'''simple docstring'''




from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    return DownloadCommand(args.model				,  args.cache_dir				,  args.force				,  args.trust_remote_code				)




class     a__      (   _SCREAMING_SNAKE_CASE ):






     @staticmethod
     def    SCREAMING_SNAKE_CASE__   ( a		:		ArgumentParser    ):





         """simple docstring"""



         __lowerCamelCase		=  parser.add_parser('''download'''    )
         download_parser.add_argument(
             '''--cache-dir'''  ,				type=a  ,				default=a  ,				help='''Path to location to store the models'''    )
         download_parser.add_argument(
             '''--force'''  ,				action='''store_true'''  ,				help='''Force the model to be download even if already in cache-dir'''    )
         download_parser.add_argument(
             '''--trust-remote-code'''  ,				action='''store_true'''  ,				help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''  ,				)
         download_parser.add_argument('''model'''  ,				type=a  ,				help='''Name of the model to download'''    )
         download_parser.set_defaults(func=a    )






     def __init__( self		:		Union[str, Any]  ,				a		:		str  ,				a		:		str  ,				a		:		bool  ,				a		:		bool    ):





         """simple docstring"""



         __lowerCamelCase		=  model
         __lowerCamelCase		=  cache
         __lowerCamelCase		=  force
         __lowerCamelCase		=  trust_remote_code






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         from ..models.auto import AutoModel, AutoTokenizer

         AutoModel.from_pretrained(
             self._model  ,				cache_dir=self._cache  ,				force_download=self._force  ,				trust_remote_code=self._trust_remote_code    )
         AutoTokenizer.from_pretrained(
             self._model  ,				cache_dir=self._cache  ,				force_download=self._force  ,				trust_remote_code=self._trust_remote_code    )




'''simple docstring'''






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

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

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__A					:  str											=      get_tests_dir("fixtures")






class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  int		)   ->  List[str]:
      # A mock response for an HTTP head request to emulate server down
      lowerCAmelCase_			:      str			=   mock.Mock()
      lowerCAmelCase_			:      Optional[Any]			=   5_00
      lowerCAmelCase_			:      List[str]			=   {}
      lowerCAmelCase_			:      str			=   HTTPError
      lowerCAmelCase_			:      Any			=   {}

      # Download this model to make sure it's in the cache.
      lowerCAmelCase_			:      Optional[Any]			=   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch("""requests.Session.request"""    ,						return_value=lowerCamelCase		) as mock_head:
        lowerCAmelCase_			:      Union[str, Any]			=   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
        # This check we did call the fake head request
        mock_head.assert_called()





    def __lowercase   (   self    :  Dict		)   ->  Any:
      # This test is for deprecated behavior and can be removed in v5
      lowerCAmelCase_			:      List[str]			=   WavaVecaFeatureExtractor.from_pretrained(
          """https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"""		)







@is_staging_test
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    @classmethod
    def __lowercase   (   cls    :  Tuple		)   ->  str:
      lowerCAmelCase_			:      Dict			=   TOKEN
      HfFolder.save_token(lowerCamelCase		)

    @classmethod
    def __lowercase   (   cls    :  Any		)   ->  Any:
      try:
        delete_repo(token=cls._token    ,						repo_id="""test-feature-extractor"""		)
      except HTTPError:
        pass

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

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

    def __lowercase   (   self    :  int		)   ->  str:
      lowerCAmelCase_			:      Tuple			=   WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase		)
      feature_extractor.push_to_hub("""test-feature-extractor"""    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[Any]			=   WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

      # Reset repo
      delete_repo(token=self._token    ,						repo_id="""test-feature-extractor"""		)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
        feature_extractor.save_pretrained(
            lowerCamelCase    ,						repo_id="""test-feature-extractor"""    ,						push_to_hub=lowerCamelCase    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Any			=   WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

    def __lowercase   (   self    :  Optional[Any]		)   ->  int:
      lowerCAmelCase_			:      Union[str, Any]			=   WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase		)
      feature_extractor.push_to_hub("""valid_org/test-feature-extractor"""    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[int]			=   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor"""		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

      # Reset repo
      delete_repo(token=self._token    ,						repo_id="""valid_org/test-feature-extractor"""		)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
        feature_extractor.save_pretrained(
            lowerCamelCase    ,						repo_id="""valid_org/test-feature-extractor-org"""    ,						push_to_hub=lowerCamelCase    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[int]			=   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org"""		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)





    def __lowercase   (   self    :  Optional[Any]		)   ->  Any:
      CustomFeatureExtractor.register_for_auto_class()
      lowerCAmelCase_			:      Dict			=   CustomFeatureExtractor.from_pretrained(lowerCamelCase		)

      feature_extractor.push_to_hub("""test-dynamic-feature-extractor"""    ,						use_auth_token=self._token		)

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

      lowerCAmelCase_			:      Union[str, Any]			=   AutoFeatureExtractor.from_pretrained(
          F'{USER}/test-dynamic-feature-extractor'    ,						trust_remote_code=lowerCamelCase		)
      # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
      self.assertEqual(new_feature_extractor.__class__.__name__    ,						"""CustomFeatureExtractor"""		)



