

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   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      0





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							int       =      AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''  )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Any       =      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE  )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_      :							Any       =      WavaVecaConfig()

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

              config_dict.pop('''feature_extractor_type'''  )
              UpperCAmelCase_      :							Dict       =      WavaVecaFeatureExtractor(**_SCREAMING_SNAKE_CASE  )

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

              UpperCAmelCase_      :							Any       =      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE  )

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

         self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE  )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





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





    def 							a__				(				self  )			->		Tuple:
         with self.assertRaisesRegex(
             _SCREAMING_SNAKE_CASE       ,R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''  ):
              UpperCAmelCase_      :							Dict       =      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE       ,revision='''aaaaaa'''  )





    def 							a__				(				self  )			->		List[str]:
         with self.assertRaisesRegex(
             _SCREAMING_SNAKE_CASE       ,'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''       ,):
              UpperCAmelCase_      :							Union[str, Any]       =      AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''  )





    def 							a__				(				self  )			->		int:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Dict       =      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  ):
              UpperCAmelCase_      :							str       =      AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''       ,trust_remote_code=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      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  )
              UpperCAmelCase_      :							Tuple       =      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE       ,trust_remote_code=_SCREAMING_SNAKE_CASE  )
         self.assertEqual(reloaded_feature_extractor.__class__.__name__       ,'''NewFeatureExtractor'''  )





    def 							a__				(				self  )			->		int:
         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
              UpperCAmelCase_      :							Optional[int]       =      CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE  )
              with tempfile.TemporaryDirectory() as tmp_dir:
                   feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							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 							a__				(				self  )			->		List[Any]:

         class   __a(					__snake_case       ):






             """simple docstring"""





             lowerCAmelCase        =				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
              UpperCAmelCase_      :							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.
              UpperCAmelCase_      :							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(feature_extractor.is_local  )

              # If remote is enabled, we load from the Hub
              UpperCAmelCase_      :							Optional[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(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]





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(IPNDMScheduler,)
    lowerCAmelCase        =				(('''num_inference_steps''', 50),)





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Tuple       =      {'''num_train_timesteps''': 1_000}
         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      self.dummy_sample
         UpperCAmelCase_      :							Optional[Any]       =      0.1 * sample
         UpperCAmelCase_      :							Optional[int]       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							Tuple       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              # copy over dummy past residuals
              UpperCAmelCase_      :							Optional[Any]       =      dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_      :							Tuple       =      scheduler.timesteps[len(scheduler.timesteps  ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
                   # copy over dummy past residuals
                   UpperCAmelCase_      :							Optional[int]       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							str       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"





    def 							a__				(				self  )			->		List[Any]:
         pass





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.dummy_sample
         UpperCAmelCase_      :							Tuple       =      0.1 * sample
         UpperCAmelCase_      :							int       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							str       =      self.get_scheduler_config()
              UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCAmelCase_      :							Tuple       =      dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_      :							Dict       =      scheduler.timesteps[len(scheduler.timesteps  ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Optional[int]       =      scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

                   # copy over dummy past residual (must be after setting timesteps)
                   UpperCAmelCase_      :							str       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							Tuple       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase_      :							str       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							List[str]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Tuple       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[Any]       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      10
         UpperCAmelCase_      :							Tuple       =      self.dummy_model()
         UpperCAmelCase_      :							List[str]       =      self.dummy_sample_deter
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         return sample





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      dict(self.forward_default_kwargs  )

         UpperCAmelCase_      :							Any       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							int       =      self.get_scheduler_config()
              UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Optional[int]       =      self.dummy_sample
              UpperCAmelCase_      :							List[Any]       =      0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   UpperCAmelCase_      :							Union[str, Any]       =      num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase_      :							str       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              UpperCAmelCase_      :							int       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							List[Any]       =      scheduler.timesteps[5]
              UpperCAmelCase_      :							Dict       =      scheduler.timesteps[6]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              self.assertEqual(output_a.shape       ,sample.shape  )
              self.assertEqual(output_a.shape       ,output_a.shape  )

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

              self.assertEqual(output_a.shape       ,sample.shape  )
              self.assertEqual(output_a.shape       ,output_a.shape  )





    def 							a__				(				self  )			->		Any:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         for t, num_inference_steps in zip([1, 5, 10]       ,[10, 50, 100]  ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.full_loop()
         UpperCAmelCase_      :							str       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_mean.item() - 2_540_529  ) < 10