




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=(DDPMParallelScheduler,)

			def 							UpperCAmelCase__       ( self			:   Dict       ,						**lowerCamelCase_			:   Any  )						->   int:
								__magic_name__							:	List[Any]    							=							{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**lowerCamelCase_  )
								return config

			def 							UpperCAmelCase__       ( self			:   int  )						->   str:
								for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any  )						->   int:
								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,						[0.0_0_2, 0.0_2, 0.2, 2]  ):
													self.check_over_configs(beta_start=lowerCamelCase_       ,						beta_end=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Union[str, Any]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any  )						->   str:
								for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   int:
								for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   str:
								self.check_over_configs(thresholding=lowerCamelCase_  )
								for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=lowerCamelCase_       ,						prediction_type=lowerCamelCase_       ,						sample_max_value=lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Tuple:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[str]:
								for t in [0, 500, 999]:
													self.check_over_forward(time_step=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Any:
								__magic_name__							:	List[str]    							=							self.scheduler_classes[0]
								__magic_name__							:	int    							=							self.get_scheduler_config()
								__magic_name__							:	Tuple    							=							scheduler_class(**lowerCamelCase_  )

								assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_0_9_7_9  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.0_2  )  ) < 1E-5

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Dict:
								__magic_name__							:	Any    							=							self.scheduler_classes[0]
								__magic_name__							:	List[str]    							=							self.get_scheduler_config()
								__magic_name__							:	Optional[int]    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							len(lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							self.dummy_model()
								__magic_name__							:	Tuple    							=							self.dummy_sample_deter
								__magic_name__							:	int    							=							self.dummy_sample_deter + 0.1
								__magic_name__							:	int    							=							self.dummy_sample_deter - 0.1

								__magic_name__							:	Union[str, Any]    							=							samplea.shape[0]
								__magic_name__							:	Any    							=							torch.stack([samplea, samplea, samplea]       ,						dim=0  )
								__magic_name__							:	Any    							=							torch.arange(lowerCamelCase_  )[0:3, None].repeat(1       ,						lowerCamelCase_  )

								__magic_name__							:	int    							=							model(samples.flatten(0       ,						1  )       ,						timesteps.flatten(0       ,						1  )  )
								__magic_name__							:	Optional[int]    							=							scheduler.batch_step_no_noise(lowerCamelCase_       ,						timesteps.flatten(0       ,						1  )       ,						samples.flatten(0       ,						1  )  )

								__magic_name__							:	Tuple    							=							torch.sum(torch.abs(lowerCamelCase_  )  )
								__magic_name__							:	Optional[int]    							=							torch.mean(torch.abs(lowerCamelCase_  )  )

								assert abs(result_sum.item() - 1_1_5_3.1_8_3_3  ) < 1E-2
								assert abs(result_mean.item() - 0.5_0_0_5  ) < 1E-3

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Optional[int]:
								__magic_name__							:	Any    							=							self.scheduler_classes[0]
								__magic_name__							:	Optional[Any]    							=							self.get_scheduler_config()
								__magic_name__							:	List[Any]    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							len(lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							self.dummy_model()
								__magic_name__							:	Dict    							=							self.dummy_sample_deter
								__magic_name__							:	int    							=							torch.manual_seed(0  )

								for t in reversed(range(lowerCamelCase_  )  ):
													# 1. predict noise residual
													__magic_name__							:	int    							=							model(lowerCamelCase_       ,						lowerCamelCase_  )

													# 2. predict previous mean of sample x_t-1
													__magic_name__							:	Optional[int]    							=							scheduler.step(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						generator=lowerCamelCase_  ).prev_sample

													__magic_name__							:	int    							=							pred_prev_sample

								__magic_name__							:	str    							=							torch.sum(torch.abs(lowerCamelCase_  )  )
								__magic_name__							:	List[Any]    							=							torch.mean(torch.abs(lowerCamelCase_  )  )

								assert abs(result_sum.item() - 2_5_8.9_6_0_6  ) < 1E-2
								assert abs(result_mean.item() - 0.3_3_7_2  ) < 1E-3

			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	str    							=							self.scheduler_classes[0]
								__magic_name__							:	Tuple    							=							self.get_scheduler_config(prediction_type='''v_prediction'''  )
								__magic_name__							:	Any    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							len(lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							self.dummy_model()
								__magic_name__							:	List[str]    							=							self.dummy_sample_deter
								__magic_name__							:	Any    							=							torch.manual_seed(0  )

								for t in reversed(range(lowerCamelCase_  )  ):
													# 1. predict noise residual
													__magic_name__							:	List[str]    							=							model(lowerCamelCase_       ,						lowerCamelCase_  )

													# 2. predict previous mean of sample x_t-1
													__magic_name__							:	Optional[Any]    							=							scheduler.step(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						generator=lowerCamelCase_  ).prev_sample

													__magic_name__							:	Any    							=							pred_prev_sample

								__magic_name__							:	Tuple    							=							torch.sum(torch.abs(lowerCamelCase_  )  )
								__magic_name__							:	Optional[int]    							=							torch.mean(torch.abs(lowerCamelCase_  )  )

								assert abs(result_sum.item() - 2_0_2.0_2_9_6  ) < 1E-2
								assert abs(result_mean.item() - 0.2_6_3_1  ) < 1E-3

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   str:
								__magic_name__							:	Optional[Any]    							=							self.scheduler_classes[0]
								__magic_name__							:	str    							=							self.get_scheduler_config()
								__magic_name__							:	Optional[Any]    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							[100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=lowerCamelCase_  )

								__magic_name__							:	int    							=							scheduler.timesteps

								for i, timestep in enumerate(lowerCamelCase_  ):
													if i == len(lowerCamelCase_  ) - 1:
																		__magic_name__							:	Optional[int]    							=							-1
													else:
																		__magic_name__							:	List[str]    							=							timesteps[i + 1]

													__magic_name__							:	Union[str, Any]    							=							scheduler.previous_timestep(lowerCamelCase_  )
													__magic_name__							:	Dict    							=							prev_t.item()

													self.assertEqual(lowerCamelCase_       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   List[Any]:
								__magic_name__							:	Tuple    							=							self.scheduler_classes[0]
								__magic_name__							:	Optional[int]    							=							self.get_scheduler_config()
								__magic_name__							:	str    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							[100, 87, 50, 51, 0]

								with self.assertRaises(lowerCamelCase_       ,						msg='''`custom_timesteps` must be in descending order.'''  ):
													scheduler.set_timesteps(timesteps=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   int:
								__magic_name__							:	List[str]    							=							self.scheduler_classes[0]
								__magic_name__							:	Tuple    							=							self.get_scheduler_config()
								__magic_name__							:	Any    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	Dict    							=							[100, 87, 50, 1, 0]
								__magic_name__							:	str    							=							len(lowerCamelCase_  )

								with self.assertRaises(lowerCamelCase_       ,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''  ):
													scheduler.set_timesteps(num_inference_steps=lowerCamelCase_       ,						timesteps=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Any  )						->   str:
								__magic_name__							:	Any    							=							self.scheduler_classes[0]
								__magic_name__							:	int    							=							self.get_scheduler_config()
								__magic_name__							:	Dict    							=							scheduler_class(**lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							[scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    lowerCamelCase_       ,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,						):
													scheduler.set_timesteps(timesteps=lowerCamelCase_  )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[Any]				=			{
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSpeech2TextForConditionalGeneration''',
	    '''TFSpeech2TextModel''',
	    '''TFSpeech2TextPreTrainedModel''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''Speech2TextForConditionalGeneration''',
	    '''Speech2TextModel''',
	    '''Speech2TextPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						List[str]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



