

'''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_				  =							get_tests_dir("fixtures")
A_				  =							get_tests_dir("fixtures/dummy_feature_extractor_config.json")
A_				  =							get_tests_dir("fixtures/dummy-config.json")





class 							_snake_case       (				unittest.TestCase  ):
   def 			__UpperCamelCase  (       self	:					int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    0
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE:Any  					=    AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"							)
     self.assertIsInstance(__UpperCAmelCase   ,__UpperCAmelCase							)
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple  					=    AutoFeatureExtractor.from_pretrained(__UpperCAmelCase							)
     self.assertIsInstance(__UpperCAmelCase   ,__UpperCAmelCase							)
   def 			__UpperCamelCase  (       self	:					List[str]							):
     with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    WavaVecaConfig()

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

       config_dict.pop("feature_extractor_type"							)
       SCREAMING_SNAKE_CASE:List[Any]  					=    WavaVecaFeatureExtractor(**__UpperCAmelCase							)

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

       SCREAMING_SNAKE_CASE:Dict  					=    AutoFeatureExtractor.from_pretrained(__UpperCAmelCase							)

       # make sure private variable is not incorrectly saved
       SCREAMING_SNAKE_CASE:Dict  					=    json.loads(config.to_json_string()							)
       self.assertTrue("_processor_class" not in dict_as_saved							)

     self.assertIsInstance(__UpperCAmelCase   ,__UpperCAmelCase							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    AutoFeatureExtractor.from_pretrained(__UpperCAmelCase							)
     self.assertIsInstance(__UpperCAmelCase   ,__UpperCAmelCase							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     with self.assertRaisesRegex(
         __UpperCAmelCase   ,"bert-base is not a local folder and is not a valid model identifier"							):
       SCREAMING_SNAKE_CASE:str  					=    AutoFeatureExtractor.from_pretrained("bert-base"							)
   def 			__UpperCamelCase  (       self	:					Tuple							):
     with self.assertRaisesRegex(
         __UpperCAmelCase   ,R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"							):
       SCREAMING_SNAKE_CASE:str  					=    AutoFeatureExtractor.from_pretrained(__UpperCAmelCase   ,revision="aaaaaa"							)
   def 			__UpperCamelCase  (       self	:					str							):
     with self.assertRaisesRegex(
         __UpperCAmelCase   ,"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."   ,):
       SCREAMING_SNAKE_CASE:List[Any]  					=    AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"							)
   def 			__UpperCamelCase  (       self	:					str							):
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(__UpperCAmelCase							):
       SCREAMING_SNAKE_CASE:int  					=    AutoFeatureExtractor.from_pretrained(
           "hf-internal-testing/test_dynamic_feature_extractor"							)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(__UpperCAmelCase							):
       SCREAMING_SNAKE_CASE:str  					=    AutoFeatureExtractor.from_pretrained(
           "hf-internal-testing/test_dynamic_feature_extractor"   ,trust_remote_code=__UpperCAmelCase							)

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

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(__UpperCAmelCase							)
       SCREAMING_SNAKE_CASE:Any  					=    AutoFeatureExtractor.from_pretrained(__UpperCAmelCase   ,trust_remote_code=__UpperCAmelCase							)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__   ,"NewFeatureExtractor"							)
   def 			__UpperCamelCase  (       self	:					str							):
     try:
       AutoConfig.register("custom"   ,__UpperCAmelCase							)
       AutoFeatureExtractor.register(__UpperCAmelCase   ,__UpperCAmelCase							)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(__UpperCAmelCase							):
         AutoFeatureExtractor.register(__UpperCAmelCase   ,__UpperCAmelCase							)

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

     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 			__UpperCamelCase  (       self	:					Optional[Any]							):





     class 							_snake_case       (				__A  ):
        _A				:   str			      =    True

     try:
       AutoConfig.register("custom"   ,__UpperCAmelCase							)
       AutoFeatureExtractor.register(__UpperCAmelCase   ,__UpperCAmelCase							)
       # If remote code is not set, the default is to use local
       SCREAMING_SNAKE_CASE:Tuple  					=    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:Any  					=    AutoFeatureExtractor.from_pretrained(
           "hf-internal-testing/test_dynamic_feature_extractor"   ,trust_remote_code=__UpperCAmelCase							)
       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:Dict  					=    AutoFeatureExtractor.from_pretrained(
           "hf-internal-testing/test_dynamic_feature_extractor"   ,trust_remote_code=__UpperCAmelCase							)
       self.assertEqual(feature_extractor.__class__.__name__   ,"NewFeatureExtractor"							)
       self.assertTrue(not hasattr(__UpperCAmelCase   ,"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]







"""simple docstring"""




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

import datasets


__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\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 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							len(references[0]   )
													if any(len(__UpperCAmelCase   ) != references_per_prediction for refs in references   ):
																				raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
													A__		   =							[[refs[i] for refs in references] for i in range(__UpperCAmelCase   )]

													A__		   =							CHRF(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							sb_chrf.corpus_score(__UpperCAmelCase						,__UpperCAmelCase   )

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





