




'''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


a       :						Dict								=		8


def __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[int]		,				_UpperCAmelCase		:   Any=BITS						)      ->      Any:
			__snake_case			=		x.device

			__snake_case			=		(x * 2_55).int().clamp(0		,				2_55						)

			__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 __UpperCAmelCase	(   _UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   Union[str, Any]=BITS						)      ->      List[Any]:
			__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 / 2_55).clamp(0.0		,				1.0						)


def __UpperCAmelCase	(   self		:   Optional[Any]		,				_UpperCAmelCase		:   torch.FloatTensor		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   torch.FloatTensor		,				_UpperCAmelCase		:   float = 0.0		,				_UpperCAmelCase		:   bool = True		,				_UpperCAmelCase		:   int=None		,				_UpperCAmelCase		:   bool = True		,				)      ->      Union[DDIMSchedulerOutput, Tuple]:
			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 __UpperCAmelCase	(   self		:   Tuple		,				_UpperCAmelCase		:   torch.FloatTensor		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   torch.FloatTensor		,				_UpperCAmelCase		:   Dict="epsilon"		,				_UpperCAmelCase		:   Dict=None		,				_UpperCAmelCase		:   bool = True		,				)      ->      Union[DDPMSchedulerOutput, Tuple]:
			__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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





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



						"""simple docstring"""




						super().__init__()
						__snake_case			=		bit_scale
						__snake_case			=		(
						    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						:    int      ,		a_						:    Optional[int] = 256      ,		a_						:    Optional[int] = 256      ,		a_						:    Optional[int] = 50      ,		a_						:    Optional[torch.Generator] = None      ,		a_						:    Optional[int] = 1      ,		a_						:    Optional[str] = "pil"      ,		a_						:    bool = True      ,		**a_						:    Dict      ,		):



						"""simple docstring"""




						__snake_case			=		torch.randn(
						    (batch_size, self.unet.config.in_channels, height, width)      ,		generator=a_      ,		)
						__snake_case			=		decimal_to_bits(a_							) * self.bit_scale
						__snake_case			=		latents.to(self.device							)

						self.scheduler.set_timesteps(a_							)

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

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

						__snake_case			=		bits_to_decimal(a_							)

						if output_type == "pil":
									__snake_case			=		self.numpy_to_pil(a_							)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=a_							)











import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
      __magic_name__  :  List[Any]					=       """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

      print("""Googling.....""")

      __magic_name__  :  List[Any]					=       F"https://www.google.com/search?q={query}&num=100"

      __magic_name__  :  Union[str, Any]					=       requests.get(
          url,
          headers={"""User-Agent""": str(UserAgent().random)},
      )

      try:
            __magic_name__  :  Optional[Any]					=       (
                BeautifulSoup(res.text, """html.parser""")
                .find("""div""", attrs={"""class""": """yuRUbf"""})
                .find("""a""")
                .get("""href""")
            )

      except AttributeError:
            __magic_name__  :  List[Any]					=       parse_qs(
                BeautifulSoup(res.text, """html.parser""")
                .find("""div""", attrs={"""class""": """kCrYT"""})
                .find("""a""")
                .get("""href""")
            )["""url"""][0]

      webbrowser.open(link)

