'''simple docstring'''





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[int]          =  (UnCLIPScheduler,)



    def 					_lowercase						(self    :    str   ,			**__a    :    Optional[Any] ):
         UpperCAmelCase_           =   {
             "num_train_timesteps": 1000,
             "variance_type": "fixed_small_log",
             "clip_sample": True,
             "clip_sample_range": 1.0,
             "prediction_type": "epsilon",
         }

         config.update(**__a )
         return config



    def 					_lowercase						(self    :    Any ):
         for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__a )



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



    def 					_lowercase						(self    :    Union[str, Any] ):
         for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=__a )



    def 					_lowercase						(self    :    Optional[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=__a   ,			prev_timestep=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(variance_type="fixed_small_log" )
         UpperCAmelCase_           =   scheduler_class(**__a )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000E-10 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_54_96_25 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_99_49_87 ) ) < 1E-5



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(variance_type="learned_range" )
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   0.5

         assert scheduler._get_variance(1   ,			predicted_variance=__a ) - -10.1_71_27_90 < 1E-5
         assert scheduler._get_variance(487   ,			predicted_variance=__a ) - -5.7_99_80_52 < 1E-5
         assert scheduler._get_variance(999   ,			predicted_variance=__a ) - -0.0_01_00_11 < 1E-5



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config()
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   scheduler.timesteps

         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter
         UpperCAmelCase_           =   torch.manual_seed(0 )

         for i, t in enumerate(__a ):
              # 1. predict noise residual
              UpperCAmelCase_           =   model(__a   ,			__a )

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_           =   scheduler.step(__a   ,			__a   ,			__a   ,			generator=__a ).prev_sample

              UpperCAmelCase_           =   pred_prev_sample

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

         assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2
         assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config()
         UpperCAmelCase_           =   scheduler_class(**__a )

         scheduler.set_timesteps(25 )

         UpperCAmelCase_           =   scheduler.timesteps

         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter
         UpperCAmelCase_           =   torch.manual_seed(0 )

         for i, t in enumerate(__a ):
              # 1. predict noise residual
              UpperCAmelCase_           =   model(__a   ,			__a )

              if i + 1 == timesteps.shape[0]:
                   UpperCAmelCase_           =   None
              else:
                   UpperCAmelCase_           =   timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_           =   scheduler.step(
                  __a   ,			__a   ,			__a   ,			prev_timestep=__a   ,			generator=__a ).prev_sample

              UpperCAmelCase_           =   pred_prev_sample

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

         assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2
         assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3



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






    def 					_lowercase						(self    :    Any ):
         pass






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      A__							:






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return self.get_dummy_input()






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							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 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]=True    ,     a					:      Any=False    ,     a					:      Dict=False    ,     a					:      Union[str, Any]=False    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		4
							lowerCAmelCase__      :      int         =		32
							lowerCAmelCase__      :      Tuple         =		(32, 32)

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		torch.device(a   )
							lowerCAmelCase__      :      str         =		(batch_size, num_channels) + sizes
							lowerCAmelCase__      :      Tuple         =		randn_tensor(a    ,     generator=a    ,     device=a   )
							lowerCAmelCase__      :      Optional[Any]         =		{'hidden_states': hidden_states}

							if include_temb:
										lowerCAmelCase__      :      int         =		128
										lowerCAmelCase__      :      List[str]         =		randn_tensor((batch_size, temb_channels)    ,     generator=a    ,     device=a   )

							if include_res_hidden_states_tuple:
										lowerCAmelCase__      :      int         =		torch.manual_seed(1   )
										lowerCAmelCase__      :      str         =		(randn_tensor(a    ,     generator=a    ,     device=a   ),)

							if include_encoder_hidden_states:
										lowerCAmelCase__      :      Any         =		floats_tensor((batch_size, 32, 32)   ).to(a   )

							if include_skip_sample:
										lowerCAmelCase__      :      Union[str, Any]         =		randn_tensor(((batch_size, 3) + sizes)    ,     generator=a    ,     device=a   )

							return dummy_input






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{
							    '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__      :      Optional[int]         =		self.dummy_input
							return init_dict, inputs_dict






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      int         =		self.block_class(**a   )
							unet_block.to(a   )
							unet_block.eval()

							with torch.no_grad():
										lowerCAmelCase__      :      int         =		unet_block(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		output[0]

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

							lowerCAmelCase__      :      List[str]         =		output[0, -1, -3:, -3:]
							lowerCAmelCase__      :      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 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      Any         =		self.block_class(**a   )
							model.to(a   )
							model.train()
							lowerCAmelCase__      :      int         =		model(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		output[0]

							lowerCAmelCase__      :      Optional[int]         =		torch.device(a   )
							lowerCAmelCase__      :      List[Any]         =		randn_tensor(output.shape    ,     device=a   )
							lowerCAmelCase__      :      List[Any]         =		torch.nn.functional.mse_loss(a    ,     a   )
							loss.backward()