'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   int					)       ->      List[Any]:

		'''simple docstring'''


		if isinstance(lowerCamelCase_ ,  (list, tuple)					) and isinstance(videos[0] ,  (list, tuple)					) and is_valid_image(videos[0][0]					):
				return videos

		elif isinstance(lowerCamelCase_ ,  (list, tuple)					) and is_valid_image(videos[0]					):
				return [videos]

		elif is_valid_image(lowerCamelCase_					):
				return [[videos]]

		raise ValueError(f"Could not make batched video from {videos}"					)






class 							a__(   A__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	int						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""shortest_edge""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					lowerCAmelCase     =    crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       param_name="""crop_size""")

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    do_center_crop
					lowerCAmelCase     =    crop_size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    offset
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "shortest_edge" in size:
							lowerCAmelCase     =    get_resize_output_image_size(__lowerCAmelCase			,       size["""shortest_edge"""]			,       default_to_square=__lowerCAmelCase)
					elif "height" in size and "width" in size:
							lowerCAmelCase     =    (size["""height"""], size["""width"""])
					else:
							raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}")
					return center_crop(__lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    image.astype(np.floataa)
					if offset:
							lowerCAmelCase     =    image - (scale / 2)
					return rescale(__lowerCAmelCase			,       scale=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return normalize(__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       ):




					"""simple docstring"""


					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample 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.""")

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

					if offset and not do_rescale:
							raise ValueError("""For offset, do_rescale must also be set to True.""")

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    to_numpy_array(__lowerCAmelCase)

					if do_resize:
							lowerCAmelCase     =    self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase)

					if do_center_crop:
							lowerCAmelCase     =    self.center_crop(__lowerCAmelCase			,       size=__lowerCAmelCase)

					if do_rescale:
							lowerCAmelCase     =    self.rescale(image=__lowerCAmelCase			,       scale=__lowerCAmelCase			,       offset=__lowerCAmelCase)

					if do_normalize:
							lowerCAmelCase     =    self.normalize(image=__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase)

					lowerCAmelCase     =    to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase)
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_center_crop if do_center_crop is not None else self.do_center_crop
					lowerCAmelCase     =    do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase     =    rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase     =    offset if offset is not None else self.offset
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase     =    image_std if image_std is not None else self.image_std

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					lowerCAmelCase     =    crop_size if crop_size is not None else self.crop_size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       param_name="""crop_size""")

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

					lowerCAmelCase     =    make_batched(__lowerCAmelCase)

					lowerCAmelCase     =    [
					    [
					        self._preprocess_image(
					            image=__lowerCAmelCase			,       do_resize=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       do_center_crop=__lowerCAmelCase			,       crop_size=__lowerCAmelCase			,       do_rescale=__lowerCAmelCase			,       rescale_factor=__lowerCAmelCase			,       offset=__lowerCAmelCase			,       do_normalize=__lowerCAmelCase			,       image_mean=__lowerCAmelCase			,       image_std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       )
					        for img in video
					    ]
					    for video in videos
					]

					lowerCAmelCase     =    {"""pixel_values""": videos}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)












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




