





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


_a 						=      ''''''


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






			class 					__A    (    tr.AbstractTransform ):

						'''simple docstring'''







						def __init__(    self       ,  __lowerCAmelCase = " "							):



							'''simple docstring'''







							lowerCamelCase__								=		sentence_delimiter





						def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



							'''simple docstring'''







							return list(__lowerCAmelCase							)





						def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



							'''simple docstring'''







							lowerCamelCase__								=		[]
							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



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


_a 						=      '''\
@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.}
}
'''

_a 						=      '''\
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.
'''

_a 						=      '''
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 					__A    (    datasets.Metric ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







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

				lowerCamelCase__								=		0
				lowerCamelCase__								=		0
				for prediction, reference in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		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










import argparse
from collections import defaultdict

import yaml


A    :					str =      '''docs/source/en/_toctree.yml'''







def    __lowerCamelCase					(							__a   :str    )		->				List[Any]:






		"""simple docstring"""

		A__        =   defaultdict(__a    )
		A__        =   []
		A__        =   []
		for doc in doc_list:
				if "local" in doc:
						counts[doc["local"]] += 1

				if doc["title"].lower() == "overview":
						overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}    )
				else:
						new_doc_list.append(__a    )

		A__        =   new_doc_list
		A__        =   [key for key, value in counts.items() if value > 1]

		A__        =   []
		for duplicate_key in duplicates:
				A__        =   list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}    )
				if len(__a    ) > 1:
						raise ValueError(
						    F'{duplicate_key} is present several times in the documentation table of content at '
						    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
						    """others."""    )
				# Only add this once
				new_doc.append({"""local""": duplicate_key, """title""": titles[0]}    )

		# Add none duplicate-keys
		new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]    )
		A__        =   sorted(__a    ,     key=lambda __a    : s["title"].lower()    )

		# "overview" gets special treatment and is always first
		if len(__a    ) > 1:
				raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""    )

		overview_doc.extend(__a    )

		# Sort
		return overview_doc







def    __lowerCamelCase					(							__a   :Any=False    )		->				List[str]:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf-8"""    ) as f:
				A__        =   yaml.safe_load(f.read()    )

		# Get to the API doc
		A__        =   0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A__        =   content[api_idx]["""sections"""]

		# Then to the model doc
		A__        =   0
		while api_doc[scheduler_idx]["title"] != "Schedulers":
				scheduler_idx += 1

		A__        =   api_doc[scheduler_idx]["""sections"""]
		A__        =   clean_doc_toc(__a    )

		A__        =   False
		if new_scheduler_doc != scheduler_doc:
				A__        =   True
				if overwrite:
						A__        =   new_scheduler_doc

		if diff:
				if overwrite:
						A__        =   api_doc
						with open(__a    ,     """w"""    ,     encoding="""utf-8"""    ) as f:
								f.write(yaml.dump(__a    ,     allow_unicode=__a    )    )
				else:
						raise ValueError(
						    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""    )







def    __lowerCamelCase					(							__a   :Optional[int]=False    )		->				Dict:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf-8"""    ) as f:
				A__        =   yaml.safe_load(f.read()    )

		# Get to the API doc
		A__        =   0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A__        =   content[api_idx]["""sections"""]

		# Then to the model doc
		A__        =   0
		while api_doc[pipeline_idx]["title"] != "Pipelines":
				pipeline_idx += 1

		A__        =   False
		A__        =   api_doc[pipeline_idx]["""sections"""]
		A__        =   []

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						A__        =   pipeline_doc["""section"""]
						A__        =   clean_doc_toc(__a    )
						if overwrite:
								A__        =   new_sub_pipeline_doc
				new_pipeline_docs.append(__a    )

		# sort overall pipeline doc
		A__        =   clean_doc_toc(__a    )

		if new_pipeline_docs != pipeline_docs:
				A__        =   True
				if overwrite:
						A__        =   new_pipeline_docs

		if diff:
				if overwrite:
						A__        =   api_doc
						with open(__a    ,     """w"""    ,     encoding="""utf-8"""    ) as f:
								f.write(yaml.dump(__a    ,     allow_unicode=__a    )    )
				else:
						raise ValueError(
						    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""    )


if __name__ == "__main__":
					A    :					Tuple =      argparse.ArgumentParser()
					parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
					A    :					Optional[Any] =      parser.parse_args()

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)



