






'''simple docstring'''





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 							__a							(  datasets.Metric						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						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       ):
						if concatenate_texts:
										return compute_measures(UpperCamelCase__ ,    UpperCamelCase__       )["wer"]
						else:
										SCREAMING_SNAKE_CASE_   :	Any													=   0
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
										for prediction, reference in zip(UpperCamelCase__ ,    UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   compute_measures(UpperCamelCase__ ,    UpperCamelCase__       )
														incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
														total += measures["substitutions"] + measures["deletions"] + measures["hits"]
										return incorrect / total







import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


A        =			logging.get_logger(__name__)



def 					_lowerCamelCase(			lowerCAmelCase__				:		np.ndarray    ,    lowerCAmelCase__				:		Union[int, Iterable[int]]    ,    lowerCAmelCase__				:		bool    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				def constraint_to_multiple_of(lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]=0    ,    lowerCAmelCase__				:		Any=None      ):
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   round(val / multiple      ) * multiple

								if max_val is not None and x > max_val:
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   math.floor(val / multiple      ) * multiple

								if x < min_val:
												SCREAMING_SNAKE_CASE_   :	Dict													=   math.ceil(val / multiple      ) * multiple

								return x

				SCREAMING_SNAKE_CASE_   :	str													=   (output_size, output_size) if isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      ) else output_size

				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   get_image_size(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   output_size

				# determine new height and width
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   output_height / input_height
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width      ) < abs(1 - scale_height      ):
												# fit width
												SCREAMING_SNAKE_CASE_   :	str													=   scale_width
								else:
												# fit height
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scale_height

				SCREAMING_SNAKE_CASE_   :	Any													=   constraint_to_multiple_of(scale_height * input_height    ,    multiple=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   constraint_to_multiple_of(scale_width * input_width    ,    multiple=lowerCAmelCase__      )

				return (new_height, new_width)


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[Any]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PILImageResampling.BILINEAR ,    UpperCamelCase__ = False ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = 1 / 255 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   size if size is not None else {'height': 384, 'width': 384}
						SCREAMING_SNAKE_CASE_   :	str													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   do_resize
						SCREAMING_SNAKE_CASE_   :	str													=   size
						SCREAMING_SNAKE_CASE_   :	int													=   keep_aspect_ratio
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ensure_multiple_of
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   resample
						SCREAMING_SNAKE_CASE_   :	int													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	int													=   rescale_factor
						SCREAMING_SNAKE_CASE_   :	str													=   do_normalize
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE_   :	int													=   image_std if image_std is not None else IMAGENET_STANDARD_STD


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = False ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = PILImageResampling.BICUBIC ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_size_dict(UpperCamelCase__       )
						if "height" not in size or "width" not in size:
										raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_resize_output_image_size(
						    UpperCamelCase__ ,    output_size=(size['height'], size['width']) ,    keep_aspect_ratio=UpperCamelCase__ ,    multiple=UpperCamelCase__ ,    )
						return resize(UpperCamelCase__ ,    size=UpperCamelCase__ ,    resample=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return rescale(UpperCamelCase__ ,    scale=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return normalize(UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   size if size is not None else self.size
						SCREAMING_SNAKE_CASE_   :	str													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						SCREAMING_SNAKE_CASE_   :	str													=   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE_   :	Dict													=   rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE_   :	Dict													=   image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   make_list_of_images(UpperCamelCase__       )

						if not valid_images(UpperCamelCase__       ):
										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 or resample is None:
										raise ValueError('Size and resample must be specified if do_resize 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.'       )

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE_   :	Any													=   [to_numpy_array(UpperCamelCase__       ) for image in images]

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

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

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

						SCREAMING_SNAKE_CASE_   :	str													=   [to_channel_dimension_format(UpperCamelCase__ ,    UpperCamelCase__       ) for image in images]

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






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
														raise ValueError(
														    'Make sure that you pass in as many target sizes as the batch dimension of the logits'       )

										if is_torch_tensor(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	int													=   target_sizes.numpy()

										SCREAMING_SNAKE_CASE_   :	Tuple													=   []

										for idx in range(len(UpperCamelCase__       )       ):
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0       ) ,    size=target_sizes[idx] ,    mode='bilinear' ,    align_corners=UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   resized_logits[0].argmax(dim=0       )
														semantic_segmentation.append(UpperCamelCase__       )
						else:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   logits.argmax(dim=1       )
										SCREAMING_SNAKE_CASE_   :	Any													=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

						return semantic_segmentation