




'''simple docstring'''

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata


a       :						Optional[int]								=		''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):



						class 		SCREAMING_SNAKE_CASE__  (			tr.AbstractTransform	):





									def __init__(      self						:    int      ,		a_						:    str = " "							):



												"""simple docstring"""




												__snake_case			=		sentence_delimiter





									def 							A (      self						:    Tuple      ,		a_						:    str							):



												"""simple docstring"""




												return list(a_							)






									def 							A (      self						:    Tuple      ,		a_						:    List[str]							):



												"""simple docstring"""




												__snake_case			=		[]
												for sent_idx, sentence in enumerate(a_							):
															chars.extend(self.process_string(a_							)							)
															if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(a_							) - 1:
																		chars.append(self.sentence_delimiter							)
												return chars







						a       :						Optional[int]								=		tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
else:
						a       :						Dict								=		tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)


a       :						str								=		'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

a       :						str								=		'''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

a       :						Any								=		'''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''



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





			def 							A (      self						:    Union[str, 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"							),
						        }							)      ,		codebase_urls=["https://github.com/jitsi/jiwer/"]      ,		reference_urls=[
						        "https://en.wikipedia.org/wiki/Word_error_rate",
						        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
						    ]      ,		)






			def 							A (      self						:    Tuple      ,		a_						:    Any      ,		a_						:    List[str]      ,		a_						:    Optional[Any]=False							):



						"""simple docstring"""




						if concatenate_texts:
									return jiwer.compute_measures(
									    a_      ,		a_      ,		truth_transform=a_      ,		hypothesis_transform=a_      ,		)["wer"]

						__snake_case			=		0
						__snake_case			=		0
						for prediction, reference in zip(a_      ,		a_							):
									__snake_case			=		jiwer.compute_measures(
									    a_      ,		a_      ,		truth_transform=a_      ,		hypothesis_transform=a_      ,		)
									incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
									total += measures["substitutions"] + measures["deletions"] + measures["hits"]

						return incorrect / total












# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
         self.adj_list[destination_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [source_vertex]
       else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   List[str]			      =  []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )
