




import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       UpperCAmelCase_									=	'platform'
       import jax
       import jax.numpy as jnp
       import numpy as np

       from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class     lowerCamelCase__:
      UpperCAmelCase__      :	str    		=   PegasusConfig
      UpperCAmelCase__      :	List[Any]    		=   {}
      UpperCAmelCase__      :	Dict    		=   'gelu'
      def __init__(				self: Dict    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Optional[Any]=13    ,       UpperCamelCase_: List[Any]=7    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Optional[int]=False    ,       UpperCamelCase_: List[Any]=99    ,       UpperCamelCase_: Union[str, Any]=32    ,       UpperCamelCase_: str=5    ,       UpperCamelCase_: Optional[Any]=4    ,       UpperCamelCase_: List[str]=37    ,       UpperCamelCase_: Optional[Any]=0.1    ,       UpperCamelCase_: str=0.1    ,       UpperCamelCase_: Tuple=20    ,       UpperCamelCase_: Optional[int]=2    ,       UpperCamelCase_: str=1    ,       UpperCamelCase_: Optional[int]=0    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_labels
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						hidden_size
             __lowerCamelCase   =						num_hidden_layers
             __lowerCamelCase   =						num_attention_heads
             __lowerCamelCase   =						intermediate_size

             __lowerCamelCase   =						hidden_dropout_prob
             __lowerCamelCase   =						attention_probs_dropout_prob
             __lowerCamelCase   =						max_position_embeddings
             __lowerCamelCase   =						eos_token_id
             __lowerCamelCase   =						pad_token_id
             __lowerCamelCase   =						bos_token_id
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size   ).clip(3    ,       self.vocab_size   )
             __lowerCamelCase   =						np.expand_dims(np.array([self.eos_token_id] * self.batch_size   )    ,       1   )
             __lowerCamelCase   =						np.concatenate([input_ids, eos_tensor]    ,       axis=1   )

             __lowerCamelCase   =						ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size   )

             __lowerCamelCase   =						self.config_cls(
                 vocab_size=self.vocab_size    ,       d_model=self.hidden_size    ,       encoder_layers=self.num_hidden_layers    ,       decoder_layers=self.num_hidden_layers    ,       encoder_attention_heads=self.num_attention_heads    ,       decoder_attention_heads=self.num_attention_heads    ,       encoder_ffn_dim=self.intermediate_size    ,       decoder_ffn_dim=self.intermediate_size    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       eos_token_ids=[2]    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       decoder_start_token_id=self.pad_token_id    ,       **self.config_updates    ,       )
             __lowerCamelCase   =						prepare_pegasus_inputs_dict(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             return config, inputs_dict
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						20
             __lowerCamelCase   =						model_class_name(UpperCamelCase_   )

             __lowerCamelCase   =						model.encode(inputs_dict["""input_ids"""]   )

             __lowerCamelCase,   __lowerCamelCase   =						(
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             __lowerCamelCase   =						model.init_cache(decoder_input_ids.shape[0]    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             __lowerCamelCase   =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,       dtype="""i4"""   )

             __lowerCamelCase   =						jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )
             __lowerCamelCase   =						model.decode(
                 decoder_input_ids[:, :-1]    ,       UpperCamelCase_    ,       decoder_attention_mask=UpperCamelCase_    ,       past_key_values=UpperCamelCase_    ,       decoder_position_ids=UpperCamelCase_    ,       )

             __lowerCamelCase   =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype="""i4"""   )
             __lowerCamelCase   =						model.decode(
                 decoder_input_ids[:, -1:]    ,       UpperCamelCase_    ,       decoder_attention_mask=UpperCamelCase_    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_position_ids=UpperCamelCase_    ,       )

             __lowerCamelCase   =						model.decode(UpperCamelCase_    ,       UpperCamelCase_   )

             __lowerCamelCase   =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
             self.parent.assertTrue(diff < 1E-3    ,       msg=F'Max diff is {diff}'   )




      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						20
             __lowerCamelCase   =						model_class_name(UpperCamelCase_   )

             __lowerCamelCase   =						model.encode(inputs_dict["""input_ids"""]   )

             __lowerCamelCase,   __lowerCamelCase   =						(
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             __lowerCamelCase   =						jnp.concatenate(
                 [
                     decoder_attention_mask,
                     jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
                 ]    ,       axis=-1    ,       )

             __lowerCamelCase   =						model.init_cache(decoder_input_ids.shape[0]    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             __lowerCamelCase   =						jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )

             __lowerCamelCase   =						model.decode(
                 decoder_input_ids[:, :-1]    ,       UpperCamelCase_    ,       decoder_attention_mask=UpperCamelCase_    ,       past_key_values=UpperCamelCase_    ,       decoder_position_ids=UpperCamelCase_    ,       )
             __lowerCamelCase   =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype="""i4"""   )
             __lowerCamelCase   =						model.decode(
                 decoder_input_ids[:, -1:]    ,       UpperCamelCase_    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_attention_mask=UpperCamelCase_    ,       decoder_position_ids=UpperCamelCase_    ,       )

             __lowerCamelCase   =						model.decode(UpperCamelCase_    ,       UpperCamelCase_    ,       decoder_attention_mask=UpperCamelCase_   )

             __lowerCamelCase   =						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
             self.parent.assertTrue(diff < 1E-3    ,       msg=F'Max diff is {diff}'   )




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Tuple  ,			A__       :					str  ,			A__       :					Tuple=None  ,			A__       :					Tuple=None  ,			):





       '''simple docstring'''



       if attention_mask is None:
              __lowerCamelCase   =						np.not_equal(A__  ,			config.pad_token_id	).astype(np.inta	)
       if decoder_attention_mask is None:
              __lowerCamelCase   =						np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape  ,			dtype=np.inta	),
                      np.not_equal(decoder_input_ids[:, 1:]  ,			config.pad_token_id	).astype(np.inta	),
                  ]  ,			axis=-1  ,			)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }





@require_flax
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	int    		=   (
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      UpperCAmelCase__      :	int    		=   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      UpperCAmelCase__      :	Dict    		=   True
      UpperCAmelCase__      :	List[str]    		=   False
      UpperCAmelCase__      :	Dict    		=   False
      UpperCAmelCase__      :	Dict    		=   False
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						FlaxPegasusModelTester(self   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             self.config_tester.run_common_tests()
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__   ):
                           __lowerCamelCase   =						self._prepare_for_class(UpperCamelCase_    ,       UpperCamelCase_   )
                           __lowerCamelCase   =						model_class(UpperCamelCase_   )

                           @jax.jit
                           def encode_jitted(UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Any]=None    ,       **UpperCamelCase_: Dict   ):
                                  return model.encode(input_ids=UpperCamelCase_    ,       attention_mask=UpperCamelCase_   )

                           with self.subTest("""JIT Enabled"""   ):
                                  __lowerCamelCase   =						encode_jitted(**UpperCamelCase_   ).to_tuple()

                           with self.subTest("""JIT Disabled"""   ):
                                  with jax.disable_jit():
                                         __lowerCamelCase   =						encode_jitted(**UpperCamelCase_   ).to_tuple()

                           self.assertEqual(len(UpperCamelCase_   )    ,       len(UpperCamelCase_   )   )
                           for jitted_output, output in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                                  self.assertEqual(jitted_output.shape    ,       output.shape   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__   ):
                           __lowerCamelCase   =						model_class(UpperCamelCase_   )
                           __lowerCamelCase   =						model.encode(inputs_dict["""input_ids"""]    ,       inputs_dict["""attention_mask"""]   )

                           __lowerCamelCase   =						{
                               """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                               """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                               """encoder_outputs""": encoder_outputs,
                           }

                           @jax.jit
                           def decode_jitted(UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: str   ):
                                  return model.decode(
                                      decoder_input_ids=UpperCamelCase_    ,       decoder_attention_mask=UpperCamelCase_    ,       encoder_outputs=UpperCamelCase_    ,       )

                           with self.subTest("""JIT Enabled"""   ):
                                  __lowerCamelCase   =						decode_jitted(**UpperCamelCase_   ).to_tuple()

                           with self.subTest("""JIT Disabled"""   ):
                                  with jax.disable_jit():
                                         __lowerCamelCase   =						decode_jitted(**UpperCamelCase_   ).to_tuple()

                           self.assertEqual(len(UpperCamelCase_   )    ,       len(UpperCamelCase_   )   )
                           for jitted_output, output in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                                  self.assertEqual(jitted_output.shape    ,       output.shape   )
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             for model_class_name in self.all_model_classes:
                    __lowerCamelCase   =						model_class_name.from_pretrained("""google/pegasus-large"""    ,       from_pt=UpperCamelCase_   )
                    __lowerCamelCase   =						np.ones((1, 1)   )
                    __lowerCamelCase   =						model(UpperCamelCase_   )
                    self.assertIsNotNone(UpperCamelCase_   )




      @slow
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""   )
             __lowerCamelCase   =						PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""   )

             __lowerCamelCase   =						[
                 """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
                 """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
             ]

             __lowerCamelCase   =						[
                 """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
                 """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
             ]

             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""np"""    ,       truncation=UpperCamelCase_    ,       max_length=5_12    ,       padding=UpperCamelCase_   )
             __lowerCamelCase   =						model.generate(**UpperCamelCase_    ,       num_beams=2   ).sequences
             __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_    ,       skip_special_tokens=UpperCamelCase_   )
             assert tgt_text == decoded







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


UpperCAmelCase_									=	get_tests_dir('fixtures')
UpperCAmelCase_									=	get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase_									=	get_tests_dir('fixtures/dummy-config.json')





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

                    # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   ).to_dict()

                    config_dict.pop("""feature_extractor_type"""   )
                    __lowerCamelCase   =						WavaVecaFeatureExtractor(**UpperCamelCase_   )

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

                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )

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

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

             __lowerCamelCase   =						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_   )
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_    ,       trust_remote_code=UpperCamelCase_   )
             self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
      def 			lowerCAmelCase__						(				self: Any   ):
             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
                    __lowerCamelCase   =						CustomFeatureExtractor.from_pretrained(UpperCamelCase_   )
                    with tempfile.TemporaryDirectory() as tmp_dir:
                           feature_extractor.save_pretrained(UpperCamelCase_   )
                           __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Dict   ):





             class     lowerCamelCase__(					__lowerCamelCase):
                   UpperCAmelCase__      :	str    		=   True

             try:
                    AutoConfig.register("""custom"""    ,       UpperCamelCase_   )
                    AutoFeatureExtractor.register(UpperCamelCase_    ,       UpperCamelCase_   )
                    # If remote code is not set, the default is to use local
                    __lowerCamelCase   =						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.
                    __lowerCamelCase   =						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
                    __lowerCamelCase   =						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]

