






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Any      =						'''data2vec-vision'''
 def __init__(							self			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=12			,     __lowerCAmelCase=12			,     __lowerCAmelCase=30_72			,     __lowerCAmelCase="gelu"			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1E-12			,     __lowerCAmelCase=2_24			,     __lowerCAmelCase=16			,     __lowerCAmelCase=3			,     __lowerCAmelCase=False			,     __lowerCAmelCase=False			,     __lowerCAmelCase=False			,     __lowerCAmelCase=False			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=True			,     __lowerCAmelCase=[3, 5, 7, 11]			,     __lowerCAmelCase=[1, 2, 3, 6]			,     __lowerCAmelCase=True			,     __lowerCAmelCase=0.4			,     __lowerCAmelCase=2_56			,     __lowerCAmelCase=1			,     __lowerCAmelCase=False			,     __lowerCAmelCase=2_55			,     **__lowerCAmelCase			,     ):
       super().__init__(**__lowerCAmelCase )

       UpperCamelCase_				:   Optional[Any]					    =							hidden_size
       UpperCamelCase_				:   str					    =							num_hidden_layers
       UpperCamelCase_				:   int					    =							num_attention_heads
       UpperCamelCase_				:   List[str]					    =							intermediate_size
       UpperCamelCase_				:   str					    =							hidden_act
       UpperCamelCase_				:   Any					    =							hidden_dropout_prob
       UpperCamelCase_				:   List[Any]					    =							attention_probs_dropout_prob
       UpperCamelCase_				:   List[str]					    =							initializer_range
       UpperCamelCase_				:   Any					    =							layer_norm_eps

       UpperCamelCase_				:   Dict					    =							image_size
       UpperCamelCase_				:   Any					    =							patch_size
       UpperCamelCase_				:   Union[str, Any]					    =							num_channels
       UpperCamelCase_				:   Dict					    =							use_mask_token
       UpperCamelCase_				:   Union[str, Any]					    =							use_absolute_position_embeddings
       UpperCamelCase_				:   Tuple					    =							use_relative_position_bias
       UpperCamelCase_				:   List[str]					    =							use_shared_relative_position_bias
       UpperCamelCase_				:   int					    =							layer_scale_init_value
       UpperCamelCase_				:   Dict					    =							drop_path_rate
       UpperCamelCase_				:   str					    =							use_mean_pooling
       # decode head attributes (semantic segmentation)
       UpperCamelCase_				:   Dict					    =							out_indices
       UpperCamelCase_				:   Dict					    =							pool_scales
       # auxiliary head attributes (semantic segmentation)
       UpperCamelCase_				:   str					    =							use_auxiliary_head
       UpperCamelCase_				:   Dict					    =							auxiliary_loss_weight
       UpperCamelCase_				:   str					    =							auxiliary_channels
       UpperCamelCase_				:   int					    =							auxiliary_num_convs
       UpperCamelCase_				:   Dict					    =							auxiliary_concat_input
       UpperCamelCase_				:   Optional[Any]					    =							semantic_loss_ignore_index






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[int]      =						version.parse('''1.11'''						)
 @property
 def   _UpperCAmelCase       (							self ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ] )





 @property
 def   _UpperCAmelCase       (							self ):
       return 1E-4














'''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


UpperCamelCase							=""


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




      class     A    (      tr.AbstractTransform						):






       """simple docstring"""





       def __init__(							self			,     __lowerCAmelCase = " " ):
             UpperCamelCase_				:   str					    =							sentence_delimiter
       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             return list(__lowerCAmelCase )





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






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


UpperCamelCase							="\\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"

UpperCamelCase							="\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (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 characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER'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\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCamelCase							="\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character 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    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




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






 """simple docstring"""





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

       UpperCamelCase_				:   Optional[Any]					    =							0
       UpperCamelCase_				:   str					    =							0
       for prediction, reference in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Optional[int]					    =							jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total






