


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		(DDIMParallelScheduler,)
    __magic_name__        =		(('eta', 0.0), ('num_inference_steps', 50))
    def 		a_      (	self      , **__snake_case   ):
         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**__snake_case   )
         return config
    def 		a_      (	self      , **__snake_case   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config(**__snake_case   )
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case			,  snake_case					=    1_0, 0.0

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter

         scheduler.set_timesteps(__snake_case   )

         for t in scheduler.timesteps:
              snake_case					=    model(__snake_case      , __snake_case   )
              snake_case					=    scheduler.step(__snake_case      , __snake_case      , __snake_case      , __snake_case   ).prev_sample

         return sample
    def 		a_      (	self   ):
         for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=__snake_case   )
    def 		a_      (	self   ):
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__snake_case   )

         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config(steps_offset=1   )
         snake_case					=    scheduler_class(**__snake_case   )
         scheduler.set_timesteps(5   )
         assert torch.equal(scheduler.timesteps      , torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]   )   )
    def 		a_      (	self   ):
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]   ):
              self.check_over_configs(beta_start=__snake_case      , beta_end=__snake_case   )
    def 		a_      (	self   ):
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__snake_case   )
    def 		a_      (	self   ):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__snake_case   )
    def 		a_      (	self   ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__snake_case   )
    def 		a_      (	self   ):
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__snake_case   )
    def 		a_      (	self   ):
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__snake_case   )
    def 		a_      (	self   ):
         self.check_over_configs(thresholding=__snake_case   )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__snake_case      , prediction_type=__snake_case      , sample_max_value=__snake_case      , )
    def 		a_      (	self   ):
         for t in [1, 1_0, 4_9]:
              self.check_over_forward(time_step=__snake_case   )
    def 		a_      (	self   ):
         for t, num_inference_steps in zip([1, 1_0, 5_0]      , [1_0, 5_0, 5_0_0]   ):
              self.check_over_forward(time_step=__snake_case      , num_inference_steps=__snake_case   )
    def 		a_      (	self   ):
         for t, eta in zip([1, 1_0, 4_9]      , [0.0, 0.5, 1.0]   ):
              self.check_over_forward(time_step=__snake_case      , eta=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         assert torch.sum(torch.abs(scheduler._get_variance(0      , 0   ) - 0.0   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      , 4_0_0   ) - 0.1_4771   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      , 9_6_0   ) - 0.3_2460   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0      , 0   ) - 0.0   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      , 4_8_6   ) - 0.0_0979   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      , 9_9_8   ) - 0.02   )   ) < 1E-5
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case			,  snake_case					=    1_0, 0.0
         scheduler.set_timesteps(__snake_case   )

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter
         snake_case					=    self.dummy_sample_deter + 0.1
         snake_case					=    self.dummy_sample_deter - 0.1

         snake_case					=    samplea.shape[0]
         snake_case					=    torch.stack([samplea, samplea, samplea]      , dim=0   )
         snake_case					=    torch.arange(__snake_case   )[0:3, None].repeat(1      , __snake_case   )

         snake_case					=    model(samples.flatten(0      , 1   )      , timesteps.flatten(0      , 1   )   )
         snake_case					=    scheduler.batch_step_no_noise(__snake_case      , timesteps.flatten(0      , 1   )      , samples.flatten(0      , 1   )      , __snake_case   )

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 1147.7904   ) < 1E-2
         assert abs(result_mean.item() - 0.4982   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.full_loop()

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 172.0067   ) < 1E-2
         assert abs(result_mean.item() - 0.22_3967   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.full_loop(prediction_type='''v_prediction'''   )

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 52.5302   ) < 1E-2
         assert abs(result_mean.item() - 0.0684   ) < 1E-3
    def 		a_      (	self   ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case					=    self.full_loop(set_alpha_to_one=__snake_case      , beta_start=0.01   )
         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 149.8295   ) < 1E-2
         assert abs(result_mean.item() - 0.1951   ) < 1E-3




    def 		a_      (	self   ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case					=    self.full_loop(set_alpha_to_one=__snake_case      , beta_start=0.01   )
         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 149.0784   ) < 1E-2
         assert abs(result_mean.item() - 0.1941   ) < 1E-3








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			Any      				=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

              for node in successors:
                   self.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__snake_case   ) - 1):
                   continue

              if grid[pos_y][pos_x] != 0:
                   continue

              successors.append(
                  Node(__snake_case      , __snake_case      , self.target.pos_y      , self.target.pos_x      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

              for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                        bfs.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.fwd_bfs.start.pos]
         return None




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

 print("Unidirectional BFS computation time : ", bfs_time)

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

 print("Bidirectional BFS computation time : ", bd_bfs_time)




