





from jiwer import compute_measures

import datasets


__lowercase       :Optional[Any]       =  "\\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"

__lowercase       :Union[str, Any]       =  "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (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 words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__lowercase       :Optional[Any]       =  "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word 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    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"



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






							"""simple docstring"""

							def      A_  (      self				:     Union[str, Any]    )	->Tuple:
													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				:     Optional[int]							,	a				:     Tuple=None							,	a				:     List[str]=None							,	a				:     Union[str, Any]=False    )	->int:
													if concatenate_texts:
																			return compute_measures(a							,	a    )["wer"]
													else:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					0
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
																			for prediction, reference in zip(a							,	a    ):
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					compute_measures(a							,	a    )
																									incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																									total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																			return incorrect / total






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase       :List[Any]       =  abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						config.addinivalue_line(
						    "markers"			,   "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"     )
						config.addinivalue_line(
						    "markers"			,   "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"     )
						config.addinivalue_line("markers"			,   "is_pipeline_test: mark test to run only when pipelines are tested"     )
						config.addinivalue_line("markers"			,   "is_staging_test: mark test to run only in the staging environment"     )
						config.addinivalue_line("markers"			,   "accelerate_tests: mark test that require accelerate"     )
						config.addinivalue_line("markers"			,   "tool_tests: mark the tool tests that are run on their specific schedule"     )







def       UpperCAmelCase							(     _lowerCamelCase      :  str     ):





						'''simple docstring'''


						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple     ):





						'''simple docstring'''


						from transformers.testing_utils import pytest_terminal_summary_main

						SCREAMING_SNAKE_CASE__     :				List[str]   =					terminalreporter.config.getoption("--make-reports"     )
						if make_reports:
												pytest_terminal_summary_main(_lowerCamelCase			,   id=_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						if exitstatus == 5:
												SCREAMING_SNAKE_CASE__     :				List[str]   =					0


# Doctest custom flag to ignore output.
__lowercase       :Optional[Any]       =  doctest.register_optionflag("IGNORE_RESULT")

__lowercase       :Dict       =  doctest.OutputChecker



class    _a     (				lowercase__							):






							"""simple docstring"""

							def      A_  (      self				:     Dict							,	a				:     List[str]							,	a				:     Dict							,	a				:     int    )	->Optional[Any]:
													if IGNORE_RESULT & optionflags:
																			return True
													return OutputChecker.check_output(self							,	a							,	a							,	a    )







__lowercase       :Any       =  CustomOutputChecker
__lowercase       :Any       =  HfDoctestModule



__lowercase       :int       =  HfDocTestParser