



from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


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






class SCREAMING_SNAKE_CASE__					(		snake_case__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				["""pixel_values"""]






    def __init__(	self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BILINEAR,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        super().__init__(**_A		)
        A			:	List[str]										=       size if size is not None else {"""shortest_edge""": 256}
        A			:	Union[str, Any]										=       get_size_dict(_A,		default_to_square=_A		)
        A			:	List[Any]										=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A			:	Optional[Any]										=       get_size_dict(_A,		param_name="""crop_size"""		)
        A			:	str										=       do_resize
        A			:	int										=       size
        A			:	Tuple										=       resample
        A			:	Any										=       do_center_crop
        A			:	Dict										=       crop_size
        A			:	Union[str, Any]										=       do_rescale
        A			:	int										=       rescale_factor
        A			:	List[Any]										=       do_normalize
        A			:	Optional[int]										=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A			:	str										=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A			:	int										=       get_size_dict(_A,		default_to_square=_A		)
        if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
        A			:	Tuple										=       get_resize_output_image_size(_A,		size=size["""shortest_edge"""],		default_to_square=_A		)
        return resize(_A,		size=_A,		resample=_A,		data_format=_A,		**_A		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A			:	Any										=       get_size_dict(_A		)
        if "height" not in size or "width" not in size:
            raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''		)
        return center_crop(_A,		size=(size["""height"""], size["""width"""]),		data_format=_A,		**_A		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__		):
        return rescale(_A,		scale=_A,		data_format=_A,		**_A		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return normalize(_A,		mean=_A,		std=_A,		data_format=_A,		**_A		)






    def       _lowerCAmelCase (	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,		**lowerCamelCase__,		):
        A			:	Optional[Any]										=       do_resize if do_resize is not None else self.do_resize
        A			:	int										=       size if size is not None else self.size
        A			:	List[str]										=       get_size_dict(_A,		default_to_square=_A		)
        A			:	Optional[Any]										=       resample if resample is not None else self.resample
        A			:	List[Any]										=       do_center_crop if do_center_crop is not None else self.do_center_crop
        A			:	Optional[Any]										=       crop_size if crop_size is not None else self.crop_size
        A			:	Optional[Any]										=       get_size_dict(_A,		param_name="""crop_size"""		)
        A			:	Any										=       do_rescale if do_rescale is not None else self.do_rescale
        A			:	Optional[int]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
        A			:	List[str]										=       do_normalize if do_normalize is not None else self.do_normalize
        A			:	Dict										=       image_mean if image_mean is not None else self.image_mean
        A			:	Tuple										=       image_std if image_std is not None else self.image_std

        A			:	Any										=       make_list_of_images(_A		)

        if not valid_images(_A		):
            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."""		)

        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.
        A			:	Optional[Any]										=       [to_numpy_array(_A		) for image in images]

        if do_resize:
            A			:	Optional[Any]										=       [self.resize(image=_A,		size=_A,		resample=_A		) for image in images]

        if do_center_crop:
            A			:	Any										=       [self.center_crop(image=_A,		size=_A		) for image in images]

        if do_rescale:
            A			:	Union[str, Any]										=       [self.rescale(image=_A,		scale=_A		) for image in images]

        if do_normalize:
            A			:	str										=       [self.normalize(image=_A,		mean=_A,		std=_A		) for image in images]

        A			:	Optional[int]										=       [to_channel_dimension_format(_A,		_A		) for image in images]

        A			:	Optional[Any]										=       {"""pixel_values""": images}
        return BatchFeature(data=_A,		tensor_type=_A		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	int										=       outputs.logits

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

            if is_torch_tensor(_A		):
                A			:	Union[str, Any]										=       target_sizes.numpy()

            A			:	Any										=       []

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

        return semantic_segmentation







from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE_: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.}
}
"""

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The 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.

This 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.

Word error rate can then be computed as:

WER = (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 words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

SCREAMING_SNAKE_CASE_:List[Any]					=						"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE__					(		datasets.Metric   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	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       _lowerCAmelCase (	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=False		):
        if concatenate_texts:
            return compute_measures(lowerCamelCase__,		lowerCamelCase__		)["wer"]
        else:
            A			:	str										=       0
            A			:	int										=       0
            for prediction, reference in zip(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Tuple										=       compute_measures(lowerCamelCase__,		lowerCamelCase__		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total


