
'''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


lowercase    :							Optional[int]      =					8







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Any:
	_snake_case   						=		x.device

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

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

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Dict:
	_snake_case   						=		x.device

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

	_snake_case   						=		rearrange(__UpperCamelCase			,				'd -> d 1 1'	)
	_snake_case   						=		rearrange(__UpperCamelCase			,				'b (c d) h w -> b c d h w'			,				d=8	)
	_snake_case   						=		reduce(x * mask			,				'b c d h w -> b c h w'			,				'sum'	)
	return (dec / 255).clamp(0.0			,				1.0	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A = 0.0			,				__A = True			,				__A=None			,				__A = True			,				)       ->	Optional[Any]:







	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)
	_snake_case   						=		timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

	# 4. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		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)
	_snake_case   						=		self._get_variance(__UpperCamelCase			,				__UpperCamelCase	)
	_snake_case   						=		eta * variance ** 0.5

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

		_snake_case   						=		prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=__UpperCamelCase			,				pred_original_sample=__UpperCamelCase	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A="epsilon"			,				__A=None			,				__A = True			,				)       ->	Dict:
	_snake_case   						=		timestep

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

	# 1. compute alphas, betas
	_snake_case   						=		self.alphas_cumprod[t]
	_snake_case   						=		self.alphas_cumprod[t - 1] if t > 0 else self.one
	_snake_case   						=		1 - alpha_prod_t
	_snake_case   						=		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":
		_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		_snake_case   						=		model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'	)

	# 3. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		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
	_snake_case   						=		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	_snake_case   						=		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
	_snake_case   						=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

	_snake_case   						=		pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=__UpperCamelCase			,				pred_original_sample=__UpperCamelCase	)





class 						__UpperCAmelCase      (					_lowerCAmelCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = 1.0    ,   ):





								"""simple docstring"""






								super().__init__()
								_snake_case   						=		bit_scale
								_snake_case   						=		(
								    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_ = 2_56    ,   lowerCAmelCase_ = 2_56    ,   lowerCAmelCase_ = 50    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 1    ,   lowerCAmelCase_ = "pil"    ,   lowerCAmelCase_ = True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






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

								self.scheduler.set_timesteps(_lowerCamelCase  )

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

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

								_snake_case   						=		bits_to_decimal(_lowerCamelCase  )

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

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=_lowerCamelCase  )









"""simple docstring"""


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
       import ctypes
       import msvcrt  # noqa


       class 					snake_case		(							ctypes.Structure						):


          '''simple docstring'''

          A_						:	List[str]						 = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l''' )
    sys.stdout.flush()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h''' )
    sys.stdout.flush()





@contextmanager
def 					lowerCAmelCase				(    ):




  """simple docstring"""







  try:
    hide_cursor()
    yield
  finally:
    show_cursor()




