"""simple docstring"""






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCAmelCase       :			List[Any]   	="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

__lowerCAmelCase       :			str   	="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

__lowerCAmelCase       :			Tuple   	="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:int						)					->       Tuple:
      '''simple docstring'''


      return float((preds == labels).mean()						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int="binary"						)					->       Dict:
      '''simple docstring'''


      lowercase								=      simple_accuracy(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      float(fa_score(y_true=lowerCAmelCase__   ,						y_pred=lowerCAmelCase__   ,						average=lowerCAmelCase__						)						)
      return {
          "accuracy": acc,
          "f1": fa,
      }


def      UpperCAmelCase__			(							lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Dict						)					->       Optional[int]:
      '''simple docstring'''


      lowercase								=      {}
      for id_pred, label in zip(lowerCAmelCase__   ,						lowerCAmelCase__						):
            lowercase								=      f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
            lowercase								=      id_pred["""prediction"""]
            if question_id in question_map:
                  question_map[question_id].append((pred, label)						)
            else:
                  lowercase								=      [(pred, label)]
      lowercase				,				lowercase								=      [], []
      for question, preds_labels in question_map.items():
            lowercase				,				lowercase								=      zip(*lowerCAmelCase__						)
            lowercase								=      fa_score(y_true=lowerCAmelCase__   ,						y_pred=lowerCAmelCase__   ,						average="""macro"""						)
            fas.append(lowerCAmelCase__						)
            lowercase								=      int(sum(pred == label for pred, label in preds_labels						) == len(lowerCAmelCase__						)						)
            ems.append(lowerCAmelCase__						)
      lowercase								=      float(sum(lowerCAmelCase__						) / len(lowerCAmelCase__						)						)
      lowercase								=      sum(lowerCAmelCase__						) / len(lowerCAmelCase__						)
      lowercase								=      float(fa_score(y_true=lowerCAmelCase__   ,						y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]						)						)
      return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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

      def 							A__			(		self		):







            """simple docstring"""



            if self.config_name not in [
                "boolq",
                "cb",
                "copa",
                "multirc",
                "record",
                "rte",
                "wic",
                "wsc",
                "wsc.fixed",
                "axb",
                "axg",
            ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)
            return datasets.MetricInfo(
                description=_DESCRIPTION						,			citation=_CITATION						,			inputs_description=_KWARGS_DESCRIPTION						,			features=datasets.Features(self._get_feature_types()		)						,			codebase_urls=[]						,			reference_urls=[]						,			format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None						,			)

      def 							A__			(		self		):







            """simple docstring"""



            if self.config_name == "record":
                  return {
                      "predictions": {
                          "idx": {
                              "passage": datasets.Value("""int64"""		),
                              "query": datasets.Value("""int64"""		),
                          },
                          "prediction_text": datasets.Value("""string"""		),
                      },
                      "references": {
                          "idx": {
                              "passage": datasets.Value("""int64"""		),
                              "query": datasets.Value("""int64"""		),
                          },
                          "answers": datasets.Sequence(datasets.Value("""string"""		)		),
                      },
                  }
            elif self.config_name == "multirc":
                  return {
                      "predictions": {
                          "idx": {
                              "answer": datasets.Value("""int64"""		),
                              "paragraph": datasets.Value("""int64"""		),
                              "question": datasets.Value("""int64"""		),
                          },
                          "prediction": datasets.Value("""int64"""		),
                      },
                      "references": datasets.Value("""int64"""		),
                  }
            else:
                  return {
                      "predictions": datasets.Value("""int64"""		),
                      "references": datasets.Value("""int64"""		),
                  }







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if self.config_name == "axb":
                  return {"matthews_correlation": matthews_corrcoef(__lowerCAmelCase						,			__lowerCAmelCase		)}
            elif self.config_name == "cb":
                  return acc_and_fa(__lowerCAmelCase						,			__lowerCAmelCase						,			fa_avg="""macro"""		)
            elif self.config_name == "record":
                  lowercase								=      [
                      {
                          """qas""": [
                              {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                              for ref in references
                          ]
                      }
                  ]
                  lowercase								=      {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
                  return evaluate_record(__lowerCAmelCase						,			__lowerCAmelCase		)[0]
            elif self.config_name == "multirc":
                  return evaluate_multirc(__lowerCAmelCase						,			__lowerCAmelCase		)
            elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                  return {"accuracy": simple_accuracy(__lowerCAmelCase						,			__lowerCAmelCase		)}
            else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)




"""simple docstring"""






import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__lowerCAmelCase       :			str   	=get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures/vocab.json""")
__lowerCAmelCase       :			Tuple   	=get_tests_dir("""fixtures""")





class 					_A		(			unittest.TestCase      ):
      snake_case__     :  List[Any]				    =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      0

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)
            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig()
                  lowercase								=      AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  # save in new folder
                  model_config.save_pretrained(__lowerCAmelCase		)
                  processor.save_pretrained(__lowerCAmelCase		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)		)
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

                  # drop `processor_class` in tokenizer
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""r"""		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                        config_dict.pop("""processor_class"""		)

                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write(json.dumps(__lowerCAmelCase		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaFeatureExtractor()
                  lowercase								=      AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

                  lowercase								=      WavaVecaProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # save in new folder
                  processor.save_pretrained(__lowerCAmelCase		)

                  # drop `processor_class` in feature extractor
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""r"""		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                        config_dict.pop("""processor_class"""		)

                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write(json.dumps(__lowerCAmelCase		)		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      WavaVecaConfig(processor_class="""Wav2Vec2Processor"""		)
                  model_config.save_pretrained(__lowerCAmelCase		)
                  # copy relevant files
                  copyfile(__lowerCAmelCase						,			os.path.join(__lowerCAmelCase						,			"""vocab.json"""		)		)
                  # create emtpy sample processor
                  with open(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			"""w"""		) as f:
                        f.write("""{}"""		)

                  lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)

            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(__lowerCAmelCase		):
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)

            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
            self.assertTrue(processor.special_attribute_present		)
            self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)

            lowercase								=      processor.feature_extractor
            self.assertTrue(feature_extractor.special_attribute_present		)
            self.assertEqual(feature_extractor.__class__.__name__						,			"""NewFeatureExtractor"""		)

            lowercase								=      processor.tokenizer
            self.assertTrue(tokenizer.special_attribute_present		)
            if is_tokenizers_available():
                  self.assertEqual(tokenizer.__class__.__name__						,			"""NewTokenizerFast"""		)

                  # Test we can also load the slow version
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase						,			use_fast=__lowerCAmelCase		)
                  lowercase								=      new_processor.tokenizer
                  self.assertTrue(new_tokenizer.special_attribute_present		)
                  self.assertEqual(new_tokenizer.__class__.__name__						,			"""NewTokenizer"""		)
            else:
                  self.assertEqual(tokenizer.__class__.__name__						,			"""NewTokenizer"""		)

      def 							A__			(		self		):







            """simple docstring"""



            try:
                  AutoConfig.register("""custom"""						,			__lowerCAmelCase		)
                  AutoFeatureExtractor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  AutoTokenizer.register(__lowerCAmelCase						,			slow_tokenizer_class=__lowerCAmelCase		)
                  AutoProcessor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  # Trying to register something existing in the Transformers library will raise an error
                  with self.assertRaises(__lowerCAmelCase		):
                        AutoProcessor.register(__lowerCAmelCase						,			__lowerCAmelCase		)

                  # Now that the config is registered, it can be used as any other config with the auto-API
                  lowercase								=      CustomFeatureExtractor.from_pretrained(__lowerCAmelCase		)

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        lowercase								=      os.path.join(__lowerCAmelCase						,			"""vocab.txt"""		)
                        with open(__lowerCAmelCase						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                              vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]		)		)
                        lowercase								=      CustomTokenizer(__lowerCAmelCase		)

                  lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        processor.save_pretrained(__lowerCAmelCase		)
                        lowercase								=      AutoProcessor.from_pretrained(__lowerCAmelCase		)
                        self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

            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]
                  if CustomConfig in TOKENIZER_MAPPING._extra_content:
                        del TOKENIZER_MAPPING._extra_content[CustomConfig]
                  if CustomConfig in PROCESSOR_MAPPING._extra_content:
                        del PROCESSOR_MAPPING._extra_content[CustomConfig]

      def 							A__			(		self		):







            """simple docstring"""



            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  Optional[Any]				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  int				    =      False






            class 					_A		(			lowerCAmelCase      ):
                  snake_case__     :  List[Any]				    =      'AutoFeatureExtractor'
                  snake_case__     :  List[str]				    =      'AutoTokenizer'
                  snake_case__     :  List[str]				    =      False

            try:
                  AutoConfig.register("""custom"""						,			__lowerCAmelCase		)
                  AutoFeatureExtractor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  AutoTokenizer.register(__lowerCAmelCase						,			slow_tokenizer_class=__lowerCAmelCase		)
                  AutoProcessor.register(__lowerCAmelCase						,			__lowerCAmelCase		)
                  # If remote code is not set, the default is to use local classes.
                  lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertFalse(processor.special_attribute_present		)
                  self.assertFalse(processor.feature_extractor.special_attribute_present		)
                  self.assertFalse(processor.tokenizer.special_attribute_present		)

                  # If remote code is disabled, we load the local ones.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertFalse(processor.special_attribute_present		)
                  self.assertFalse(processor.feature_extractor.special_attribute_present		)
                  self.assertFalse(processor.tokenizer.special_attribute_present		)

                  # If remote is enabled, we load from the Hub.
                  lowercase								=      AutoProcessor.from_pretrained(
                      """hf-internal-testing/test_dynamic_processor"""						,			trust_remote_code=__lowerCAmelCase		)
                  self.assertEqual(processor.__class__.__name__						,			"""NewProcessor"""		)
                  self.assertTrue(processor.special_attribute_present		)
                  self.assertTrue(processor.feature_extractor.special_attribute_present		)
                  self.assertTrue(processor.tokenizer.special_attribute_present		)

            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]
                  if CustomConfig in TOKENIZER_MAPPING._extra_content:
                        del TOKENIZER_MAPPING._extra_content[CustomConfig]
                  if CustomConfig in PROCESSOR_MAPPING._extra_content:
                        del PROCESSOR_MAPPING._extra_content[CustomConfig]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
            self.assertEqual(processor.__class__.__name__						,			"""BertTokenizerFast"""		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""		)
            self.assertEqual(processor.__class__.__name__						,			"""ConvNextImageProcessor"""		)






@is_staging_test
class 					_A		(			unittest.TestCase      ):
      snake_case__     :  Dict				    =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            lowercase								=      TOKEN
            HfFolder.save_token(__lowerCAmelCase		)

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            try:
                  delete_repo(token=cls._token						,			repo_id="""test-processor"""		)
            except HTTPError:
                  pass

            try:
                  delete_repo(token=cls._token						,			repo_id="""valid_org/test-processor-org"""		)
            except HTTPError:
                  pass

            try:
                  delete_repo(token=cls._token						,			repo_id="""test-dynamic-processor"""		)
            except HTTPError:
                  pass

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)
            with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(
                      os.path.join(__lowerCAmelCase						,			"""test-processor"""		)						,			push_to_hub=__lowerCAmelCase						,			use_auth_token=self._token		)

                  lowercase								=      WavaVecaProcessor.from_pretrained(f'{USER}/test-processor'		)
                  for k, v in processor.feature_extractor.__dict__.items():
                        self.assertEqual(__lowerCAmelCase						,			getattr(new_processor.feature_extractor						,			__lowerCAmelCase		)		)
                  self.assertDictEqual(new_processor.tokenizer.get_vocab()						,			processor.tokenizer.get_vocab()		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessor.from_pretrained(__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(
                      os.path.join(__lowerCAmelCase						,			"""test-processor-org"""		)						,			push_to_hub=__lowerCAmelCase						,			use_auth_token=self._token						,			organization="""valid_org"""						,			)

                  lowercase								=      WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""		)
                  for k, v in processor.feature_extractor.__dict__.items():
                        self.assertEqual(__lowerCAmelCase						,			getattr(new_processor.feature_extractor						,			__lowerCAmelCase		)		)
                  self.assertDictEqual(new_processor.tokenizer.get_vocab()						,			processor.tokenizer.get_vocab()		)







      def 							A__			(		self		):







            """simple docstring"""



            CustomFeatureExtractor.register_for_auto_class()
            CustomTokenizer.register_for_auto_class()
            CustomProcessor.register_for_auto_class()

            lowercase								=      CustomFeatureExtractor.from_pretrained(__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase								=      os.path.join(__lowerCAmelCase						,			"""vocab.txt"""		)
                  with open(__lowerCAmelCase						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                        vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]		)		)
                  lowercase								=      CustomTokenizer(__lowerCAmelCase		)

            lowercase								=      CustomProcessor(__lowerCAmelCase						,			__lowerCAmelCase		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  create_repo(f'{USER}/test-dynamic-processor'						,			token=self._token		)
                  lowercase								=      Repository(__lowerCAmelCase						,			clone_from=f'{USER}/test-dynamic-processor'						,			token=self._token		)
                  processor.save_pretrained(__lowerCAmelCase		)

                  # This has added the proper auto_map field to the feature extractor config
                  self.assertDictEqual(
                      processor.feature_extractor.auto_map						,			{
                          """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
                          """AutoProcessor""": """custom_processing.CustomProcessor""",
                      }						,			)

                  # This has added the proper auto_map field to the tokenizer config
                  with open(os.path.join(__lowerCAmelCase						,			"""tokenizer_config.json"""		)		) as f:
                        lowercase								=      json.load(__lowerCAmelCase		)
                  self.assertDictEqual(
                      tokenizer_config["""auto_map"""]						,			{
                          """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
                          """AutoProcessor""": """custom_processing.CustomProcessor""",
                      }						,			)

                  # The code has been copied from fixtures
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_feature_extraction.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_tokenization.py"""		)		)		)
                  self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase						,			"""custom_processing.py"""		)		)		)

                  repo.push_to_hub()

            lowercase								=      AutoProcessor.from_pretrained(f'{USER}/test-dynamic-processor'						,			trust_remote_code=__lowerCAmelCase		)
            # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
            self.assertEqual(new_processor.__class__.__name__						,			"""CustomProcessor"""		)



