




'''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


UpperCamelCase_						:      Optional[Any]       =						8





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[str]=BITS   )						->			List[Any]:

					"""simple docstring"""




					_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      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Dict=BITS   )						->			Optional[Any]:

					"""simple docstring"""




					_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      __a     (       self:   Any ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   int ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   float = 0.0 ,						_UpperCamelCase:   bool = True ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   bool = 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)
					_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      __a     (       self:   Optional[Any] ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   int ,						_UpperCamelCase:   torch.FloatTensor ,						_UpperCamelCase:   Dict="epsilon" ,						_UpperCamelCase:   Dict=None ,						_UpperCamelCase:   bool = True ,						)						->			Union[DDPMSchedulerOutput, Tuple]:

					"""simple docstring"""




					_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        _a    (						__lowerCAmelCase							):






					def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 1.0 ,)							->      Tuple:
										super().__init__()
										_snake_case							=				bit_scale
										_snake_case							=				(
										    ddim_bit_scheduler_step if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else ddpm_bit_scheduler_step
										)

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






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

										self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

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

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

										_snake_case							=				bits_to_decimal(_SCREAMING_SNAKE_CASE   )

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

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





import pprint

import requests

UpperCamelCase_						:      Tuple       =						'''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_						:      Any       =						random_quotes()
   pprint.pprint(response)


