
"""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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class     _UpperCAmelCase:
		# setable values
		lowercase__						=      None
		lowercase__						=      None
		lowercase__						=      None  # sigma(t_i)



		@classmethod
		def 						UpperCAmelCase							(					cls)       ->       Optional[Any]:






							'''simple docstring'''
							return cls()






@dataclass
class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      42
		lowercase__						=      42
		lowercase__						=      42






class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase	):



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






							'''simple docstring'''
							return True



		@register_to_config
		def __init__(					self				,		__a = 0.02				,		__a = 1_00				,		__a = 1.007				,		__a = 80				,		__a = 0.05				,		__a = 50				,		)       ->       Union[str, Any]:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							return KarrasVeSchedulerState.create()



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = ())       ->       KarrasVeSchedulerState:






							'''simple docstring'''
							_UpperCamelCase							 =  jnp.arange(0				,		__a)[::-1].copy()
							_UpperCamelCase							 =  [
							    (
							        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=__a				,		schedule=jnp.array(__a				,		dtype=jnp.floataa)				,		timesteps=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Tuple[jnp.ndarray, float]:






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

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

							return sample_hat, sigma_hat



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a = True				,		)       ->       Union[FlaxKarrasVeOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase							 =  sample_hat + sigma_hat * model_output
							_UpperCamelCase							 =  (sample_hat - pred_original_sample) / sigma_hat
							_UpperCamelCase							 =  sample_hat + (sigma_prev - sigma_hat) * derivative

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

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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a = True				,		)       ->       Union[FlaxKarrasVeOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase							 =  sample_prev + sigma_prev * model_output
							_UpperCamelCase							 =  (sample_prev - pred_original_sample) / sigma_prev
							_UpperCamelCase							 =  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=__a				,		derivative=__a				,		state=__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       Tuple:






							'''simple docstring'''
							raise NotImplementedError()








"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






