


'''simple docstring'''




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


_a   :   Tuple												=   logging.get_logger(__name__)




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	["pixel_values"]
   def __init__(						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Optional[Dict[str, int]] = None       ,   SCREAMING_SNAKE_CASE_       :      PILImageResampling = PILImageResampling.BICUBIC       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Union[int, float] = 1 / 255       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Dict       ,   ) ->							None:
        super().__init__(**SCREAMING_SNAKE_CASE_      )
        __snake_case           =						size if size is not None else {'height': 224, 'width': 224}
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						crop_size if crop_size is not None else {'height': 224, 'width': 224}
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_       ,   default_to_square=SCREAMING_SNAKE_CASE_       ,   param_name='crop_size'      )

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

        if "shortest_edge" in size:
             __snake_case           =						get_resize_output_image_size(SCREAMING_SNAKE_CASE_       ,   size=size['shortest_edge']       ,   default_to_square=SCREAMING_SNAKE_CASE_      )
             # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
             __snake_case           =						(size['height'], size['width'])
        else:
             raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
        return resize(SCREAMING_SNAKE_CASE_       ,   size=SCREAMING_SNAKE_CASE_       ,   resample=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   ) ->							np.ndarray:
        __snake_case           =						get_size_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_       ,   size=(size['height'], size['width'])       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      float       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							np.ndarray:
        return rescale(SCREAMING_SNAKE_CASE_       ,   scale=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      Union[float, List[float]]       ,   SCREAMING_SNAKE_CASE_       :      Union[float, List[float]]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      List[Any]       ,   ) ->							np.ndarray:
        return normalize(SCREAMING_SNAKE_CASE_       ,   mean=SCREAMING_SNAKE_CASE_       ,   std=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )





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

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

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

        if not valid_images(SCREAMING_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.
        __snake_case           =						[to_numpy_array(SCREAMING_SNAKE_CASE_      ) for image in images]

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

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

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

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

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

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












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\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__      :     Optional[int]       =					'\\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__      :     Dict       =					'\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      ):

				"""simple docstring"""




				def 							lowercase__  (	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 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

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




