



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class       __lowerCAmelCase   (      UpperCAmelCase_						):
			UpperCamelCase        =					(DDIMParallelScheduler,)
			UpperCamelCase        =					(("""eta""", 0.0), ("""num_inference_steps""", 5_0))



			def    _lowerCamelCase	(						self       :  List[str]			,		**A       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       {
						    '''num_train_timesteps''': 10_00,
						    '''beta_start''': 0.0_0_0_1,
						    '''beta_end''': 0.0_2,
						    '''beta_schedule''': '''linear''',
						    '''clip_sample''': True,
						}

						config.update(**__lowercase)
						return config



			def    _lowerCamelCase	(						self       :  List[str]			,		**A       :  Optional[Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config(**__lowercase)
						_UpperCAmelCase			=       scheduler_class(**__lowercase)

						_UpperCAmelCase			=       10, 0.0

						_UpperCAmelCase			=       self.dummy_model()
						_UpperCAmelCase			=       self.dummy_sample_deter

						scheduler.set_timesteps(__lowercase)

						for t in scheduler.timesteps:
									_UpperCAmelCase			=       model(__lowercase			,		__lowercase)
									_UpperCAmelCase			=       scheduler.step(__lowercase			,		__lowercase			,		__lowercase			,		__lowercase).prev_sample

						return sample



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



						"""simple docstring"""






						for timesteps in [1_00, 5_00, 10_00]:
									self.check_over_configs(num_train_timesteps=__lowercase)



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



						"""simple docstring"""






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

						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config(steps_offset=1)
						_UpperCAmelCase			=       scheduler_class(**__lowercase)
						scheduler.set_timesteps(5)
						assert torch.equal(scheduler.timesteps			,		torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						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=__lowercase			,		beta_end=__lowercase)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  str)       ->   int:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






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



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



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[Any])       ->   str:



						"""simple docstring"""






						for t, num_inference_steps in zip([1, 10, 50]			,		[10, 50, 5_00]):
									self.check_over_forward(time_step=__lowercase			,		num_inference_steps=__lowercase)



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



						"""simple docstring"""






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



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**__lowercase)

						assert torch.sum(torch.abs(scheduler._get_variance(0			,		0) - 0.0)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_20			,		4_00) - 0.1_4_7_7_1)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_80			,		9_60) - 0.3_2_4_6_0)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0			,		0) - 0.0)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87			,		4_86) - 0.0_0_9_7_9)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99			,		9_98) - 0.0_2)) < 1E-5



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**__lowercase)

						_UpperCAmelCase			=       10, 0.0
						scheduler.set_timesteps(__lowercase)

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

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

						_UpperCAmelCase			=       model(samples.flatten(0			,		1)			,		timesteps.flatten(0			,		1))
						_UpperCAmelCase			=       scheduler.batch_step_no_noise(__lowercase			,		timesteps.flatten(0			,		1)			,		samples.flatten(0			,		1)			,		__lowercase)

						_UpperCAmelCase			=       torch.sum(torch.abs(__lowercase))
						_UpperCAmelCase			=       torch.mean(torch.abs(__lowercase))

						assert abs(result_sum.item() - 11_47.79_04) < 1E-2
						assert abs(result_mean.item() - 0.4_9_8_2) < 1E-3



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.full_loop()

						_UpperCAmelCase			=       torch.sum(torch.abs(__lowercase))
						_UpperCAmelCase			=       torch.mean(torch.abs(__lowercase))

						assert abs(result_sum.item() - 1_72.00_67) < 1E-2
						assert abs(result_mean.item() - 0.2_2_3_9_6_7) < 1E-3



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






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

						_UpperCAmelCase			=       torch.sum(torch.abs(__lowercase))
						_UpperCAmelCase			=       torch.mean(torch.abs(__lowercase))

						assert abs(result_sum.item() - 5_2.5_3_0_2) < 1E-2
						assert abs(result_mean.item() - 0.0_6_8_4) < 1E-3



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       self.full_loop(set_alpha_to_one=__lowercase			,		beta_start=0.0_1)
						_UpperCAmelCase			=       torch.sum(torch.abs(__lowercase))
						_UpperCAmelCase			=       torch.mean(torch.abs(__lowercase))

						assert abs(result_sum.item() - 1_49.82_95) < 1E-2
						assert abs(result_mean.item() - 0.1_9_5_1) < 1E-3



			def    _lowerCamelCase	(						self       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.full_loop(set_alpha_to_one=__lowercase			,		beta_start=0.0_1)
						_UpperCAmelCase			=       torch.sum(torch.abs(__lowercase))
						_UpperCAmelCase			=       torch.mean(torch.abs(__lowercase))

						assert abs(result_sum.item() - 1_49.07_84) < 1E-2
						assert abs(result_mean.item() - 0.1_9_4_1) < 1E-3











from typing import TYPE_CHECKING

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


UpperCAmelCase__       =	{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPTokenizerFast"]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPFeatureExtractor"]
							UpperCAmelCase__       =	["CLIPImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CLIPModel",
							    "CLIPPreTrainedModel",
							    "CLIPTextModel",
							    "CLIPTextModelWithProjection",
							    "CLIPVisionModel",
							    "CLIPVisionModelWithProjection",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCLIPModel",
							    "TFCLIPPreTrainedModel",
							    "TFCLIPTextModel",
							    "TFCLIPVisionModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "FlaxCLIPModel",
							    "FlaxCLIPPreTrainedModel",
							    "FlaxCLIPTextModel",
							    "FlaxCLIPTextPreTrainedModel",
							    "FlaxCLIPVisionModel",
							    "FlaxCLIPVisionPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
