
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A( a			):
				snake_case_						   =    (DDIMParallelScheduler,)
				snake_case_						   =    (('''eta''', 0.0), ('''num_inference_steps''', 5_0))




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**_snake_case				)
								return config




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(**_snake_case				)
								__a										=    scheduler_class(**_snake_case				)

								__a							,     __a										=    10, 0.0

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter

								scheduler.set_timesteps(_snake_case				)

								for t in scheduler.timesteps:
												__a										=    model(_snake_case		,     _snake_case				)
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     _snake_case				).prev_sample

								return sample




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for timesteps in [100, 500, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_snake_case				)

								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(steps_offset=1				)
								__a										=    scheduler_class(**_snake_case				)
								scheduler.set_timesteps(5				)
								assert torch.equal(scheduler.timesteps		,     torch.LongTensor([801, 601, 401, 201, 1]				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,     [0.002, 0.02, 0.2, 2]				):
												self.check_over_configs(beta_start=_snake_case		,     beta_end=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								self.check_over_configs(thresholding=_snake_case				)
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=_snake_case		,     prediction_type=_snake_case		,     sample_max_value=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for t in [1, 10, 49]:
												self.check_over_forward(time_step=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for t, num_inference_steps in zip([1, 10, 50]		,     [10, 50, 500]				):
												self.check_over_forward(time_step=_snake_case		,     num_inference_steps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for t, eta in zip([1, 10, 49]		,     [0.0, 0.5, 1.0]				):
												self.check_over_forward(time_step=_snake_case		,     eta=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								assert torch.sum(torch.abs(scheduler._get_variance(0		,     0				) - 0.0				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420		,     400				) - 0.1_4771				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980		,     960				) - 0.3_2460				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0		,     0				) - 0.0				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487		,     486				) - 0.0_0979				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999		,     998				) - 0.02				)				) < 1E-5




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a							,     __a										=    10, 0.0
								scheduler.set_timesteps(_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter
								__a										=    self.dummy_sample_deter + 0.1
								__a										=    self.dummy_sample_deter - 0.1

								__a										=    samplea.shape[0]
								__a										=    torch.stack([samplea, samplea, samplea]		,     dim=0				)
								__a										=    torch.arange(_snake_case				)[0:3, None].repeat(1		,     _snake_case				)

								__a										=    model(samples.flatten(0		,     1				)		,     timesteps.flatten(0		,     1				)				)
								__a										=    scheduler.batch_step_no_noise(_snake_case		,     timesteps.flatten(0		,     1				)		,     samples.flatten(0		,     1				)		,     _snake_case				)

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 1147.7904				) < 1E-2
								assert abs(result_mean.item() - 0.4982				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.full_loop()

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 172.0067				) < 1E-2
								assert abs(result_mean.item() - 0.22_3967				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.full_loop(prediction_type='''v_prediction'''				)

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 52.5302				) < 1E-2
								assert abs(result_mean.item() - 0.0684				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.full_loop(set_alpha_to_one=_snake_case		,     beta_start=0.01				)
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 149.8295				) < 1E-2
								assert abs(result_mean.item() - 0.1951				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.full_loop(set_alpha_to_one=_snake_case		,     beta_start=0.01				)
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 149.0784				) < 1E-2
								assert abs(result_mean.item() - 0.1941				) < 1E-3

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Optional[int]    = {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['WhisperTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	str    = [
					    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'WhisperForConditionalGeneration',
					    'WhisperModel',
					    'WhisperPreTrainedModel',
					    'WhisperForAudioClassification',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFWhisperForConditionalGeneration',
					    'TFWhisperModel',
					    'TFWhisperPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'FlaxWhisperForConditionalGeneration',
					    'FlaxWhisperModel',
					    'FlaxWhisperPreTrainedModel',
					    'FlaxWhisperForAudioClassification',
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)