
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Any				       =(DPMSolverSinglestepScheduler,)
	A__    :   int				       =(("""num_inference_steps""", 2_5),)
	def      A_				(			self							:   List[str]       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= {
						    'num_train_timesteps': 1000,
						    'beta_start': 0.0_001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'solver_order': 2,
						    'prediction_type': 'epsilon',
						    'thresholding': False,
						    'sample_max_value': 1.0,
						    'algorithm_type': 'dpmsolver++',
						    'solver_type': 'midpoint',
						    'lambda_min_clipped': -float('inf'							),
						    'variance_type': None,
						}

						config.update(**UpperCAmelCase_							)
						return config
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]=0       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= dict(self.forward_default_kwargs							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('num_inference_steps'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.dummy_sample
						SCREAMING_SNAKE_CASE__													= 0.1 * sample
						SCREAMING_SNAKE_CASE__													= [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

																assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
	def      A_				(			self							:   Optional[int]							):
						pass
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   str=0       ,   **UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= dict(self.forward_default_kwargs							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('num_inference_steps'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.dummy_sample
						SCREAMING_SNAKE_CASE__													= 0.1 * sample
						SCREAMING_SNAKE_CASE__													= [residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__													= self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)
											scheduler.set_timesteps(UpperCAmelCase_							)

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

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

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

											SCREAMING_SNAKE_CASE__													= scheduler.step(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   **UpperCAmelCase_							).prev_sample
											SCREAMING_SNAKE_CASE__													= new_scheduler.step(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   **UpperCAmelCase_							).prev_sample

											assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   Optional[int]=None       ,   **UpperCAmelCase_							:   Union[str, Any]							):







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

						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 10
						SCREAMING_SNAKE_CASE__													= self.dummy_model()
						SCREAMING_SNAKE_CASE__													= self.dummy_sample_deter
						scheduler.set_timesteps(UpperCAmelCase_							)

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

						return sample
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
						SCREAMING_SNAKE_CASE__													= 50
						SCREAMING_SNAKE_CASE__													= self.dummy_model()
						SCREAMING_SNAKE_CASE__													= self.dummy_sample_deter
						scheduler.set_timesteps(UpperCAmelCase_							)

						# make sure that the first t is uneven
						for i, t in enumerate(scheduler.timesteps[3:]							):
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= scheduler.step(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							).prev_sample

						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.2_574							) < 1e-3
	def      A_				(			self							:   List[Any]							):







						for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
						SCREAMING_SNAKE_CASE__													= self.full_loop(scheduler=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.2_791							) < 1e-3

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

						SCREAMING_SNAKE_CASE__													= self.full_loop(scheduler=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.2_791							) < 1e-3
	def      A_				(			self							:   Any							):
						self.check_over_configs(thresholding=UpperCAmelCase_							)
						for order in [1, 2, 3]:
											for solver_type in ["midpoint", "heun"]:
																for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    thresholding=UpperCAmelCase_       ,   prediction_type=UpperCAmelCase_       ,   sample_max_value=UpperCAmelCase_       ,   algorithm_type='dpmsolver++'       ,   solver_order=UpperCAmelCase_       ,   solver_type=UpperCAmelCase_       ,   )
	def      A_				(			self							:   Union[str, Any]							):







						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]							):







						for algorithm_type in ["dpmsolver", "dpmsolver++"]:
											for solver_type in ["midpoint", "heun"]:
																for order in [1, 2, 3]:
																					for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    solver_order=UpperCAmelCase_       ,   solver_type=UpperCAmelCase_       ,   prediction_type=UpperCAmelCase_       ,   algorithm_type=UpperCAmelCase_       ,   )
																										SCREAMING_SNAKE_CASE__													= self.full_loop(
																										    solver_order=UpperCAmelCase_       ,   solver_type=UpperCAmelCase_       ,   prediction_type=UpperCAmelCase_       ,   algorithm_type=UpperCAmelCase_       ,   )
																										assert not torch.isnan(UpperCAmelCase_							).any(), "Samples have nan numbers"
	def      A_				(			self							:   Optional[Any]							):
						self.check_over_configs(lower_order_final=UpperCAmelCase_							)
						self.check_over_configs(lower_order_final=UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						self.check_over_configs(lambda_min_clipped=-float('inf'							)							)
						self.check_over_configs(lambda_min_clipped=-5.1							)
	def      A_				(			self							:   int							):
						self.check_over_configs(variance_type=UpperCAmelCase_							)
						self.check_over_configs(variance_type='learned_range'							)
	def      A_				(			self							:   int							):







						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=UpperCAmelCase_       ,   time_step=0							)
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.full_loop()
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.2_791							) < 1e-3
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= self.full_loop(use_karras_sigmas=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.2_248							) < 1e-3
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.full_loop(prediction_type='v_prediction'							)
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.1_453							) < 1e-3
	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.full_loop(prediction_type='v_prediction'       ,   use_karras_sigmas=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.mean(torch.abs(UpperCAmelCase_							)							)

						assert abs(result_mean.item() - 0.0_649							) < 1e-3





	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config(thresholding=UpperCAmelCase_       ,   dynamic_thresholding_ratio=0							)
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 10
						SCREAMING_SNAKE_CASE__													= self.dummy_model()
						SCREAMING_SNAKE_CASE__													= self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCAmelCase_							)

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

						assert sample.dtype == torch.floataa











__UpperCamelCase					:       Optional[int]    					=      {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

