





"""simple docstring"""





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class 		SCREAMING_SNAKE_CASE_							:
		"""simple docstring"""




		@property
		def 					__lowercase  (     self  :Any			):
									return self.get_dummy_input()




		@property
		def 					__lowercase  (     self  :Optional[int]			):
									if self.block_type == "down":
																return (4, 32, 16, 16)
									elif self.block_type == "mid":
																return (4, 32, 32, 32)
									elif self.block_type == "up":
																return (4, 32, 64, 64)

									raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'			)




		def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :List[Any]=True						,    __lowercase  :str=False						,    __lowercase  :int=False						,    __lowercase  :List[Any]=False						,    ):
									__lowerCamelCase      :      str         =4
									__lowerCamelCase      :      List[str]         =32
									__lowerCamelCase      :      str         =(32, 32)

									__lowerCamelCase      :      List[str]         =torch.manual_seed(0			)
									__lowerCamelCase      :      Tuple         =torch.device(A__			)
									__lowerCamelCase      :      str         =(batch_size, num_channels) + sizes
									__lowerCamelCase      :      int         =randn_tensor(A__						,    generator=A__						,    device=A__			)
									__lowerCamelCase      :      Union[str, Any]         ={"""hidden_states""": hidden_states}

									if include_temb:
																__lowerCamelCase      :      List[Any]         =128
																__lowerCamelCase      :      Dict         =randn_tensor((batch_size, temb_channels)						,    generator=A__						,    device=A__			)

									if include_res_hidden_states_tuple:
																__lowerCamelCase      :      Optional[int]         =torch.manual_seed(1			)
																__lowerCamelCase      :      List[Any]         =(randn_tensor(A__						,    generator=A__						,    device=A__			),)

									if include_encoder_hidden_states:
																__lowerCamelCase      :      List[Any]         =floats_tensor((batch_size, 32, 32)			).to(A__			)

									if include_skip_sample:
																__lowerCamelCase      :      Dict         =randn_tensor(((batch_size, 3) + sizes)						,    generator=A__						,    device=A__			)

									return dummy_input




		def 					__lowercase  (     self  :Dict			):
									__lowerCamelCase      :      Dict         ={
									    """in_channels""": 32,
									    """out_channels""": 32,
									    """temb_channels""": 128,
									}
									if self.block_type == "up":
																__lowerCamelCase      :      Union[str, Any]         =32

									if self.block_type == "mid":
																init_dict.pop('''out_channels'''			)

									__lowerCamelCase      :      Any         =self.dummy_input
									return init_dict, inputs_dict




		def 					__lowercase  (     self  :Any						,    __lowercase  :List[Any]			):
									__lowerCamelCase      :      Optional[Any]         =self.prepare_init_args_and_inputs_for_common()
									__lowerCamelCase      :      Optional[Any]         =self.block_class(**A__			)
									unet_block.to(A__			)
									unet_block.eval()

									with torch.no_grad():
																__lowerCamelCase      :      Union[str, Any]         =unet_block(**A__			)

									if isinstance(A__						,    A__			):
																__lowerCamelCase      :      Any         =output[0]

									self.assertEqual(output.shape						,    self.output_shape			)

									__lowerCamelCase      :      str         =output[0, -1, -3:, -3:]
									__lowerCamelCase      :      List[Any]         =torch.tensor(A__			).to(A__			)
									assert torch_all_close(output_slice.flatten()						,    A__						,    atol=5e-3			)




		@unittest.skipIf(torch_device == '''mps'''						,    '''Training is not supported in mps'''			)
		def 					__lowercase  (     self  :Tuple			):
									__lowerCamelCase      :      Optional[int]         =self.prepare_init_args_and_inputs_for_common()
									__lowerCamelCase      :      str         =self.block_class(**A__			)
									model.to(A__			)
									model.train()
									__lowerCamelCase      :      Optional[int]         =model(**A__			)

									if isinstance(A__						,    A__			):
																__lowerCamelCase      :      Tuple         =output[0]

									__lowerCamelCase      :      Tuple         =torch.device(A__			)
									__lowerCamelCase      :      str         =randn_tensor(output.shape						,    device=A__			)
									__lowerCamelCase      :      Union[str, Any]         =torch.nn.functional.mse_loss(A__						,    A__			)
									loss.backward()











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




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




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




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




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

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

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




