
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    _snake_case  :
							# setable values
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			Optional[int]    ):






												"""simple docstring"""



												return cls()






@dataclass
class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						None






class    _snake_case  (						_lowercase ,				_lowercase       ):






							@property
							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												return True






							@register_to_config
							def __init__(					self						:			Tuple						, snake_case__						:			int = 2_0_0_0						, snake_case__						:			float = 0.15						, snake_case__						:			float = 0.01						, snake_case__						:			float = 1_3_4_8.0						, snake_case__						:			float = 1e-5						, snake_case__						:			int = 1						, ):






												"""simple docstring"""



												pass






							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					ScoreSdeVeSchedulerState.create()
												return self.set_sigmas(
												    snake_case__						, self.config.num_train_timesteps						, self.config.sigma_min						, self.config.sigma_max						, self.config.sampling_eps						, )






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			int						, snake_case__						:			Tuple = ()						, snake_case__						:			float = None    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__a:						List[Any]		    =					jnp.linspace(1						, snake_case__						, snake_case__    )
												return state.replace(timesteps=snake_case__    )






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			int						, snake_case__						:			float = None						, snake_case__						:			float = None						, snake_case__						:			float = None						, ):






												"""simple docstring"""



												__a:						Tuple		    =					sigma_min if sigma_min is not None else self.config.sigma_min
												__a:						List[Any]		    =					sigma_max if sigma_max is not None else self.config.sigma_max
												__a:						List[str]		    =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if state.timesteps is None:
																	__a:						Optional[Any]		    =					self.set_timesteps(snake_case__						, snake_case__						, snake_case__    )

												__a:						Union[str, Any]		    =					jnp.exp(jnp.linspace(jnp.log(snake_case__    )						, jnp.log(snake_case__    )						, snake_case__    )    )
												__a:						str		    =					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]    )

												return state.replace(discrete_sigmas=snake_case__						, sigmas=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			List[Any]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												return jnp.where(timesteps == 0						, jnp.zeros_like(snake_case__    )						, state.discrete_sigmas[timesteps - 1]    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			jnp.ndarray						, snake_case__						:			int						, snake_case__						:			jnp.ndarray						, snake_case__						:			random.KeyArray						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												if state.timesteps is None:
																	raise ValueError(
																	    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

												__a:						Tuple		    =					timestep * jnp.ones(
												    sample.shape[0]						, )
												__a:						Any		    =					(timestep * (len(state.timesteps    ) - 1)).long()

												__a:						Union[str, Any]		    =					state.discrete_sigmas[timesteps]
												__a:						List[str]		    =					self.get_adjacent_sigma(snake_case__						, snake_case__						, snake_case__    )
												__a:						int		    =					jnp.zeros_like(snake_case__    )
												__a:						Tuple		    =					(sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												__a:						Tuple		    =					diffusion.flatten()
												__a:						Tuple		    =					broadcast_to_shape_from_left(snake_case__						, sample.shape    )
												__a:						List[str]		    =					drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__a:						Optional[Any]		    =					random.split(snake_case__						, num=1    )
												__a:						Dict		    =					random.normal(key=snake_case__						, shape=sample.shape    )
												__a:						List[str]		    =					sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__a:						Tuple		    =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

												return FlaxSdeVeOutput(prev_sample=snake_case__						, prev_sample_mean=snake_case__						, state=snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			jnp.ndarray						, snake_case__						:			jnp.ndarray						, snake_case__						:			random.KeyArray						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												if state.timesteps is None:
																	raise ValueError(
																	    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												__a:						Tuple		    =					random.split(snake_case__						, num=1    )
												__a:						Tuple		    =					random.normal(key=snake_case__						, shape=sample.shape    )

												# compute step size from the model_output, the noise, and the snr
												__a:						List[Any]		    =					jnp.linalg.norm(snake_case__    )
												__a:						List[str]		    =					jnp.linalg.norm(snake_case__    )
												__a:						Union[str, Any]		    =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__a:						str		    =					step_size * jnp.ones(sample.shape[0]    )

												# compute corrected sample: model_output term and noise term
												__a:						Dict		    =					step_size.flatten()
												__a:						Optional[int]		    =					broadcast_to_shape_from_left(snake_case__						, sample.shape    )
												__a:						Tuple		    =					sample + step_size * model_output
												__a:						Dict		    =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																	return (prev_sample, state)

												return FlaxSdeVeOutput(prev_sample=snake_case__						, state=snake_case__    )






							def __len__(					self						:			Tuple    ):






												"""simple docstring"""



												return self.config.num_train_timesteps


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
