
import re
import string

import numpy as np

import datasets


__lowerCAmelCase			:     str           =   '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__lowerCAmelCase			:     Tuple           =   '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__lowerCAmelCase			:     List[str]           =   '\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,       _KWARGS_DESCRIPTION     )
class        UpperCAmelCase_			( datasets.Metric     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Tuple    )			->	Any:







    """simple docstring"""






    return datasets.MetricInfo(
        description=_DESCRIPTION			,							citation=_CITATION			,							inputs_description=_KWARGS_DESCRIPTION			,							features=datasets.Features(
            {
                """predictions""": datasets.Value("""string"""			,							id="""sequence"""    ),
                """references""": datasets.Value("""string"""			,							id="""sequence"""    ),
            }    )			,							reference_urls=[]			,							)





  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Any=None			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Optional[int]=False			,							UpperCamelCase__			: List[str]=False			,							)			->	Optional[Any]:







    """simple docstring"""






    if regexes_to_ignore is not None:
      for s in regexes_to_ignore:
        __magic_name__				     =       np.array([re.sub(SCREAMING_SNAKE_CASE_			,							""""""			,							SCREAMING_SNAKE_CASE_    ) for x in predictions]    )
        __magic_name__				     =       np.array([re.sub(SCREAMING_SNAKE_CASE_			,							""""""			,							SCREAMING_SNAKE_CASE_    ) for x in references]    )
    else:
      __magic_name__				     =       np.asarray(SCREAMING_SNAKE_CASE_    )
      __magic_name__				     =       np.asarray(SCREAMING_SNAKE_CASE_    )

    if ignore_case:
      __magic_name__				     =       np.char.lower(SCREAMING_SNAKE_CASE_    )
      __magic_name__				     =       np.char.lower(SCREAMING_SNAKE_CASE_    )

    if ignore_punctuation:
      __magic_name__				     =       string.punctuation.maketrans(""""""			,							""""""			,							string.punctuation    )
      __magic_name__				     =       np.char.translate(SCREAMING_SNAKE_CASE_			,							table=SCREAMING_SNAKE_CASE_    )
      __magic_name__				     =       np.char.translate(SCREAMING_SNAKE_CASE_			,							table=SCREAMING_SNAKE_CASE_    )

    if ignore_numbers:
      __magic_name__				     =       string.digits.maketrans(""""""			,							""""""			,							string.digits    )
      __magic_name__				     =       np.char.translate(SCREAMING_SNAKE_CASE_			,							table=SCREAMING_SNAKE_CASE_    )
      __magic_name__				     =       np.char.translate(SCREAMING_SNAKE_CASE_			,							table=SCREAMING_SNAKE_CASE_    )

    __magic_name__				     =       predictions == references

    return {"exact_match": np.mean(SCREAMING_SNAKE_CASE_    ) * 100}






from __future__ import annotations

from typing import Any



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Any							=	num_of_nodes
          UpperCamelCase    :  list[list[int]]							=	[]
          UpperCamelCase    :  dict[int, int]							=	{}
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          self.m_edges.append([u_node, v_node, weight]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		int:

          if self.m_component[u_node] == u_node:
             return u_node
          return self.find_component(self.m_component[u_node]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if self.m_component[u_node] != u_node:
             for k in self.m_component:
                UpperCamelCase    :  Dict							=	self.find_component(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if component_size[u_node] <= component_size[v_node]:
             UpperCamelCase    :  Tuple							=	v_node
             component_size[v_node] += component_size[u_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )

          elif component_size[u_node] >= component_size[v_node]:
             UpperCamelCase    :  Union[str, Any]							=	self.find_component(SCREAMING_SNAKE_CASE_       )
             component_size[u_node] += component_size[v_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  int							=	[]
          UpperCamelCase    :  int							=	0

          UpperCamelCase    :  list[Any]							=	[-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       )

          UpperCamelCase    :  List[str]							=	self.m_num_of_nodes

          while num_of_components > 1:
             for edge in self.m_edges:
                UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	edge

                UpperCamelCase    :  str							=	self.m_component[u]
                UpperCamelCase    :  Any							=	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
                      ):
                         UpperCamelCase    :  Any							=	[u, v, w]

             for edge in minimum_weight_edge:
                if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  int							=	edge

                   UpperCamelCase    :  List[Any]							=	self.m_component[u]
                   UpperCamelCase    :  Tuple							=	self.m_component[v]

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

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






def 		UpperCamelCase	(   )	->     None:
   pass


if __name__ == "__main__":
    import doctest

    doctest.testmod()



