





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


_SCREAMING_SNAKE_CASE					=    8




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Tuple=BITS				):
			__lowercase										=							x.device

			__lowercase										=							(x * 2_5_5).int().clamp(0		,       2_5_5				)

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

			__lowercase										=							((x & mask) != 0).float()
			__lowercase										=							rearrange(lowerCamelCase_		,       '''b c d h w -> b (c d) h w'''				)
			__lowercase										=							bits * 2 - 1
			return bits




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  List[str]=BITS				):
			__lowercase										=							x.device

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

			__lowercase										=							rearrange(lowerCamelCase_		,       '''d -> d 1 1'''				)
			__lowercase										=							rearrange(lowerCamelCase_		,       '''b (c d) h w -> b c d h w'''		,       d=8				)
			__lowercase										=							reduce(x * mask		,       '''b c d h w -> b c h w'''		,       '''sum'''				)
			return (dec / 2_5_5).clamp(0.0		,       1.0				)




def    _lowerCAmelCase						(				self			:  Tuple		,       lowerCamelCase_			:  torch.FloatTensor		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  torch.FloatTensor		,       lowerCamelCase_			:  float = 0.0		,       lowerCamelCase_			:  bool = True		,       lowerCamelCase_			:  Tuple=None		,       lowerCamelCase_			:  bool = True		,       ):
			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)
			__lowercase										=							timestep - self.config.num_train_timesteps // self.num_inference_steps

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

			__lowercase										=							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
			__lowercase										=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

			# 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)
			__lowercase										=							self._get_variance(lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						__lowercase										=							(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
			__lowercase										=							(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
			__lowercase										=							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
						__lowercase										=							model_output.device if torch.is_tensor(lowerCamelCase_				) else '''cpu'''
						__lowercase										=							torch.randn(model_output.shape		,       dtype=model_output.dtype		,       generator=lowerCamelCase_				).to(lowerCamelCase_				)
						__lowercase										=							self._get_variance(lowerCamelCase_		,       lowerCamelCase_				) ** 0.5 * eta * noise

						__lowercase										=							prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_				)




def    _lowerCAmelCase						(				self			:  str		,       lowerCamelCase_			:  torch.FloatTensor		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  torch.FloatTensor		,       lowerCamelCase_			:  Optional[int]="epsilon"		,       lowerCamelCase_			:  Dict=None		,       lowerCamelCase_			:  bool = True		,       ):
			__lowercase										=							timestep

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

			# 1. compute alphas, betas
			__lowercase										=							self.alphas_cumprod[t]
			__lowercase										=							self.alphas_cumprod[t - 1] if t > 0 else self.one
			__lowercase										=							1 - alpha_prod_t
			__lowercase										=							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":
						__lowercase										=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						__lowercase										=							model_output
			else:
						raise ValueError(f"Unsupported prediction_type {prediction_type}."				)

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

			# 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
			__lowercase										=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			__lowercase										=							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
			__lowercase										=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

			__lowercase										=							pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_				)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = 1.0     ,)							->							int:







				'''simple docstring'''





				super().__init__()
				__lowercase										=							bit_scale
				__lowercase										=							(
				    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     ,_lowerCamelCase = 256     ,_lowerCamelCase = 256     ,_lowerCamelCase = 50     ,_lowerCamelCase = None     ,_lowerCamelCase = 1     ,_lowerCamelCase = "pil"     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							Union[Tuple, ImagePipelineOutput]:







				'''simple docstring'''





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

				self.scheduler.set_timesteps(_lowerCamelCase )

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

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

				__lowercase										=							bits_to_decimal(_lowerCamelCase )

				if output_type == "pil":
							__lowercase										=							self.numpy_to_pil(_lowerCamelCase )

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_lowerCamelCase )












'''simple docstring'''



import math




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			assert isinstance(lowerCamelCase_		,       lowerCamelCase_				) and (
			    number >= 0
			), "'number' must been an int and positive"

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or not number % 2:
						# Negatives, 0, 1 and all even numbers are not primes
						return False

			__lowercase										=							range(3		,       int(math.sqrt(lowerCamelCase_				) + 1				)		,       2				)
			return not any(not number % i for i in odd_numbers				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Any=1		,       **lowerCamelCase_			:  Tuple				):
			__lowercase										=							factor * value
			__lowercase										=							value

			while not is_prime(lowerCamelCase_				):
						value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

			if value == first_value_val:
						return next_prime(value + 1		,       **lowerCamelCase_				)
			return value





