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


__lowerCamelCase      :			Tuple							       =		''''''


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







   class __snake_case    (						tr.AbstractTransform		):




      def __init__(   self      :    int	,     _lowercase      :    str = " "				):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__      							=				sentence_delimiter




      def      __a						(   self      :    Dict	,     _lowercase      :    str				):



        """simple docstring"""






        return list(_lowercase				)




      def      __a						(   self      :    List[str]	,     _lowercase      :    List[str]				):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__      							=				[]
        for sent_idx, sentence in enumerate(_lowercase				):
          chars.extend(self.process_string(_lowercase				)				)
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_lowercase				) - 1:
            chars.append(self.sentence_delimiter				)
        return chars





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


__lowerCamelCase      :			int							       =		'''\
@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.}
}
'''

__lowerCamelCase      :			List[str]							       =		'''\
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.
'''

__lowerCamelCase      :			str							       =		'''
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      __a						(   self      :    str				):



     """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""",
             """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
         ]	,     )




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Tuple	,     _lowercase      :    Any	,     _lowercase      :    Optional[Any]=False				):



     """simple docstring"""






     if concatenate_texts:
       return jiwer.compute_measures(
           _lowercase	,     _lowercase	,     truth_transform=_lowercase	,     hypothesis_transform=_lowercase	,     )["wer"]

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

     return incorrect / total

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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     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()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

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

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       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_rescale and rescale_factor is None:
       raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

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

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

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

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

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

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

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

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

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

     return semantic_segmentation
