code: 
  
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    42
  
  				# setable values
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    None
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      List[Any]:
  											return cls(
  											    common=snake_case_		,		final_alpha_cumprod=snake_case_		,		init_noise_sigma=snake_case_		,		timesteps=snake_case_		,		)
  
  
  
  
  
  
  
  @dataclass
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  				lowerCamelCase__									=    42
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__	):
  				lowerCamelCase__									=    [e.name for e in FlaxKarrasDiffusionSchedulers]
  
  				lowerCamelCase__									=    42
  
  
  
  
  				@property
  				def     __a		( self							)    ->      Tuple:
  											return True
  
  
  
  
  				@register_to_config
  				def __init__( self		,		snake_case_ = 1_000		,		snake_case_ = 0.0001		,		snake_case_ = 0.02		,		snake_case_ = "linear"		,		snake_case_ = None		,		snake_case_ = True		,		snake_case_ = 0		,		snake_case_ = "epsilon"		,		snake_case_ = jnp.floataa		,		)    ->      int:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =dtype
  
  
  
  
  				def     __a		( self		,		snake_case_ = None							)    ->      DDIMSchedulerState:
  											if common is None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =CommonSchedulerState.create(self							)
  
  											# At every step in ddim, we are looking into the previous alphas_cumprod
  											# For the final step, there is no previous alphas_cumprod because we are already at 0
  											# `set_alpha_to_one` decides whether we set this parameter simply to one or
  											# whether we use the final alpha of the "non-previous" one.
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(
  											    jnp.array(1.0		,		dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  											)
  
  											# standard deviation of the initial noise distribution
  											SCREAMING_SNAKE_CASE			: List[str]	     =jnp.array(1.0		,		dtype=self.dtype							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =jnp.arange(0		,		self.config.num_train_timesteps							).round()[::-1]
  
  											return DDIMSchedulerState.create(
  											    common=snake_case_		,		final_alpha_cumprod=snake_case_		,		init_noise_sigma=snake_case_		,		timesteps=snake_case_		,		)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_ = None							)    ->      jnp.ndarray:
  											return sample
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_ = ()							)    ->      DDIMSchedulerState:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.config.num_train_timesteps // num_inference_steps
  											# creates integer timesteps by multiplying by ratio
  											# rounding to avoid issues when num_inference_step is power of 3
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =(jnp.arange(0		,		snake_case_							) * step_ratio).round()[::-1] + self.config.steps_offset
  
  											return state.replace(
  											    num_inference_steps=snake_case_		,		timesteps=snake_case_		,		)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =state.common.alphas_cumprod[timestep]
  											SCREAMING_SNAKE_CASE			: Any	     =jnp.where(
  											    prev_timestep >= 0		,		state.common.alphas_cumprod[prev_timestep]		,		state.final_alpha_cumprod							)
  											SCREAMING_SNAKE_CASE			: Dict	     =1 - alpha_prod_t
  											SCREAMING_SNAKE_CASE			: Dict	     =1 - alpha_prod_t_prev
  
  											SCREAMING_SNAKE_CASE			: str	     =(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  											return variance
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_ = 0.0		,		snake_case_ = True		,		)    ->      Union[FlaxDDIMSchedulerOutput, Tuple]:
  											if state.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)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =state.common.alphas_cumprod
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =state.final_alpha_cumprod
  
  											# 2. compute alphas, betas
  											SCREAMING_SNAKE_CASE			: List[Any]	     =alphas_cumprod[timestep]
  											SCREAMING_SNAKE_CASE			: Any	     =jnp.where(prev_timestep >= 0		,		alphas_cumprod[prev_timestep]		,		snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =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
  											if self.config.prediction_type == "epsilon":
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  																		SCREAMING_SNAKE_CASE			: Any	     =model_output
  											elif self.config.prediction_type == "sample":
  																		SCREAMING_SNAKE_CASE			: Tuple	     =model_output
  																		SCREAMING_SNAKE_CASE			: str	     =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  											elif self.config.prediction_type == "v_prediction":
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  																		SCREAMING_SNAKE_CASE			: Tuple	     =(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  											else:
  																		raise ValueError(
  																		    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
  																		    ''' `v_prediction`'''							)
  
  											# 4. 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)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self._get_variance(snake_case_		,		snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Dict	     =eta * variance ** 0.5
  
  											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  											SCREAMING_SNAKE_CASE			: List[str]	     =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  											if not return_dict:
  																		return (prev_sample, state)
  
  											return FlaxDDIMSchedulerOutput(prev_sample=snake_case_		,		state=snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      jnp.ndarray:
  											return add_noise_common(state.common		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      jnp.ndarray:
  											return get_velocity_common(state.common		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  
  
  
  
  
  
  				def __len__( self							)    ->      str:
  											return self.config.num_train_timesteps
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  def 						lowerCAmelCase_       (	__a = "mytext.txt"	,	__a = False )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							import socket
  
  							SCREAMING_SNAKE_CASE			: int	     =12312  # Reserve a port for your service.
  							SCREAMING_SNAKE_CASE			: Dict	     =socket.socket()  # Create a socket object
  							SCREAMING_SNAKE_CASE			: Any	     =socket.gethostname()  # Get local machine name
  							sock.bind((host, port) )  # Bind to the port
  							sock.listen(5 )  # Now wait for client connection.
  
  							print('''Server listening....''' )
  
  							while True:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =sock.accept()  # Establish connection with client.
  														print(f'Got connection from {addr}' )
  														SCREAMING_SNAKE_CASE			: List[str]	     =conn.recv(1024 )
  														print(f'Server received: {data = }' )
  
  														with open(__a	,	'''rb''' ) as in_file:
  																					SCREAMING_SNAKE_CASE			: Dict	     =in_file.read(1024 )
  																					while data:
  																												conn.send(__a )
  																												print(f'Sent {data!r}' )
  																												SCREAMING_SNAKE_CASE			: str	     =in_file.read(1024 )
  
  														print('''Done sending''' )
  														conn.close()
  														if testing:  # Allow the test to complete
  																					break
  
  							sock.shutdown(1 )
  							sock.close()
  
  
  if __name__ == "__main__":
  							send_file()
  
  
  
  
  
  
  