

from jiwer import compute_measures

import datasets


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

__UpperCAmelCase    				=     '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

__UpperCAmelCase    				=     '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'





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



 """simple docstring"""







 def    __magic_name__       (		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",
          ]     ,						)


 def    __magic_name__       (		self :	List[str]     ,						A_ :	List[Any]=None     ,						A_ :	List[Any]=None     ,						A_ :	List[str]=False       ):






      '''simple docstring'''




      if concatenate_texts:
           return compute_measures(UpperCAmelCase__     ,						UpperCAmelCase__       )["wer"]
      else:
           _lowerCAmelCase   :					Optional[int]        =    0
           _lowerCAmelCase   :					int        =    0
           for prediction, reference in zip(UpperCAmelCase__     ,						UpperCAmelCase__       ):
                _lowerCAmelCase   :					Any        =    compute_measures(UpperCAmelCase__     ,						UpperCAmelCase__       )
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
           return incorrect / total









import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__UpperCAmelCase    				=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class A__    ( unittest.TestCase  ,		A ):



 """simple docstring"""







 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    load_tool("text-question-answering"       )
      self.tool.setup()
      _lowerCAmelCase   :					int        =    load_tool("text-question-answering"     ,						remote=A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.remote_tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.remote_tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )






