




'''simple docstring'''


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_snake_case : Optional[Any]  				=						8
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Dict=BITS ):





							'''simple docstring'''


							_a      	=							x.device

							_a      	=							(x * 255).int().clamp(0					,		255 )

							_a      	=							2 ** torch.arange(bits - 1					,		-1					,		-1					,		device=UpperCamelCase )
							_a      	=							rearrange(UpperCamelCase					,		'''d -> d 1 1''' )
							_a      	=							rearrange(UpperCamelCase					,		'''b c h w -> b c 1 h w''' )

							_a      	=							((x & mask) != 0).float()
							_a      	=							rearrange(UpperCamelCase					,		'''b c d h w -> b (c d) h w''' )
							_a      	=							bits * 2 - 1
							return bits
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Any=BITS ):





							'''simple docstring'''


							_a      	=							x.device

							_a      	=							(x > 0).int()
							_a      	=							2 ** torch.arange(bits - 1					,		-1					,		-1					,		device=UpperCamelCase					,		dtype=torch.intaa )

							_a      	=							rearrange(UpperCamelCase					,		'''d -> d 1 1''' )
							_a      	=							rearrange(UpperCamelCase					,		'''b (c d) h w -> b c d h w'''					,		d=8 )
							_a      	=							reduce(x * mask					,		'''b c d h w -> b c h w'''					,		'''sum''' )
							return (dec / 255).clamp(0.0					,		1.0 )
def 						snake_case_       (self			:    Union[str, Any]					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    float = 0.0					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    Any=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							if self.num_inference_steps is None:
														raise ValueError(
														    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							_a      	=							timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							_a      	=							self.alphas_cumprod[timestep]
							_a      	=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							_a      	=							1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							torch.clamp(UpperCamelCase					,		-scale					,		UpperCamelCase )

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase )
							_a      	=							eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														_a      	=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														_a      	=							model_output.device if torch.is_tensor(UpperCamelCase ) else '''cpu'''
														_a      	=							torch.randn(model_output.shape					,		dtype=model_output.dtype					,		generator=UpperCamelCase ).to(UpperCamelCase )
														_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase ) ** 0.5 * eta * noise

														_a      	=							prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )
def 						snake_case_       (self			:    Any					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    str="epsilon"					,		UpperCamelCase			:    Dict=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							_a      	=							timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														_a	,				_a      	=							torch.split(UpperCamelCase					,		sample.shape[1]					,		dim=1 )
							else:
														_a      	=							None

							# 1. compute alphas, betas
							_a      	=							self.alphas_cumprod[t]
							_a      	=							self.alphas_cumprod[t - 1] if t > 0 else self.one
							_a      	=							1 - alpha_prod_t
							_a      	=							1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														_a      	=							model_output
							else:
														raise ValueError(f'Unsupported prediction_type {prediction_type}.' )

							# 3. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							torch.clamp(UpperCamelCase					,		-scale					,		UpperCamelCase )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_a      	=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							_a      	=							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_a      	=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							_a      	=							0
							if t > 0:
														_a      	=							torch.randn(
														    model_output.size()					,		dtype=model_output.dtype					,		layout=model_output.layout					,		generator=UpperCamelCase ).to(model_output.device )
														_a      	=							(self._get_variance(UpperCamelCase					,		predicted_variance=UpperCamelCase ) ** 0.5) * noise

							_a      	=							pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )

class      A       (						_a       ):







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    UNetaDConditionModel				,     lowerCAmelCase_			:    Union[DDIMScheduler, DDPMScheduler]				,     lowerCAmelCase_			:    Optional[float] = 1.0				,     )     ->				int:


														"""simple docstring"""


														super().__init__()
														_a      	=							bit_scale
														_a      	=							(
														    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ) else ddpm_bit_scheduler_step
														)

														self.register_modules(unet=lowerCAmelCase_				,     scheduler=lowerCAmelCase_   )







							@torch.no_grad()
							def __call__(    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 50				,     lowerCAmelCase_			:    Optional[torch.Generator] = None				,     lowerCAmelCase_			:    Optional[int] = 1				,     lowerCAmelCase_			:    Optional[str] = "pil"				,     lowerCAmelCase_			:    bool = True				,     **lowerCAmelCase_			:    Any				,     )     ->				Union[Tuple, ImagePipelineOutput]:


														"""simple docstring"""


														_a      	=							torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)				,     generator=lowerCAmelCase_				,     )
														_a      	=							decimal_to_bits(lowerCAmelCase_   ) * self.bit_scale
														_a      	=							latents.to(self.device   )

														self.scheduler.set_timesteps(lowerCAmelCase_   )

														for t in self.progress_bar(self.scheduler.timesteps   ):
																					# predict the noise residual
																					_a      	=							self.unet(lowerCAmelCase_				,     lowerCAmelCase_   ).sample

																					# compute the previous noisy sample x_t -> x_t-1
																					_a      	=							self.scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   ).prev_sample

														_a      	=							bits_to_decimal(lowerCAmelCase_   )

														if output_type == "pil":
																					_a      	=							self.numpy_to_pil(lowerCAmelCase_   )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_   )



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







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







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







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







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

				with self.assertRaises(lowercase   ,     msg='''`custom_timesteps` must be in descending order.'''			):
							scheduler.set_timesteps(timesteps=lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				with self.assertRaises(lowercase   ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''			):
							scheduler.set_timesteps(num_inference_steps=lowercase   ,     timesteps=lowercase			)







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    lowercase   ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,     ):
							scheduler.set_timesteps(timesteps=lowercase			)