





"""simple docstring"""






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_			=							''''''


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



    class 	lowerCAmelCase					(      tr.AbstractTransform							):






           def __init__(   self    ,  a__ = " "     ):
                  _UpperCAmelCase     =     sentence_delimiter






           def 							__A (   self    ,  a__     ):
                  return list(a__     )






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


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


lowerCAmelCase_			=							'''\
@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_			=							'''\
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_			=							'''
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 	lowerCAmelCase					(      datasets.Metric							):






       def 							__A (   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',
                      'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
                  ]    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__=False     ):
              if concatenate_texts:
                     return jiwer.compute_measures(
                         a__    ,  a__    ,  truth_transform=a__    ,  hypothesis_transform=a__    ,  )["wer"]

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

              return incorrect / total













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
    from PIL import Image



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  (
           """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
           """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
           """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
       )
       lowerCAmelCase__						 =  """CIDAS/clipseg-rd64-refined"""
       lowerCAmelCase__						 =  """image_segmenter"""
       lowerCAmelCase__						 =  CLIPSegForImageSegmentation

       lowerCAmelCase__						 =  ["""image""", """text"""]
       lowerCAmelCase__						 =  ["""image"""]






       def __init__(   self    ,  *a__    ,  **a__     ):
              requires_backends(self    ,  ['vision']     )
              super().__init__(*a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__     ):
              return self.pre_processor(text=[label]    ,  images=[image]    ,  padding=a__    ,  return_tensors='pt'     )






       def 							__A (   self    ,  a__     ):
              with torch.no_grad():
                     _UpperCAmelCase     =     self.model(**a__     ).logits
              return logits






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     outputs.cpu().detach().numpy()
              _UpperCAmelCase     =     0
              _UpperCAmelCase     =     1
              return Image.fromarray((array * 2_55).astype(np.uinta     )     )






