






"""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


_lowerCAmelCase         =	8






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=BITS					):
	A_		:						Tuple	    =   x.device

	A_		:						Any	    =   (x * 255).int().clamp(0   ,   255					)

	A_		:						Optional[int]	    =   2 ** torch.arange(bits - 1   ,   -1   ,   -1   ,   device=__UpperCAmelCase					)
	A_		:						List[str]	    =   rearrange(__UpperCAmelCase   ,   """d -> d 1 1"""					)
	A_		:						Optional[int]	    =   rearrange(__UpperCAmelCase   ,   """b c h w -> b c 1 h w"""					)

	A_		:						Any	    =   ((x & mask) != 0).float()
	A_		:						List[str]	    =   rearrange(__UpperCAmelCase   ,   """b c d h w -> b (c d) h w"""					)
	A_		:						int	    =   bits * 2 - 1
	return bits






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=BITS					):
	A_		:						Union[str, Any]	    =   x.device

	A_		:						Union[str, Any]	    =   (x > 0).int()
	A_		:						Optional[int]	    =   2 ** torch.arange(bits - 1   ,   -1   ,   -1   ,   device=__UpperCAmelCase   ,   dtype=torch.intaa					)

	A_		:						str	    =   rearrange(__UpperCAmelCase   ,   """d -> d 1 1"""					)
	A_		:						Dict	    =   rearrange(__UpperCAmelCase   ,   """b (c d) h w -> b c d h w"""   ,   d=8					)
	A_		:						Dict	    =   reduce(x * mask   ,   """b c d h w -> b c h w"""   ,   """sum"""					)
	return (dec / 255).clamp(0.0   ,   1.0					)






def 			__UpperCamelCase	(		self   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__ = 0.0   ,   snake_case__ = True   ,   snake_case__=None   ,   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)
	A_		:						int	    =   timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	A_		:						Union[str, Any]	    =   self.alphas_cumprod[timestep]
	A_		:						Union[str, Any]	    =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	A_		:						Optional[int]	    =   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_		:						int	    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	A_		:						Optional[Any]	    =   self.bit_scale
	if self.config.clip_sample:
		A_		:						Union[str, Any]	    =   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_		:						Union[str, Any]	    =   self._get_variance(__UpperCAmelCase   ,   __UpperCAmelCase					)
	A_		:						int	    =   eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		A_		:						int	    =   (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_		:						List[str]	    =   (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_		:						str	    =   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_		:						Optional[Any]	    =   model_output.device if torch.is_tensor(__UpperCAmelCase					) else """cpu"""
		A_		:						List[str]	    =   torch.randn(model_output.shape   ,   dtype=model_output.dtype   ,   generator=__UpperCAmelCase					).to(__UpperCAmelCase					)
		A_		:						int	    =   self._get_variance(__UpperCAmelCase   ,   __UpperCAmelCase					) ** 0.5 * eta * noise

		A_		:						Optional[Any]	    =   prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase   ,   pred_original_sample=__UpperCAmelCase					)






def 			__UpperCamelCase	(		self   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__="epsilon"   ,   snake_case__=None   ,   snake_case__ = True   ,   ):
	A_		:						int	    =   timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		A_    ,     A_		:						Optional[Any]	    =   torch.split(__UpperCAmelCase   ,   sample.shape[1]   ,   dim=1					)
	else:
		A_		:						Tuple	    =   None

	# 1. compute alphas, betas
	A_		:						str	    =   self.alphas_cumprod[t]
	A_		:						List[str]	    =   self.alphas_cumprod[t - 1] if t > 0 else self.one
	A_		:						List[Any]	    =   1 - alpha_prod_t
	A_		:						Dict	    =   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_		:						Union[str, Any]	    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		A_		:						List[Any]	    =   model_output
	else:
		raise ValueError(F"""Unsupported prediction_type {prediction_type}."""					)

	# 3. Clip "predicted x_0"
	A_		:						Union[str, Any]	    =   self.bit_scale
	if self.config.clip_sample:
		A_		:						Union[str, Any]	    =   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_		:						Optional[Any]	    =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	A_		:						List[str]	    =   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_		:						Optional[int]	    =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

	A_		:						List[str]	    =   pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase   ,   pred_original_sample=__UpperCAmelCase					)




class      SCREAMING_SNAKE_CASE  (				_snake_case						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = 1.0     ,     ):
		super().__init__()
		A_		:						Any	    =   bit_scale
		A_		:						Tuple	    =   (
		    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_     ,     ):
		A_		:						Any	    =   torch.randn(
		    (batch_size, self.unet.config.in_channels, height, width)     ,     generator=lowerCAmelCase_     ,     )
		A_		:						int	    =   decimal_to_bits(lowerCAmelCase_							) * self.bit_scale
		A_		:						str	    =   latents.to(self.device							)

		self.scheduler.set_timesteps(lowerCAmelCase_							)

		for t in self.progress_bar(self.scheduler.timesteps							):
			# predict the noise residual
			A_		:						Union[str, Any]	    =   self.unet(lowerCAmelCase_     ,     lowerCAmelCase_							).sample

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

		A_		:						Dict	    =   bits_to_decimal(lowerCAmelCase_							)

		if output_type == "pil":
			A_		:						List[Any]	    =   self.numpy_to_pil(lowerCAmelCase_							)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=lowerCAmelCase_							)









"""simple docstring"""
import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   get_activation("""swish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   get_activation("""silu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   get_activation("""mish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.Mish							)

		self.assertEqual(act(torch.tensor(-200     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   get_activation("""gelu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.GELU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)

