'''simple docstring'''







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

import datasets


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

UpperCamelCase_      	=	'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

UpperCamelCase_      	=	'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'




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




						def      __lowerCamelCase       (    self :       List[str]      )     ->				str:


													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 :       List[Any]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       int = CHRF.CHAR_ORDER							,							UpperCamelCase_ :       int = CHRF.WORD_ORDER							,							UpperCamelCase_ :       int = CHRF.BETA							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       bool = False							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :Dict						    = len(references[0]      )
													if any(len(_SCREAMING_SNAKE_CASE      ) != references_per_prediction for refs in references      ):
																				raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE      )]

													SCREAMING_SNAKE_CASE__     :int						    = CHRF(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Dict						    = sb_chrf.corpus_score(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE      )

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











"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A_						:    int				=      get_tests_dir('fixtures')
A_						:    int				=      get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A_						:    Dict				=      get_tests_dir('fixtures/dummy-config.json')
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     0





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    ).to_dict()

									config_dict.pop('feature_extractor_type'    )
									SCREAMING_SNAKE_CASE						:							int							=     WavaVecaFeatureExtractor(**_SCREAMING_SNAKE_CASE    )

									# save in new folder
									model_config.save_pretrained(_SCREAMING_SNAKE_CASE    )
									config.save_pretrained(_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )

									# make sure private variable is not incorrectly saved
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     json.loads(config.to_json_string()    )
									self.assertTrue('_processor_class' not in dict_as_saved    )

					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       'bert-base is not a local folder and is not a valid model identifier'    ):
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained('bert-base'    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	str:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       revision='aaaaaa'    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	List[Any]:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,       ):
									SCREAMING_SNAKE_CASE						:							int							=     AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	str:





					"""simple docstring"""


					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							int							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
					    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )

					# Test feature extractor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(reloaded_feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[int]:





					"""simple docstring"""


					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
													AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

									# Now that the config is registered, it can be used as any other config with the auto-API
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
													self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					class     lowerCAmelCase__						(	_lowerCamelCase ):


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE	:		str  						= True

					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# If remote code is not set, the default is to use local
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote code is disabled, we load the local one.
									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote is enabled, we load from the Hub
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(not hasattr(_SCREAMING_SNAKE_CASE     ,       'is_local'    )    )

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





