




'''simple docstring'''



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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self,							A=None,							A=None,							A=False  ):







             '''simple docstring'''







             if concatenate_texts:
                   return compute_measures(A,							A  )["wer"]
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						0
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
                   for prediction, reference in zip(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						compute_measures(A,							A  )
                         incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                         total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                   return incorrect / total








'''simple docstring'''



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Dict        =						'''char'''
       A						:				Any        =						'''bpe'''
       A						:				Dict        =						'''wp'''

UpperCamelCase_    	=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						['''image_processor''', '''char_tokenizer''']
       A						:				int        =						'''ViTImageProcessor'''
       A						:				List[str]        =						'''MgpstrTokenizer'''







       def __init__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.',							A,							)
                   SCREAMING_SNAKE_CASE   :      str    					=						kwargs.pop('feature_extractor'  )

             SCREAMING_SNAKE_CASE   :      Optional[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`.'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoTokenizer.from_pretrained('gpt2'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoTokenizer.from_pretrained('bert-base-uncased'  )

             super().__init__(A,							A  )







       def __call__(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if images is None and text is None:
                   raise ValueError('You need to specify either an `images` or `text` input to process.'  )

             if images is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.image_processor(A,							return_tensors=A,							**A  )
             if text is not None:
                   SCREAMING_SNAKE_CASE   :      int    					=						self.char_tokenizer(A,							return_tensors=A,							**A  )

             if text is None:
                   return inputs
             elif images is None:
                   return encodings
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						encodings['input_ids']
                   return inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						sequences
             SCREAMING_SNAKE_CASE   :      List[str]    					=						char_preds.size(0  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						self._decode_helper(A,							'char'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self._decode_helper(A,							'bpe'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self._decode_helper(A,							'wp'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             for i in range(A  ):
                   SCREAMING_SNAKE_CASE   :      str    					=						[char_scores[i], bpe_scores[i], wp_scores[i]]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						[char_strs[i], bpe_strs[i], wp_strs[i]]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						scores.index(max(A  )  )
                   final_strs.append(strs[max_score_index]  )
                   final_scores.append(scores[max_score_index]  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      int    					=						final_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						final_scores
             SCREAMING_SNAKE_CASE   :      Dict    					=						char_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						bpe_strs
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						wp_strs
             return out







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if format == DecodeType.CHARACTER:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.char_decode
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						1
                   SCREAMING_SNAKE_CASE   :      str    					=						'[s]'
             elif format == DecodeType.BPE:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.bpe_decode
                   SCREAMING_SNAKE_CASE   :      str    					=						2
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						'#'
             elif format == DecodeType.WORDPIECE:
                   SCREAMING_SNAKE_CASE   :      Any    					=						self.wp_decode
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						102
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						'[SEP]'
             else:
                   raise ValueError(F"Format {format} is not supported."  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[], []
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_logits.size(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						pred_logits.size(1  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						pred_logits.topk(1,							dim=-1,							largest=A,							sorted=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_index.view(-1,							A  )[:, 1:]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						decoder(A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.nn.functional.softmax(A,							dim=2  ).max(dim=2  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						preds_max_prob[:, 1:]

             for index in range(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_str[index].find(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						preds_str[index][:pred_eos]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						preds_index[index].cpu().tolist()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_index.index(A  ) if eos_token in pred_index else -1
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_max_prob[index][: pred_eos_index + 1]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
                   dec_strs.append(A  )
                   conf_scores.append(A  )

             return dec_strs, conf_scores







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[seq.replace(' ',							''  ) for seq in self.char_tokenizer.batch_decode(A  )]
             return decode_strs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.bpe_tokenizer.batch_decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[seq.replace(' ',							''  ) for seq in self.wp_tokenizer.batch_decode(A  )]
             return decode_strs


