





"""simple docstring"""






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
		import tensorflow as tf

		from ..tf_utils import stable_softmax


if is_torch_available():
		import torch


A			      =	logging.get_logger(__name__)




@add_end_docstrings(
    _UpperCamelCase ,   R'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    ''' ,   )
class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if self.framework == "tf":
													__a			:    int      							=	tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
									elif self.framework == "pt":
													__a			:    List[Any]      							=	torch.nonzero(input_ids == self.tokenizer.mask_token_id						,      as_tuple=_UpperCAmelCase  )
									else:
													raise ValueError('''Unsupported framework'''  )
									return masked_index







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	self.get_masked_index(_UpperCAmelCase  )
									__a			:    Any      							=	np.prod(masked_index.shape  )
									if numel < 1:
													raise PipelineException(
													    '''fill-mask'''						,      self.model.base_model_prefix						,      f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""						,      )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ):
													for model_input in model_inputs:
																	self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]  )
									else:
													for input_ids in model_inputs["input_ids"]:
																	self._ensure_exactly_one_mask_token(_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=None						,      **_UpperCAmelCase  ):
									if return_tensors is None:
													__a			:    Tuple      							=	self.framework
									__a			:    Optional[int]      							=	self.tokenizer(_UpperCAmelCase						,      return_tensors=_UpperCAmelCase  )
									self.ensure_exactly_one_mask_token(_UpperCAmelCase  )
									return model_inputs







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    str      							=	self.model(**_UpperCAmelCase  )
									__a			:    Optional[int]      							=	model_inputs['''input_ids''']
									return model_outputs







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=5						,      _UpperCAmelCase=None  ):
									# Cap top_k if there are targets
									if target_ids is not None and target_ids.shape[0] < top_k:
													__a			:    Union[str, Any]      							=	target_ids.shape[0]
									__a			:    Optional[Any]      							=	model_outputs['''input_ids'''][0]
									__a			:    str      							=	model_outputs['''logits''']

									if self.framework == "tf":
													__a			:    Dict      							=	tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

													__a			:    List[Any]      							=	outputs.numpy()

													__a			:    int      							=	outputs[0, masked_index, :]
													__a			:    Optional[int]      							=	stable_softmax(_UpperCAmelCase						,      axis=-1  )
													if target_ids is not None:
																	__a			:    Any      							=	tf.gather_nd(tf.squeeze(_UpperCAmelCase						,      0  )						,      target_ids.reshape(-1						,      1  )  )
																	__a			:    Union[str, Any]      							=	tf.expand_dims(_UpperCAmelCase						,      0  )

													__a			:    List[str]      							=	tf.math.top_k(_UpperCAmelCase						,      k=_UpperCAmelCase  )
													__a							,    __a			:    str      							=	topk.values.numpy(), topk.indices.numpy()
									else:
													__a			:    Optional[Any]      							=	torch.nonzero(input_ids == self.tokenizer.mask_token_id						,      as_tuple=_UpperCAmelCase  ).squeeze(-1  )
													# Fill mask pipeline supports only one ${mask_token} per sample

													__a			:    int      							=	outputs[0, masked_index, :]
													__a			:    str      							=	logits.softmax(dim=-1  )
													if target_ids is not None:
																	__a			:    List[Any]      							=	probs[..., target_ids]

													__a							,    __a			:    Union[str, Any]      							=	probs.topk(_UpperCAmelCase  )

									__a			:    List[str]      							=	[]
									__a			:    Union[str, Any]      							=	values.shape[0] == 1
									for i, (_values, _predictions) in enumerate(zip(values.tolist()						,      predictions.tolist()  )  ):
													__a			:    Tuple      							=	[]
													for v, p in zip(_values						,      _predictions  ):
																	# Copy is important since we're going to modify this array in place
																	__a			:    Optional[Any]      							=	input_ids.numpy().copy()
																	if target_ids is not None:
																					__a			:    str      							=	target_ids[p].tolist()

																	__a			:    Optional[Any]      							=	p
																	# Filter padding out:
																	__a			:    Tuple      							=	tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
																	# Originally we skip special tokens to give readable output.
																	# For multi masks though, the other [MASK] would be removed otherwise
																	# making the output look odd, so we add them back
																	__a			:    Tuple      							=	self.tokenizer.decode(_UpperCAmelCase						,      skip_special_tokens=_UpperCAmelCase  )
																	__a			:    Union[str, Any]      							=	{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]  ), '''sequence''': sequence}
																	row.append(_UpperCAmelCase  )
													result.append(_UpperCAmelCase  )
									if single_mask:
													return result[0]
									return result







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=None  ):
									if isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    List[str]      							=	[targets]
									try:
													__a			:    Tuple      							=	self.tokenizer.get_vocab()
									except Exception:
													__a			:    Any      							=	{}
									__a			:    Optional[Any]      							=	[]
									for target in targets:
													__a			:    Optional[Any]      							=	vocab.get(_UpperCAmelCase						,      _UpperCAmelCase  )
													if id_ is None:
																	__a			:    Union[str, Any]      							=	self.tokenizer(
																	    _UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      return_attention_mask=_UpperCAmelCase						,      return_token_type_ids=_UpperCAmelCase						,      max_length=1						,      truncation=_UpperCAmelCase						,      )['''input_ids''']
																	if len(_UpperCAmelCase  ) == 0:
																					logger.warning(
																					    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																					    '''We cannot replace it with anything meaningful, ignoring it'''  )
																					continue
																	__a			:    List[str]      							=	input_ids[0]
																	# XXX: If users encounter this pass
																	# it becomes pretty slow, so let's make sure
																	# The warning enables them to fix the input to
																	# get faster performance.
																	logger.warning(
																	    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																	    f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`."""  )
													target_ids.append(id_  )
									__a			:    List[Any]      							=	list(set(_UpperCAmelCase  )  )
									if len(_UpperCAmelCase  ) == 0:
													raise ValueError('''At least one target must be provided when passed.'''  )
									__a			:    Optional[int]      							=	np.array(_UpperCAmelCase  )
									return target_ids







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None  ):
									__a			:    Any      							=	{}

									if targets is not None:
													__a			:    Dict      							=	self.get_target_ids(_UpperCAmelCase						,      _UpperCAmelCase  )
													__a			:    Dict      							=	target_ids

									if top_k is not None:
													__a			:    Union[str, Any]      							=	top_k

									if self.tokenizer.mask_token_id is None:
													raise PipelineException(
													    '''fill-mask'''						,      self.model.base_model_prefix						,      '''The tokenizer does not define a `mask_token`.'''  )
									return {}, {}, postprocess_params







					def __call__(    self						,      _UpperCAmelCase						,      *_UpperCAmelCase						,      **_UpperCAmelCase  ):
									__a			:    Tuple      							=	super().__call__(_UpperCAmelCase						,      **_UpperCAmelCase  )
									if isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) and len(_UpperCAmelCase  ) == 1:
													return outputs[0]
									return outputs


from jiwer import compute_measures

import datasets


A									=  '\\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'

A									=  '\\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'

A									=  '\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 SCREAMING_SNAKE_CASE	(		datasets.Metric				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     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       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


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