






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 				__a				:





		'''simple docstring'''



		UpperCAmelCase__   :					List[str]					     =    42

		# setable values
		UpperCAmelCase__   :					Tuple					     =    42
		UpperCAmelCase__   :					Union[str, Any]					     =    42
		UpperCAmelCase__   :					Optional[Any]					     =    42
		UpperCAmelCase__   :					Tuple					     =    None

		# running values
		UpperCAmelCase__   :					str					     =    None


		@classmethod
		def 	__snake_case      (    cls			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						return cls(common=__A			,     init_noise_sigma=__A			,     timesteps=__A			,     sigmas=__A							)






@dataclass
class 				__a				(	a__    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    42






class 				__a				(	a__			,						a__    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Any					     =    [e.name for e in FlaxKarrasDiffusionSchedulers]

		UpperCAmelCase__   :					Optional[int]					     =    42


		@property
		def 	__snake_case      (    self							):
						return True


		@register_to_config
		def __init__(    self			,     UpperCamelCase__ = 1000			,     UpperCamelCase__ = 0.00_01			,     UpperCamelCase__ = 0.02			,     UpperCamelCase__ = "linear"			,     UpperCamelCase__ = None			,     UpperCamelCase__ = "epsilon"			,     UpperCamelCase__ = jnp.floataa			,     ):
						SCREAMING_SNAKE_CASE_ :						int    				= dtype


		def 	__snake_case      (    self			,     UpperCamelCase__ = None							):







						if common is None:
										SCREAMING_SNAKE_CASE_ :						Dict    				= CommonSchedulerState.create(self							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= jnp.arange(0			,     self.config.num_train_timesteps							).round()[::-1]
						SCREAMING_SNAKE_CASE_ :						List[str]    				= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						SCREAMING_SNAKE_CASE_ :						Dict    				= sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=__A			,     init_noise_sigma=__A			,     timesteps=__A			,     sigmas=__A			,     )


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						(SCREAMING_SNAKE_CASE_       ) :						Optional[Any]    				= jnp.where(state.timesteps == timestep			,     size=1							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= step_index[0]

						SCREAMING_SNAKE_CASE_ :						Any    				= state.sigmas[step_index]
						SCREAMING_SNAKE_CASE_ :						str    				= sample / ((sigma**2 + 1) ** 0.5)
						return sample


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):







						def lms_derivative(UpperCamelCase__							):
										SCREAMING_SNAKE_CASE_ :						Any    				= 1.0
										for k in range(__A							):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						SCREAMING_SNAKE_CASE_ :						int    				= integrate.quad(__A			,     state.sigmas[t]			,     state.sigmas[t + 1]			,     epsrel=1E-4							)[0]

						return integrated_coeff


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__ = ()							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= jnp.linspace(self.config.num_train_timesteps - 1			,     0			,     __A			,     dtype=self.dtype							)

						SCREAMING_SNAKE_CASE_ :						Any    				= jnp.floor(__A							).astype(jnp.intaa							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= jnp.ceil(__A							).astype(jnp.intaa							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= jnp.mod(__A			,     1.0							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						SCREAMING_SNAKE_CASE_ :						str    				= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						SCREAMING_SNAKE_CASE_ :						str    				= jnp.concatenate([sigmas, jnp.array([0.0]			,     dtype=self.dtype							)]							)

						SCREAMING_SNAKE_CASE_ :						int    				= timesteps.astype(jnp.intaa							)

						# initial running values
						SCREAMING_SNAKE_CASE_ :						Tuple    				= jnp.zeros((0,) + shape			,     dtype=self.dtype							)

						return state.replace(
						    timesteps=__A			,     sigmas=__A			,     num_inference_steps=__A			,     derivatives=__A			,     )


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__ = 4			,     UpperCamelCase__ = True			,     ):







						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'							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

						# 2. Convert to an ODE derivative
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= (sample - pred_original_sample) / sigma
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= state.replace(derivatives=jnp.append(state.derivatives			,     __A							)							)
						if len(state.derivatives							) > order:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= state.replace(derivatives=jnp.delete(state.derivatives			,     0							)							)

						# 3. Compute linear multistep coefficients
						SCREAMING_SNAKE_CASE_ :						Any    				= min(timestep + 1			,     __A							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= [self.get_lms_coefficient(__A			,     __A			,     __A			,     __A							) for curr_order in range(__A							)]

						# 4. Compute previous sample based on the derivatives path
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= sample + sum(
						    coeff * derivative for coeff, derivative in zip(__A			,     reversed(state.derivatives							)							)							)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=__A			,     state=__A							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						Any    				= state.sigmas[timesteps].flatten()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= broadcast_to_shape_from_left(__A			,     noise.shape							)

						SCREAMING_SNAKE_CASE_ :						Dict    				= original_samples + noise * sigma

						return noisy_samples






		def __len__(    self							):
						return self.config.num_train_timesteps



'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





