






"""simple docstring"""




import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					a__				(       snake_case__ ,				snake_case__       )		->      np.array:
			lowerCamelCase       =      F'{sampling_rate}'
			lowerCamelCase       =      '''1'''
			lowerCamelCase       =      '''f32le'''
			lowerCamelCase       =      [
			    '''ffmpeg''',
			    '''-i''',
			    '''pipe:0''',
			    '''-ac''',
			    ac,
			    '''-ar''',
			    ar,
			    '''-f''',
			    format_for_conversion,
			    '''-hide_banner''',
			    '''-loglevel''',
			    '''quiet''',
			    '''pipe:1''',
			]

			try:
						with subprocess.Popen(a_ ,				stdin=subprocess.PIPE ,				stdout=subprocess.PIPE       ) as ffmpeg_process:
									lowerCamelCase       =      ffmpeg_process.communicate(a_       )
			except FileNotFoundError as error:
						raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""       ) from error
			lowerCamelCase       =      output_stream[0]
			lowerCamelCase       =      np.frombuffer(a_ ,				np.floataa       )
			if audio.shape[0] == 0:
						raise ValueError("""Malformed soundfile"""       )
			return audio



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ = "f32le" ,				)		->      Dict:
			lowerCamelCase       =      F'{sampling_rate}'
			lowerCamelCase       =      '''1'''
			if format_for_conversion == "s16le":
						lowerCamelCase       =      2
			elif format_for_conversion == "f32le":
						lowerCamelCase       =      4
			else:
						raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

			lowerCamelCase       =      platform.system()
			if system == "Linux":
						lowerCamelCase       =      '''alsa'''
						lowerCamelCase       =      '''default'''
			elif system == "Darwin":
						lowerCamelCase       =      '''avfoundation'''
						lowerCamelCase       =      ''':0'''
			elif system == "Windows":
						lowerCamelCase       =      '''dshow'''
						lowerCamelCase       =      '''default'''

			lowerCamelCase       =      [
			    '''ffmpeg''',
			    '''-f''',
			    format_,
			    '''-i''',
			    input_,
			    '''-ac''',
			    ac,
			    '''-ar''',
			    ar,
			    '''-f''',
			    format_for_conversion,
			    '''-fflags''',
			    '''nobuffer''',
			    '''-hide_banner''',
			    '''-loglevel''',
			    '''quiet''',
			    '''pipe:1''',
			]
			lowerCamelCase       =      int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
			lowerCamelCase       =      _ffmpeg_stream(a_ ,				a_       )
			for item in iterator:
						yield item



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = "f32le" ,				)		->      Optional[Any]:
			if stream_chunk_s is not None:
						lowerCamelCase       =      stream_chunk_s
			else:
						lowerCamelCase       =      chunk_length_s

			lowerCamelCase       =      ffmpeg_microphone(a_ ,				a_ ,				format_for_conversion=a_       )
			if format_for_conversion == "s16le":
						lowerCamelCase       =      np.intaa
						lowerCamelCase       =      2
			elif format_for_conversion == "f32le":
						lowerCamelCase       =      np.floataa
						lowerCamelCase       =      4
			else:
						raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

			if stride_length_s is None:
						lowerCamelCase       =      chunk_length_s / 6
			lowerCamelCase       =      int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
			if isinstance(a_ ,				(int, float)       ):
						lowerCamelCase       =      [stride_length_s, stride_length_s]

			lowerCamelCase       =      int(round(sampling_rate * stride_length_s[0]       )       ) * size_of_sample
			lowerCamelCase       =      int(round(sampling_rate * stride_length_s[1]       )       ) * size_of_sample
			lowerCamelCase       =      datetime.datetime.now()
			lowerCamelCase       =      datetime.timedelta(seconds=a_       )
			for item in chunk_bytes_iter(a_ ,				a_ ,				stride=(stride_left, stride_right) ,				stream=a_       ):
						# Put everything back in numpy scale
						lowerCamelCase       =      np.frombuffer(item["""raw"""] ,				dtype=a_       )
						lowerCamelCase       =      (
						    item['''stride'''][0] // size_of_sample,
						    item['''stride'''][1] // size_of_sample,
						)
						lowerCamelCase       =      sampling_rate
						audio_time += delta
						if datetime.datetime.now() > audio_time + 10 * delta:
									# We're late !! SKIP
									continue
						yield item



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ = False       )		->      Any:
			lowerCamelCase       =      b''''''
			lowerCamelCase       =      stride
			if stride_left + stride_right >= chunk_len:
						raise ValueError(
						    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'       )
			lowerCamelCase       =      0
			for raw in iterator:
						acc += raw
						if stream and len(a_       ) < chunk_len:
									lowerCamelCase       =      (_stride_left, 0)
									yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
						else:
									while len(a_       ) >= chunk_len:
												# We are flushing the accumulator
												lowerCamelCase       =      (_stride_left, stride_right)
												lowerCamelCase       =      {'''raw''': acc[:chunk_len], '''stride''': stride}
												if stream:
															lowerCamelCase       =      False
												yield item
												lowerCamelCase       =      stride_left
												lowerCamelCase       =      acc[chunk_len - stride_left - stride_right :]
    # Last chunk
			if len(a_       ) > stride_left:
						lowerCamelCase       =      {'''raw''': acc, '''stride''': (_stride_left, 0)}
						if stream:
									lowerCamelCase       =      False
						yield item



def 					a__				(       snake_case__ ,				snake_case__       )		->      int:
			lowerCamelCase       =      2**24  # 16Mo
			try:
						with subprocess.Popen(a_ ,				stdout=subprocess.PIPE ,				bufsize=a_       ) as ffmpeg_process:
									while True:
												lowerCamelCase       =      ffmpeg_process.stdout.read(a_       )
												if raw == b"":
															break
												yield raw
			except FileNotFoundError as error:
						raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""       ) from error









"""simple docstring"""




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase							:    List[Any]  							=							"""\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowerCAmelCase							:    List[str]  							=							"""\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

lowerCAmelCase							:    str  							=							"""
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""






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






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
										raise ImportWarning(
										    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
										    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,				citation=_CITATION	,				homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""	,				inputs_description=_KWARGS_DESCRIPTION	,				features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""	,				id="""sequence"""				),
							            """references""": datasets.Sequence(datasets.Value("""string"""	,				id="""sequence"""				)	,				id="""references"""				),
							        }				)	,				codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]	,				reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ]	,				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = CHRF.CHAR_ORDER	,				_a = CHRF.WORD_ORDER	,				_a = CHRF.BETA	,				_a = False	,				_a = False	,				_a = False	,				):





							"""simple docstring"""
							lowerCamelCase       =      len(references[0]				)
							if any(len(_a				) != references_per_prediction for refs in references				):
										raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							lowerCamelCase       =      [[refs[i] for refs in references] for i in range(_a				)]

							lowerCamelCase       =      CHRF(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)
							lowerCamelCase       =      sb_chrf.corpus_score(_a	,				_a				)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}



