





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_							=			8


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=BITS		):
		lowerCamelCase   : List[str]								=x.device

		lowerCamelCase   : Tuple								=(x * 2_5_5).int().clamp(0							,       2_5_5		)

		lowerCamelCase   : int								=2 ** torch.arange(bits - 1							,       -1							,       -1							,       device=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Union[str, Any]								=rearrange(SCREAMING_SNAKE_CASE_							,       '''d -> d 1 1'''		)
		lowerCamelCase   : Union[str, Any]								=rearrange(SCREAMING_SNAKE_CASE_							,       '''b c h w -> b c 1 h w'''		)

		lowerCamelCase   : Any								=((x & mask) != 0).float()
		lowerCamelCase   : Optional[int]								=rearrange(SCREAMING_SNAKE_CASE_							,       '''b c d h w -> b (c d) h w'''		)
		lowerCamelCase   : List[Any]								=bits * 2 - 1
		return bits


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=BITS		):
		lowerCamelCase   : Union[str, Any]								=x.device

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

		lowerCamelCase   : List[Any]								=rearrange(SCREAMING_SNAKE_CASE_							,       '''d -> d 1 1'''		)
		lowerCamelCase   : List[str]								=rearrange(SCREAMING_SNAKE_CASE_							,       '''b (c d) h w -> b c d h w'''							,       d=8		)
		lowerCamelCase   : List[str]								=reduce(x * mask							,       '''b c d h w -> b c h w'''							,       '''sum'''		)
		return (dec / 2_5_5).clamp(0.0							,       1.0		)


def 					A__	(       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)
		lowerCamelCase   : Union[str, Any]								=timestep - self.config.num_train_timesteps // self.num_inference_steps

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

		lowerCamelCase   : 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
		lowerCamelCase   : Tuple								=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

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

				lowerCamelCase   : Any								=prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_							,       pred_original_sample=SCREAMING_SNAKE_CASE_		)


def 					A__	(       self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_="epsilon"							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_ = True							,       ):
		lowerCamelCase   : str								=timestep

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

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

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

		# 6. Add noise
		lowerCamelCase   : Union[str, Any]								=0
		if t > 0:
				lowerCamelCase   : Optional[int]								=torch.randn(
				    model_output.size()							,       dtype=model_output.dtype							,       layout=model_output.layout							,       generator=SCREAMING_SNAKE_CASE_		).to(model_output.device		)
				lowerCamelCase   : int								=(self._get_variance(SCREAMING_SNAKE_CASE_							,       predicted_variance=SCREAMING_SNAKE_CASE_		) ** 0.5) * noise

		lowerCamelCase   : Tuple								=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        snake_case_		(			_A):







		def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase = 1.0			,     )				->   Tuple:
				super().__init__()
				lowerCamelCase   : List[str]								=bit_scale
				lowerCamelCase   : List[Any]								=(
				    ddim_bit_scheduler_step if isinstance(__lowercase			,     __lowercase   ) else ddpm_bit_scheduler_step
				)

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







		@torch.no_grad()
		def __call__(		self			,     __lowercase = 2_5_6			,     __lowercase = 2_5_6			,     __lowercase = 5_0			,     __lowercase = None			,     __lowercase = 1			,     __lowercase = "pil"			,     __lowercase = True			,     **__lowercase			,     )				->   Union[Tuple, ImagePipelineOutput]:
				lowerCamelCase   : Dict								=torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)			,     generator=__lowercase			,     )
				lowerCamelCase   : Optional[Any]								=decimal_to_bits(__lowercase   ) * self.bit_scale
				lowerCamelCase   : List[str]								=latents.to(self.device   )

				self.scheduler.set_timesteps(__lowercase   )

				for t in self.progress_bar(self.scheduler.timesteps   ):
						# predict the noise residual
						lowerCamelCase   : Optional[int]								=self.unet(__lowercase			,     __lowercase   ).sample

						# compute the previous noisy sample x_t -> x_t-1
						lowerCamelCase   : Optional[int]								=self.scheduler.step(__lowercase			,     __lowercase			,     __lowercase   ).prev_sample

				lowerCamelCase   : List[str]								=bits_to_decimal(__lowercase   )

				if output_type == "pil":
						lowerCamelCase   : Optional[int]								=self.numpy_to_pil(__lowercase   )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=__lowercase   )








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

