


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


snake_case							:       int     			=      ''''''


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







						class        snake_case_   (tr.AbstractTransform      ):
											def __init__(      self   :Tuple			,__snake_case   :str = " "      )					->    List[Any]:
																		a__             =     sentence_delimiter
											def  lowerCamelCase__(      self   :Any			,__snake_case   :str      )					->    Optional[int]:
																		return list(__snake_case      )



											def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :List[str]      )					->    Optional[int]:
																		a__             =     []
																		for sent_idx, sentence in enumerate(__snake_case      ):
																									chars.extend(self.process_string(__snake_case      )      )
																									if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__snake_case      ) - 1:
																																chars.append(self.sentence_delimiter      )
																		return chars





						snake_case							:       Tuple     			=      tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
else:
						snake_case							:       str     			=      tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)


snake_case							:       List[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.}
}
'''

snake_case							:       Optional[Any]     			=      '''\
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.
'''

snake_case							:       Optional[int]     			=      '''
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        snake_case_   (datasets.Metric      ):
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												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  lowerCamelCase__(      self   :Tuple			,__snake_case   :Dict			,__snake_case   :str			,__snake_case   :Any=False      )					->    int:
												if concatenate_texts:
																			return jiwer.compute_measures(
																			    __snake_case			,__snake_case			,truth_transform=__snake_case			,hypothesis_transform=__snake_case			,)["wer"]

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

												return incorrect / total








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
												return center_crop(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

												if do_resize and size is None:
																			raise ValueError('Size must be specified if do_resize is True.'      )

												if do_center_crop and crop_size is None:
																			raise ValueError('Crop size must be specified if do_center_crop is True.'      )

												if do_rescale and rescale_factor is None:
																			raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

												# All transformations expect numpy arrays.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

												if do_resize:
																			a__             =     [self.resize(image=__snake_case			,size=__snake_case			,resample=__snake_case      ) for image in images]

												if do_center_crop:
																			a__             =     [self.center_crop(image=__snake_case			,size=__snake_case      ) for image in images]

												if do_rescale:
																			a__             =     [self.rescale(image=__snake_case			,scale=__snake_case      ) for image in images]

												if do_normalize:
																			a__             =     [self.normalize(image=__snake_case			,mean=__snake_case			,std=__snake_case      ) for image in images]

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




