




from __future__ import annotations

from queue import Queue






class 	A	:
     """simple docstring"""

     def __init__(			self     :      int	) ->				None:
           lowerCamelCase__					     = {}




     def        __snake_case      (			self     :      Any	) ->				None:
           for i in self.vertices:
                 print(A__		,     ' : '		,     ' -> '.join([str(A__	) for j in self.vertices[i]]	)	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      int		,     A__     :      int	) ->				None:
           if from_vertex in self.vertices:
                 self.vertices[from_vertex].append(A__	)
           else:
                 lowerCamelCase__					     = [to_vertex]




     def        __snake_case      (			self     :      Any		,     A__     :      int	) ->				set[int]:
           lowerCamelCase__					     = set()

           # create a first in first out queue to store all the vertices for BFS
           lowerCamelCase__					     = Queue()

           # mark the source node as visited and enqueue it
           visited.add(A__	)
           queue.put(A__	)

           while not queue.empty():
                 lowerCamelCase__					     = queue.get()

                 # loop through all adjacent vertex and enqueue it if not yet visited
                 for adjacent_vertex in self.vertices[vertex]:
                       if adjacent_vertex not in visited:
                             queue.put(A__	)
                             visited.add(A__	)
           return visited


if __name__ == "__main__":
      from doctest import testmod

      testmod(verbose=True)

      __SCREAMING_SNAKE_CASE			:      Dict      			=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()
      # 0  :  1 -> 2
      # 1  :  2
      # 2  :  0 -> 3
      # 3  :  3

      assert sorted(g.bfs(2)) == [0, 1, 2, 3]





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

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






@flax.struct.dataclass
class 	A	:
     """simple docstring"""

     _snake_case:		CommonSchedulerState
     _snake_case:		jnp.ndarray

     # setable values
     _snake_case:		jnp.ndarray
     _snake_case:		jnp.ndarray
     _snake_case:		Optional[int]   =None




     @classmethod
     def        __snake_case      (			cls     :      Optional[int]		,     A__     :      CommonSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				Any:
           return cls(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )






@dataclass
class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		DDIMSchedulerState






class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		List[str]   =[e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case:		jnp.dtype




     @property
     def        __snake_case      (			self     :      Optional[int]	) ->				List[str]:
           return True




     @register_to_config
     def __init__(			self     :      Optional[Any]		,     A__     :      int = 1_0_0_0		,     A__     :      float = 0.0001		,     A__     :      float = 0.02		,     A__     :      str = "linear"		,     A__     :      Optional[jnp.ndarray] = None		,     A__     :      bool = True		,     A__     :      int = 0		,     A__     :      str = "epsilon"		,     A__     :      jnp.dtype = jnp.floataa		,     ) ->				Optional[int]:
           lowerCamelCase__					     = dtype




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Optional[CommonSchedulerState] = None	) ->				DDIMSchedulerState:
           if common is None:
                 lowerCamelCase__					     = CommonSchedulerState.create(self	)

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           lowerCamelCase__					     = (
               jnp.array(1.0		,     dtype=self.dtype	) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

           # standard deviation of the initial noise distribution
           lowerCamelCase__					     = jnp.array(1.0		,     dtype=self.dtype	)

           lowerCamelCase__					     = jnp.arange(0		,     self.config.num_train_timesteps	).round()[::-1]

           return DDIMSchedulerState.create(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      Optional[int] = None	) ->				jnp.ndarray:
           return sample




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      DDIMSchedulerState		,     A__     :      int		,     A__     :      Tuple = ()	) ->				DDIMSchedulerState:
           lowerCamelCase__					     = self.config.num_train_timesteps // num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # rounding to avoid issues when num_inference_step is power of 3
           lowerCamelCase__					     = (jnp.arange(0		,     A__	) * step_ratio).round()[::-1] + self.config.steps_offset

           return state.replace(
               num_inference_steps=A__		,     timesteps=A__		,     )




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      DDIMSchedulerState		,     A__     :      List[str]		,     A__     :      Optional[Any]	) ->				int:
           lowerCamelCase__					     = state.common.alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(
               prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod	)
           lowerCamelCase__					     = 1 - alpha_prod_t
           lowerCamelCase__					     = 1 - alpha_prod_t_prev

           lowerCamelCase__					     = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance




     def        __snake_case      (			self     :      List[str]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      int		,     A__     :      jnp.ndarray		,     A__     :      float = 0.0		,     A__     :      bool = True		,     ) ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
           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'	)

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           lowerCamelCase__					     = timestep - self.config.num_train_timesteps // state.num_inference_steps

           lowerCamelCase__					     = state.common.alphas_cumprod
           lowerCamelCase__					     = state.final_alpha_cumprod

           # 2. compute alphas, betas
           lowerCamelCase__					     = alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     A__	)

           lowerCamelCase__					     = 1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCamelCase__					     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 lowerCamelCase__					     = model_output
           elif self.config.prediction_type == "sample":
                 lowerCamelCase__					     = model_output
                 lowerCamelCase__					     = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                 raise ValueError(
                     F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                     ' `v_prediction`'	)

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           lowerCamelCase__					     = self._get_variance(A__		,     A__		,     A__	)
           lowerCamelCase__					     = eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                 return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=A__		,     state=A__	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return add_noise_common(state.common		,     A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return get_velocity_common(state.common		,     A__		,     A__		,     A__	)




     def __len__(			self     :      Any	) ->				Union[str, Any]:
           return self.config.num_train_timesteps