"""simple docstring"""




from jiwer import compute_measures

import datasets


__lowerCamelCase											=  "\\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"

__lowerCamelCase											=  "\\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"

__lowerCamelCase											=  "\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     UpperCamelCase__(			datasets.Metric ):





						def 				snake_case__	(       self     )					->				List[str]:
													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 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=False     )					->				str:




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






"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {"vocab_file": "vocab.txt"}

__lowerCamelCase											=  {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

__lowerCamelCase											=  {
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with open(UpperCamelCase__       ,    'r'				) as f:
														A__	     =							f.read().splitlines()
														return [l.strip() for l in lines]


class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Any													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						List[str]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						List[Any]													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						Dict													=		['input_ids', 'attention_mask']





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase="<unk>"		,__UpperCAmelCase="<cls>"		,__UpperCAmelCase="<pad>"		,__UpperCAmelCase="<mask>"		,__UpperCAmelCase="<eos>"		,**__UpperCAmelCase		,)					->				Optional[int]:
													super().__init__(**__UpperCAmelCase     )
													A__	     =							load_vocab_file(__UpperCAmelCase     )
													A__	     =							dict(enumerate(self.all_tokens     )     )
													A__	     =							{tok: ind for ind, tok in enumerate(self.all_tokens     )}
													A__	     =							unk_token
													A__	     =							cls_token
													A__	     =							pad_token
													A__	     =							mask_token
													A__	     =							eos_token
													A__	     =							self.all_tokens
													self._create_trie(self.unique_no_split_tokens     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return text.split()





						def 				snake_case__	(       self		,__UpperCAmelCase=False     )					->				int:
													return len(self._id_to_token     )





						def 				snake_case__	(       self     )					->				List[str]:
													return {token: i for i, token in enumerate(self.all_tokens     )}





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:
													A__	     =							[self.cls_token_id]
													A__	     =							[self.eos_token_id]  # No sep token in ESM vocabulary
													if token_ids_a is None:
																				if self.eos_token_id is None:
																											return cls + token_ids_a
																				else:
																											return cls + token_ids_a + sep
													elif self.eos_token_id is None:
																				raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'     )
													return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False     )					->				List[int]:
													if already_has_special_tokens:
																				if token_ids_a is not None:
																											raise ValueError(
																											    'You should not supply a second sequence if the provided sequence of '
																											    'ids is already formatted with special tokens for the model.'     )

																				return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
													A__	     =							[1] + ([0] * len(__UpperCAmelCase     )) + [1]
													if token_ids_a is not None:
																				mask += [0] * len(__UpperCAmelCase     ) + [1]
													return mask





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							os.path.join(__UpperCAmelCase		,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'     )
													with open(__UpperCAmelCase		,'w'     ) as f:
																				f.write('\n'.join(self.all_tokens     )     )
													return (vocab_file,)





						@property
						def 				snake_case__	(       self     )					->				int:
													return self.get_vocab_size(with_added_tokens=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = False     )					->				int:
													return super()._add_tokens(__UpperCAmelCase		,special_tokens=__UpperCAmelCase     )





