

from jiwer import compute_measures

import datasets


_UpperCAmelCase	:     Dict		       = """\
@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.}
}
"""

_UpperCAmelCase	:     Tuple		       = """\
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.
"""

_UpperCAmelCase	:     int		       = """
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 				lowercase       ( 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',
											    ]   ,     )




				def 					a							(      self   ,     snake_case=None   ,     snake_case=None   ,     snake_case=False ):
											if concatenate_texts:
																		return compute_measures(SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_ )["wer"]
											else:
																		snake_case_           =					0
																		snake_case_           =					0
																		for prediction, reference in zip(SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_ ):
																									snake_case_           =					compute_measures(SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_ )
																									incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																									total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																		return incorrect / total







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	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




