




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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       __a					:


     """simple docstring"""


     _A				:    Optional[jnp.ndarray]									=  None
     _A				:    Optional[jnp.ndarray]									=  None
     _A				:    Optional[jnp.ndarray]									=  None




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


         '''simple docstring'''





         return cls()






@dataclass
class       __a					( __lowerCamelCase     ):


     """simple docstring"""


     _A				:    ScoreSdeVeSchedulerState
     _A				:    jnp.ndarray
     _A				:    Optional[jnp.ndarray]									=  None






class       __a					( __lowerCamelCase							,     __lowerCamelCase     ):


     """simple docstring"""


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


         '''simple docstring'''





         return True




     @register_to_config
     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: int = 2_0_0_0				,_UpperCamelCase			: float = 0.15				,_UpperCamelCase			: float = 0.01				,_UpperCamelCase			: float = 1348.0				,_UpperCamelCase			: float = 1e-5				,_UpperCamelCase			: int = 1				,)		->   Optional[int]:


         '''simple docstring'''





         pass




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ScoreSdeVeSchedulerState.create()
         return self.set_sigmas(
             _UpperCamelCase				,self.config.num_train_timesteps				,self.config.sigma_min				,self.config.sigma_max				,self.config.sampling_eps				,)




     def   __A				(	self			: int				,_UpperCamelCase			: ScoreSdeVeSchedulerState				,_UpperCamelCase			: int				,_UpperCamelCase			: Tuple = ()				,_UpperCamelCase			: float = None						)		->   ScoreSdeVeSchedulerState:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=sampling_eps if sampling_eps is not None else self.config.sampling_eps

         SCREAMING_SNAKE_CASE__								=jnp.linspace(1				,_UpperCamelCase				,_UpperCamelCase						)
         return state.replace(timesteps=_UpperCamelCase						)




     def   __A				(	self			: Dict				,_UpperCamelCase			: ScoreSdeVeSchedulerState				,_UpperCamelCase			: int				,_UpperCamelCase			: float = None				,_UpperCamelCase			: float = None				,_UpperCamelCase			: float = None				,)		->   ScoreSdeVeSchedulerState:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=sigma_min if sigma_min is not None else self.config.sigma_min
         SCREAMING_SNAKE_CASE__								=sigma_max if sigma_max is not None else self.config.sigma_max
         SCREAMING_SNAKE_CASE__								=sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if state.timesteps is None:
             SCREAMING_SNAKE_CASE__								=self.set_timesteps(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=jnp.exp(jnp.linspace(jnp.log(_UpperCamelCase						)				,jnp.log(_UpperCamelCase						)				,_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__								=jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)

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




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: Optional[Any]				,_UpperCamelCase			: int				,_UpperCamelCase			: Any						)		->   List[Any]:


         '''simple docstring'''





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




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: ScoreSdeVeSchedulerState				,_UpperCamelCase			: jnp.ndarray				,_UpperCamelCase			: int				,_UpperCamelCase			: jnp.ndarray				,_UpperCamelCase			: random.KeyArray				,_UpperCamelCase			: bool = True				,)		->   Union[FlaxSdeVeOutput, Tuple]:


         '''simple docstring'''





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

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

         SCREAMING_SNAKE_CASE__								=state.discrete_sigmas[timesteps]
         SCREAMING_SNAKE_CASE__								=self.get_adjacent_sigma(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=jnp.zeros_like(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=(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
         SCREAMING_SNAKE_CASE__								=diffusion.flatten()
         SCREAMING_SNAKE_CASE__								=broadcast_to_shape_from_left(_UpperCamelCase				,sample.shape						)
         SCREAMING_SNAKE_CASE__								=drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         SCREAMING_SNAKE_CASE__								=random.split(_UpperCamelCase				,num=1						)
         SCREAMING_SNAKE_CASE__								=random.normal(key=_UpperCamelCase				,shape=sample.shape						)
         SCREAMING_SNAKE_CASE__								=sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         SCREAMING_SNAKE_CASE__								=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=_UpperCamelCase				,prev_sample_mean=_UpperCamelCase				,state=_UpperCamelCase						)




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: ScoreSdeVeSchedulerState				,_UpperCamelCase			: jnp.ndarray				,_UpperCamelCase			: jnp.ndarray				,_UpperCamelCase			: random.KeyArray				,_UpperCamelCase			: bool = True				,)		->   Union[FlaxSdeVeOutput, Tuple]:


         '''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
         SCREAMING_SNAKE_CASE__								=random.split(_UpperCamelCase				,num=1						)
         SCREAMING_SNAKE_CASE__								=random.normal(key=_UpperCamelCase				,shape=sample.shape						)

         # compute step size from the model_output, the noise, and the snr
         SCREAMING_SNAKE_CASE__								=jnp.linalg.norm(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=jnp.linalg.norm(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=(self.config.snr * noise_norm / grad_norm) ** 2 * 2
         SCREAMING_SNAKE_CASE__								=step_size * jnp.ones(sample.shape[0]						)

         # compute corrected sample: model_output term and noise term
         SCREAMING_SNAKE_CASE__								=step_size.flatten()
         SCREAMING_SNAKE_CASE__								=broadcast_to_shape_from_left(_UpperCamelCase				,sample.shape						)
         SCREAMING_SNAKE_CASE__								=sample + step_size * model_output
         SCREAMING_SNAKE_CASE__								=prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
             return (prev_sample, state)

         return FlaxSdeVeOutput(prev_sample=_UpperCamelCase				,state=_UpperCamelCase						)




     def __len__(	self			: List[Any]						)		->   Optional[int]:


         '''simple docstring'''





         return self.config.num_train_timesteps












from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T")
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: int				,_UpperCamelCase			: T						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=data  # Assign data
         SCREAMING_SNAKE_CASE__								=None  # Initialize next as null
         SCREAMING_SNAKE_CASE__								=None  # Initialize prev as null






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Tuple						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: Dict				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         if self.head is None:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=new_node
             SCREAMING_SNAKE_CASE__								=self.head
             SCREAMING_SNAKE_CASE__								=None
             SCREAMING_SNAKE_CASE__								=new_node




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         if self.head is None:
             return None
         else:
             assert self.head is not None
             SCREAMING_SNAKE_CASE__								=self.head.data
             SCREAMING_SNAKE_CASE__								=self.head.next
             if self.head is not None:
                 SCREAMING_SNAKE_CASE__								=None
             return temp




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         return self.head.data if self.head is not None else None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.head
         SCREAMING_SNAKE_CASE__								=0
         while temp is not None:
             count += 1
             SCREAMING_SNAKE_CASE__								=temp.next
         return count




     def   __A				(	self			: List[Any]						)		->   bool:


         '''simple docstring'''





         return self.head is None




     def   __A				(	self			: int						)		->   None:


         '''simple docstring'''





         print("""stack elements are:"""						)
         SCREAMING_SNAKE_CASE__								=self.head
         while temp is not None:
             print(temp.data				,end="""->"""						)
             SCREAMING_SNAKE_CASE__								=temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    lowerCamelCase_  						=   Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print("Stack operations using Doubly LinkedList")
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print("\nTop element is ", stack.top())

    # Print the stack size
    print("Size of the stack is ", len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print("\nstack is empty:", stack.is_empty())






