
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


_UpperCAmelCase :			Union[str, Any]              =	8


def 				A	( lowercase   ,	lowercase=BITS			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					x.device

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

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

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


def 				A	( lowercase   ,	lowercase=BITS			)       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					x.device

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

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


def 				A	( self   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase = 0.0   ,	lowercase = True   ,	lowercase=None   ,	lowercase = True   ,	)       ->  Union[DDIMSchedulerOutput, Tuple]:
		'''simple docstring'''

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

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

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

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

		# 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)
		UpperCamelCase          =					self._get_variance(lowercase   ,	lowercase			)
		UpperCamelCase          =					eta * variance ** 0.5

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

				UpperCamelCase          =					prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=lowercase   ,	pred_original_sample=lowercase			)


def 				A	( self   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase="epsilon"   ,	lowercase=None   ,	lowercase = True   ,	)       ->  Union[DDPMSchedulerOutput, Tuple]:
		'''simple docstring'''

		UpperCamelCase          =					timestep

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

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

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

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

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

		UpperCamelCase          =					pred_prev_sample + variance

		if not return_dict:
				return (pred_prev_sample,)

		return DDPMSchedulerOutput(prev_sample=lowercase   ,	pred_original_sample=lowercase			)


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_ = 1.0    ,				)  -> Union[str, Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					bit_scale
				UpperCamelCase          =					(
				    ddim_bit_scheduler_step if isinstance(A_    ,				A_	) else ddpm_bit_scheduler_step
				)

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



		@torch.no_grad()
		def __call__(			self    ,				A_ = 256    ,				A_ = 256    ,				A_ = 50    ,				A_ = None    ,				A_ = 1    ,				A_ = "pil"    ,				A_ = True    ,				**A_    ,				)  -> Union[Tuple, ImagePipelineOutput]:
				"""simple docstring"""
				UpperCamelCase          =					torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)    ,				generator=A_    ,				)
				UpperCamelCase          =					decimal_to_bits(A_	) * self.bit_scale
				UpperCamelCase          =					latents.to(self.device	)

				self.scheduler.set_timesteps(A_	)

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

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

				UpperCamelCase          =					bits_to_decimal(A_	)

				if output_type == "pil":
						UpperCamelCase          =					self.numpy_to_pil(A_	)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_	)


import pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
