelCase__	)



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						[]
    __lowercase								=						0

    __lowercase								=						[-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes	):
       self.m_component.update({node: node}	)
       component_size.append(1	)

    __lowercase								=						self.m_num_of_nodes

    while num_of_components > 1:
       for edge in self.m_edges:
          __lowercase		,__lowercase		,__lowercase								=						edge

          __lowercase								=						self.m_component[u]
          __lowercase								=						self.m_component[v]

          if u_component != v_component:

             for component in (u_component, v_component):
                if (
                    minimum_weight_edge[component] == -1
                    or minimum_weight_edge[component][2] > w
                ):
                   __lowercase								=						[u, v, w]

       for edge in minimum_weight_edge:
          if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
             __lowercase		,__lowercase		,__lowercase								=						edge

             __lowercase								=						self.m_component[u]
             __lowercase								=						self.m_component[v]

             if u_component != v_component:
                mst_weight += w
                self.union(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
                print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
                num_of_components -= 1

       __lowercase								=						[-1] * self.m_num_of_nodes
    print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""	)







def   _A  (  )	->						None:
   '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()




