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        UpperCamelCase						:
						# setable values
						a__		:Optional[int]      =			None
						a__		:Optional[jnp.ndarray]      =			None
						a__		:Optional[jnp.ndarray]      =			None  # sigma(t_i)





						@classmethod
						def        A_							(cls						)				->				str:
												return cls()


@dataclass
class        UpperCamelCase						(				__a				):
						a__		:jnp.ndarray
						a__		:jnp.ndarray
						a__		:KarrasVeSchedulerState


class        UpperCamelCase						(				__a	,	__a				):





						@property
						def        A_							(self						)				->				Union[str, Any]:
												return True





						@register_to_config
						def __init__(self			,     __UpperCamelCase = 0.02			,     __UpperCamelCase = 100			,     __UpperCamelCase = 1.007			,     __UpperCamelCase = 80			,     __UpperCamelCase = 0.05			,     __UpperCamelCase = 50			,     )				->				Any:
												pass





						def        A_							(self						)				->				Dict:
												return KarrasVeSchedulerState.create()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = ()						)				->				KarrasVeSchedulerState:
												UpperCamelCase_		:							str  =		jnp.arange(0			,     __UpperCamelCase						)[::-1].copy()
												UpperCamelCase_		:							Optional[int]  =		[
												    (
												        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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     )				->				Tuple[jnp.ndarray, float]:
												if self.config.s_min <= sigma <= self.config.s_max:
																		UpperCamelCase_		:							Optional[Any]  =		min(self.config.s_churn / state.num_inference_steps			,     2**0.5 - 1						)
												else:
																		UpperCamelCase_		:							List[Any]  =		0

												# sample eps ~ N(0, S_noise^2 * I)
												UpperCamelCase_		:							Tuple  =		random.split(__UpperCamelCase			,     num=1						)
												UpperCamelCase_		:							Optional[int]  =		self.config.s_noise * random.normal(key=__UpperCamelCase			,     shape=sample.shape						)
												UpperCamelCase_		:							int  =		sigma + gamma * sigma
												UpperCamelCase_		:							int  =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = True			,     )				->				Union[FlaxKarrasVeOutput, Tuple]:
												UpperCamelCase_		:							Optional[int]  =		sample_hat + sigma_hat * model_output
												UpperCamelCase_		:							Dict  =		(sample_hat - pred_original_sample) / sigma_hat
												UpperCamelCase_		:							List[str]  =		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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = True			,     )				->				Union[FlaxKarrasVeOutput, Tuple]:
												UpperCamelCase_		:							Union[str, Any]  =		sample_prev + sigma_prev * model_output
												UpperCamelCase_		:							str  =		(sample_prev - pred_original_sample) / sigma_prev
												UpperCamelCase_		:							Any  =		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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Dict:
												raise NotImplementedError()







from heapq import heappop, heappush

import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				bool		, ):
						UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		grid.shape
						UpperCamelCase_		:							List[str]  =		[-1, 1, 0, 0]
						UpperCamelCase_		:							Dict  =		[0, 0, -1, 1]
						if allow_diagonal:
												dx += [-1, -1, 1, 1]
												dy += [-1, 1, -1, 1]

						UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		[(0, source)], set()
						UpperCamelCase_		:							Any  =		np.full((rows, cols)		, np.inf			)
						UpperCamelCase_		:							List[str]  =		0
						UpperCamelCase_		:							Union[str, Any]  =		np.empty((rows, cols)		, dtype=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[int]  =		None

						while queue:
												((UpperCamelCase_),(UpperCamelCase_))		:							Any  =		heappop(_SCREAMING_SNAKE_CASE			)
												if (x, y) in visited:
																		continue
												visited.add((x, y)			)

												if (x, y) == destination:
																		UpperCamelCase_		:							Tuple  =		[]
																		while (x, y) != source:
																								path.append((x, y)			)
																								UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		predecessors[x, y]
																		path.append(_SCREAMING_SNAKE_CASE			)  # add the source manually
																		path.reverse()
																		return matrix[destination], path

												for i in range(len(_SCREAMING_SNAKE_CASE			)			):
																		UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		x + dx[i], y + dy[i]
																		if 0 <= nx < rows and 0 <= ny < cols:
																								UpperCamelCase_		:							Union[str, Any]  =		grid[nx][ny]
																								if next_node == 1 and matrix[nx, ny] > dist + 1:
																														heappush(_SCREAMING_SNAKE_CASE		, (dist + 1, (nx, ny))			)
																														UpperCamelCase_		:							Optional[Any]  =		dist + 1
																														UpperCamelCase_		:							List[Any]  =		(x, y)

						return np.inf, []


if __name__ == "__main__":
	import doctest

	doctest.testmod()






