"""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				:		str	   =       8





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	Tuple=BITS     )	->				List[Any]:
  _lowerCAmelCase						:	Optional[int]        =					x.device

  _lowerCAmelCase						:	Tuple        =					(x * 255).int().clamp(0      ,255     )

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

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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]=BITS     )	->				Optional[int]:
  _lowerCAmelCase						:	List[Any]        =					x.device

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

  _lowerCAmelCase						:	Optional[Any]        =					rearrange(_lowerCamelCase      ,"""d -> d 1 1"""     )
  _lowerCAmelCase						:	int        =					rearrange(_lowerCamelCase      ,"""b (c d) h w -> b c d h w"""      ,d=8     )
  _lowerCAmelCase						:	Any        =					reduce(x * mask      ,"""b c d h w -> b c h w"""      ,"""sum"""     )
  return (dec / 255).clamp(0.0      ,1.0     )





def 							SCREAMING_SNAKE_CASE					(      self						:	Tuple      ,_lowerCamelCase						:	torch.FloatTensor      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	torch.FloatTensor      ,_lowerCamelCase						:	float = 0.0      ,_lowerCamelCase						:	bool = True      ,_lowerCamelCase						:	List[str]=None      ,_lowerCamelCase						:	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)
  _lowerCAmelCase						:	Dict        =					timestep - self.config.num_train_timesteps // self.num_inference_steps

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

  _lowerCAmelCase						:	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						:	Dict        =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  _lowerCAmelCase						:	Tuple        =					self.bit_scale
  if self.config.clip_sample:
    _lowerCAmelCase						:	Union[str, Any]        =					torch.clamp(_lowerCamelCase      ,-scale      ,_lowerCamelCase     )

  # 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						:	List[Any]        =					self._get_variance(_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					eta * variance ** 0.5

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

    _lowerCAmelCase						:	Dict        =					prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCamelCase      ,pred_original_sample=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      self						:	str      ,_lowerCamelCase						:	torch.FloatTensor      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	torch.FloatTensor      ,_lowerCamelCase						:	Dict="epsilon"      ,_lowerCamelCase						:	Tuple=None      ,_lowerCamelCase						:	bool = True      ,)	->				Union[DDPMSchedulerOutput, Tuple]:
  _lowerCAmelCase						:	Dict        =					timestep

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

  # 1. compute alphas, betas
  _lowerCAmelCase						:	str        =					self.alphas_cumprod[t]
  _lowerCAmelCase						:	List[Any]        =					self.alphas_cumprod[t - 1] if t > 0 else self.one
  _lowerCAmelCase						:	Optional[Any]        =					1 - alpha_prod_t
  _lowerCAmelCase						:	Optional[int]        =					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						:	str        =					model_output
  else:
    raise ValueError(f"Unsupported prediction_type {prediction_type}."     )

  # 3. Clip "predicted x_0"
  _lowerCAmelCase						:	List[str]        =					self.bit_scale
  if self.config.clip_sample:
    _lowerCAmelCase						:	Union[str, Any]        =					torch.clamp(_lowerCamelCase      ,-scale      ,_lowerCamelCase     )

  # 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						:	Optional[Any]        =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  _lowerCAmelCase						:	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						:	List[str]        =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  _lowerCAmelCase						:	Union[str, Any]        =					pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCamelCase      ,pred_original_sample=_lowerCamelCase     )






class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__    ,					a__ = 1.0    ,					):
       super().__init__()
       _lowerCAmelCase						:	Any        =					bit_scale
       _lowerCAmelCase						:	int        =					(
           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__    ,					):
       _lowerCAmelCase						:	Tuple        =					torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)    ,					generator=a__    ,					)
       _lowerCAmelCase						:	Optional[int]        =					decimal_to_bits(a__  ) * self.bit_scale
       _lowerCAmelCase						:	List[str]        =					latents.to(self.device  )

       self.scheduler.set_timesteps(a__  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
         # predict the noise residual
         _lowerCAmelCase						:	List[Any]        =					self.unet(a__    ,					a__  ).sample

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase						:	List[str]        =					self.scheduler.step(a__    ,					a__    ,					a__  ).prev_sample

       _lowerCAmelCase						:	Dict        =					bits_to_decimal(a__  )

       if output_type == "pil":
         _lowerCAmelCase						:	Tuple        =					self.numpy_to_pil(a__  )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__  )





"""simple docstring"""




import socket





def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					socket.socket(socket.AF_INET      ,socket.SOCK_STREAM     )
  _lowerCAmelCase						:	Optional[int]        =					socket.gethostname()
  _lowerCAmelCase						:	Tuple        =					12312

  sock.connect((host, port)     )
  sock.send(b"""Hello server!"""     )

  with open("""Received_file"""      ,"""wb"""     ) as out_file:
    print("""File opened"""     )
    print("""Receiving data..."""     )
    while True:
      _lowerCAmelCase						:	List[Any]        =					sock.recv(1024     )
      if not data:
        break
      out_file.write(_lowerCamelCase     )

  print("""Successfully received the file"""     )
  sock.close()
  print("""Connection closed"""     )


if __name__ == "__main__":
     main()




