



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__					:     Tuple    		=   8



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=BITS   ):
	__lowercase		       =			x.device

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

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

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



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=BITS   ):
	__lowercase		       =			x.device

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

	__lowercase		       =			rearrange(SCREAMING_SNAKE_CASE_  ,	"d -> d 1 1"   )
	__lowercase		       =			rearrange(SCREAMING_SNAKE_CASE_  ,	"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    snake_case_				(    self  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0.0  ,	_SCREAMING_SNAKE_CASE = True  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE = 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(SCREAMING_SNAKE_CASE_  ,	-scale  ,	SCREAMING_SNAKE_CASE_   )

	# 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(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_   )
	__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(SCREAMING_SNAKE_CASE_   ) else 'cpu'
		__lowercase		       =			torch.randn(model_output.shape  ,	dtype=model_output.dtype  ,	generator=SCREAMING_SNAKE_CASE_   ).to(SCREAMING_SNAKE_CASE_   )
		__lowercase		       =			self._get_variance(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_   ) ** 0.5 * eta * noise

		__lowercase		       =			prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,	pred_original_sample=SCREAMING_SNAKE_CASE_   )



def    snake_case_				(    self  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE="epsilon"  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE = True  ,	):
	__lowercase		       =			timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		__lowercase		       =			torch.split(SCREAMING_SNAKE_CASE_  ,	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(SCREAMING_SNAKE_CASE_  ,	-scale  ,	SCREAMING_SNAKE_CASE_   )

	# 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=SCREAMING_SNAKE_CASE_   ).to(model_output.device   )
		__lowercase		       =			(self._get_variance(SCREAMING_SNAKE_CASE_  ,	predicted_variance=SCREAMING_SNAKE_CASE_   ) ** 0.5) * noise

	__lowercase		       =			pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,	pred_original_sample=SCREAMING_SNAKE_CASE_   )

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							str							,      lowerCamelCase	:							UNetaDConditionModel							,      lowerCamelCase	:							Union[DDIMScheduler, DDPMScheduler]							,      lowerCamelCase	:							Optional[float] = 1.0							,      ):






							'''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	:							List[Any]							,      lowerCamelCase	:							Optional[int] = 256							,      lowerCamelCase	:							Optional[int] = 256							,      lowerCamelCase	:							Optional[int] = 50							,      lowerCamelCase	:							Optional[torch.Generator] = None							,      lowerCamelCase	:							Optional[int] = 1							,      lowerCamelCase	:							Optional[str] = "pil"							,      lowerCamelCase	:							bool = True							,      **lowerCamelCase	:							List[Any]							,      ):






							'''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	)








from ....utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



