




# 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












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()






