





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 	A_       (a_			):
	UpperCAmelCase__					=			(DEISMultistepScheduler,)
	UpperCAmelCase__					=			(('''num_inference_steps''', 2_5),)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''solver_order''': 2,
				}

				config.update(**_A		)
				return config
	def      _lowercase				(    self							,					_A=0							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				dict(self.forward_default_kwargs		)
				UpperCAmelCase   		=				kwargs.pop('''num_inference_steps'''							,					_A		)
				UpperCAmelCase   		=				self.dummy_sample
				UpperCAmelCase   		=				0.1 * sample
				UpperCAmelCase   		=				[residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase   		=				self.get_scheduler_config(**_A		)
							UpperCAmelCase   		=				scheduler_class(**_A		)
							scheduler.set_timesteps(_A		)
							# copy over dummy past residuals
							UpperCAmelCase   		=				dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_A		)
										UpperCAmelCase   		=				scheduler_class.from_pretrained(_A		)
										new_scheduler.set_timesteps(_A		)
										# copy over dummy past residuals
										UpperCAmelCase   		=				dummy_past_residuals[: new_scheduler.config.solver_order]

							UpperCAmelCase		,			UpperCAmelCase   		=				sample, sample
							for t in range(_A							,					time_step + scheduler.config.solver_order + 1		):
										UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample
										UpperCAmelCase   		=				new_scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self							,					_A=0							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				dict(self.forward_default_kwargs		)
				UpperCAmelCase   		=				kwargs.pop('''num_inference_steps'''							,					_A		)
				UpperCAmelCase   		=				self.dummy_sample
				UpperCAmelCase   		=				0.1 * sample
				UpperCAmelCase   		=				[residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase   		=				self.get_scheduler_config()
							UpperCAmelCase   		=				scheduler_class(**_A		)
							scheduler.set_timesteps(_A		)

							# copy over dummy past residuals (must be after setting timesteps)
							UpperCAmelCase   		=				dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_A		)
										UpperCAmelCase   		=				scheduler_class.from_pretrained(_A		)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(_A		)

										# copy over dummy past residual (must be after setting timesteps)
										UpperCAmelCase   		=				dummy_past_residuals[: new_scheduler.config.solver_order]

							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample
							UpperCAmelCase   		=				new_scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"
	def      _lowercase				(    self							,					_A=None							,					**_A		):






				'''simple docstring'''







				if scheduler is None:
							UpperCAmelCase   		=				self.scheduler_classes[0]
							UpperCAmelCase   		=				self.get_scheduler_config(**_A		)
							UpperCAmelCase   		=				scheduler_class(**_A		)

				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config(**_A		)
				UpperCAmelCase   		=				scheduler_class(**_A		)

				UpperCAmelCase   		=				1_0
				UpperCAmelCase   		=				self.dummy_model()
				UpperCAmelCase   		=				self.dummy_sample_deter
				scheduler.set_timesteps(_A		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase   		=				model(_A							,					_A		)
							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A		).prev_sample

				return sample
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				dict(self.forward_default_kwargs		)

				UpperCAmelCase   		=				kwargs.pop('''num_inference_steps'''							,					_A		)

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase   		=				self.get_scheduler_config()
							UpperCAmelCase   		=				scheduler_class(**_A		)

							UpperCAmelCase   		=				self.dummy_sample
							UpperCAmelCase   		=				0.1 * sample

							if num_inference_steps is not None and hasattr(_A							,					'''set_timesteps'''		):
										scheduler.set_timesteps(_A		)
							elif num_inference_steps is not None and not hasattr(_A							,					'''set_timesteps'''		):
										UpperCAmelCase   		=				num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							UpperCAmelCase   		=				[residual + 0.2, residual + 0.15, residual + 0.10]
							UpperCAmelCase   		=				dummy_past_residuals[: scheduler.config.solver_order]

							UpperCAmelCase   		=				scheduler.timesteps[5]
							UpperCAmelCase   		=				scheduler.timesteps[6]

							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample
							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A							,					**_A		).prev_sample

							self.assertEqual(output_a.shape							,					sample.shape		)
							self.assertEqual(output_a.shape							,					output_a.shape		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				DEISMultistepScheduler(**self.get_scheduler_config()		)
				UpperCAmelCase   		=				self.full_loop(scheduler=_A		)
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_mean.item() - 0.2_39_16		) < 1E-3

				UpperCAmelCase   		=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)
				UpperCAmelCase   		=				DPMSolverMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase   		=				UniPCMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase   		=				DEISMultistepScheduler.from_config(scheduler.config		)

				UpperCAmelCase   		=				self.full_loop(scheduler=_A		)
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_mean.item() - 0.2_39_16		) < 1E-3
	def      _lowercase				(    self		):






				'''simple docstring'''







				for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.check_over_configs(thresholding=_A		)
				for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=_A							,					prediction_type=_A							,					sample_max_value=_A							,					algorithm_type='''deis'''							,					solver_order=_A							,					solver_type=_A							,					)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=_A							,					solver_type=_A							,					prediction_type=_A							,					algorithm_type=_A							,					)
																UpperCAmelCase   		=				self.full_loop(
																    solver_order=_A							,					solver_type=_A							,					prediction_type=_A							,					algorithm_type=_A							,					)
																assert not torch.isnan(_A		).any(), "Samples have nan numbers"
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.check_over_configs(lower_order_final=_A		)
				self.check_over_configs(lower_order_final=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=_A							,					time_step=0		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.full_loop()
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_mean.item() - 0.2_39_16		) < 1E-3
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.full_loop(prediction_type='''v_prediction'''		)
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_mean.item() - 0.0_91		) < 1E-3







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config(thresholding=_A							,					dynamic_thresholding_ratio=0		)
				UpperCAmelCase   		=				scheduler_class(**_A		)

				UpperCAmelCase   		=				1_0
				UpperCAmelCase   		=				self.dummy_model()
				UpperCAmelCase   		=				self.dummy_sample_deter.half()
				scheduler.set_timesteps(_A		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase   		=				model(_A							,					_A		)
							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A		).prev_sample

				assert sample.dtype == torch.floataa










from typing import TYPE_CHECKING

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


__A				:    Union[str, 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:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "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

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



