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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class   lowercase      :
							# setable values
							lowercase__  :    Optional[int]									=							None
							lowercase__  :    Optional[jnp.ndarray]									=							None
							lowercase__  :    Optional[jnp.ndarray]									=							None  # sigma(t_i)




							@classmethod
							def 		__snake_case(  cls				:   int )   ->			List[Any]:



												'''simple docstring'''

												return cls()



@dataclass
class   lowercase      (       a       ):
							lowercase__  :    jnp.ndarray
							lowercase__  :    jnp.ndarray
							lowercase__  :    KarrasVeSchedulerState



class   lowercase      (       a			,    a       ):




							@property
							def 		__snake_case(  self				:   str )   ->			List[str]:



												'''simple docstring'''

												return True




							@register_to_config
							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   float = 0.0_2		,							_UpperCamelCase				:   float = 100		,							_UpperCamelCase				:   float = 1.0_0_7		,							_UpperCamelCase				:   float = 80		,							_UpperCamelCase				:   float = 0.0_5		,							_UpperCamelCase				:   float = 50		,							)   ->			Union[str, Any]:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   int )   ->			Union[str, Any]:



												'''simple docstring'''

												return KarrasVeSchedulerState.create()




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   KarrasVeSchedulerState		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Tuple = () )   ->			KarrasVeSchedulerState:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    jnp.arange(0		,							_UpperCamelCase )[::-1].copy()
												SCREAMING_SNAKE_CASE           =    [
												    (
												        self.config.sigma_max**2
												        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
												    )
												    for i in timesteps
												]

												return state.replace(
												    num_inference_steps=_UpperCamelCase		,							schedule=jnp.array(_UpperCamelCase		,							dtype=jnp.floataa )		,							timesteps=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   KarrasVeSchedulerState		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   random.KeyArray		,							)   ->			Tuple[jnp.ndarray, float]:



												'''simple docstring'''

												if self.config.s_min <= sigma <= self.config.s_max:
																	SCREAMING_SNAKE_CASE           =    min(self.config.s_churn / state.num_inference_steps		,							2**0.5 - 1 )
												else:
																	SCREAMING_SNAKE_CASE           =    0

												# sample eps ~ N(0, S_noise^2 * I)
												SCREAMING_SNAKE_CASE           =    random.split(_UpperCamelCase		,							num=1 )
												SCREAMING_SNAKE_CASE           =    self.config.s_noise * random.normal(key=_UpperCamelCase		,							shape=sample.shape )
												SCREAMING_SNAKE_CASE           =    sigma + gamma * sigma
												SCREAMING_SNAKE_CASE           =    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   KarrasVeSchedulerState		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   bool = True		,							)   ->			Union[FlaxKarrasVeOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    sample_hat + sigma_hat * model_output
												SCREAMING_SNAKE_CASE           =    (sample_hat - pred_original_sample) / sigma_hat
												SCREAMING_SNAKE_CASE           =    sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																	return (sample_prev, derivative, state)

												return FlaxKarrasVeOutput(prev_sample=_UpperCamelCase		,							derivative=_UpperCamelCase		,							state=_UpperCamelCase )




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   KarrasVeSchedulerState		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   jnp.ndarray		,							_UpperCamelCase				:   bool = True		,							)   ->			Union[FlaxKarrasVeOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    sample_prev + sigma_prev * model_output
												SCREAMING_SNAKE_CASE           =    (sample_prev - pred_original_sample) / sigma_prev
												SCREAMING_SNAKE_CASE           =    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

												if not return_dict:
																	return (sample_prev, derivative, state)

												return FlaxKarrasVeOutput(prev_sample=_UpperCamelCase		,							derivative=_UpperCamelCase		,							state=_UpperCamelCase )





							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   KarrasVeSchedulerState		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict )   ->			int:



												'''simple docstring'''

												raise NotImplementedError()





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




