




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   'Wav2Vec2FeatureExtractor'
		__snake_case									=   'AutoTokenizer'



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								super().__init__(UpperCamelCase__,	UpperCamelCase__   )
								lowerCAmelCase_      			=  self.feature_extractor
								lowerCAmelCase_      			=  False



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								try:
														return super().from_pretrained(UpperCamelCase__,	**UpperCamelCase__   )
								except OSError:
														warnings.warn(
														    f"Loading a tokenizer inside {cls.__name__} from a config that does not"
														    ''' include a `tokenizer_class` attribute is deprecated and will be '''
														    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
														    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
														    '''file to suppress this warning: ''',	UpperCamelCase__,	)

														lowerCAmelCase_      			=  WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__,	**UpperCamelCase__   )
														lowerCAmelCase_      			=  WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase__,	**UpperCamelCase__   )

														return cls(feature_extractor=UpperCamelCase__,	tokenizer=UpperCamelCase__   )



		def __call__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								if self._in_target_context_manager:
														return self.current_processor(*UpperCamelCase__,	**UpperCamelCase__   )

								if "raw_speech" in kwargs:
														warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
														lowerCAmelCase_      			=  kwargs.pop('''raw_speech'''   )
								else:
														lowerCAmelCase_      			=  kwargs.pop('''audio''',	UpperCamelCase__   )
								lowerCAmelCase_      			=  kwargs.pop('''sampling_rate''',	UpperCamelCase__   )
								lowerCAmelCase_      			=  kwargs.pop('''text''',	UpperCamelCase__   )
								if len(UpperCamelCase__   ) > 0:
														lowerCAmelCase_      			=  args[0]
														lowerCAmelCase_      			=  args[1:]

								if audio is None and text is None:
														raise ValueError('''You need to specify either an `audio` or `text` input to process.'''   )

								if audio is not None:
														lowerCAmelCase_      			=  self.feature_extractor(UpperCamelCase__,	*UpperCamelCase__,	sampling_rate=UpperCamelCase__,	**UpperCamelCase__   )
								if text is not None:
														lowerCAmelCase_      			=  self.tokenizer(UpperCamelCase__,	**UpperCamelCase__   )

								if text is None:
														return inputs
								elif audio is None:
														return encodings
								else:
														lowerCAmelCase_      			=  encodings['''input_ids''']
														return inputs



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								if self._in_target_context_manager:
														return self.current_processor.pad(*UpperCamelCase__,	**UpperCamelCase__   )

								lowerCAmelCase_      			=  kwargs.pop('''input_features''',	UpperCamelCase__   )
								lowerCAmelCase_      			=  kwargs.pop('''labels''',	UpperCamelCase__   )
								if len(UpperCamelCase__   ) > 0:
														lowerCAmelCase_      			=  args[0]
														lowerCAmelCase_      			=  args[1:]

								if input_features is not None:
														lowerCAmelCase_      			=  self.feature_extractor.pad(UpperCamelCase__,	*UpperCamelCase__,	**UpperCamelCase__   )
								if labels is not None:
														lowerCAmelCase_      			=  self.tokenizer.pad(UpperCamelCase__,	**UpperCamelCase__   )

								if labels is None:
														return input_features
								elif input_features is None:
														return labels
								else:
														lowerCAmelCase_      			=  labels['''input_ids''']
														return input_features



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								return self.tokenizer.batch_decode(*UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								return self.tokenizer.decode(*UpperCamelCase__,	**UpperCamelCase__   )







		@contextmanager
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								warnings.warn(
								    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
								    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
								    '''your audio inputs, or in a separate call.'''   )
								lowerCAmelCase_      			=  True
								lowerCAmelCase_      			=  self.tokenizer
								yield
								lowerCAmelCase_      			=  self.feature_extractor
								lowerCAmelCase_      			=  False










import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




