

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class    A_				( a_  ):


    '''simple docstring'''






    a__       							=    (DDIMScheduler,)
    a__       							=    (("eta", 0.0), ("num_inference_steps", 50))


    def        lowerCAmelCase_  (self       , **lowercase__					)       -> Optional[int]:
        __UpperCAmelCase			   =     {
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**snake_case__					)
        return config


    def        lowerCAmelCase_  (self       , **lowercase__					)       -> int:
        __UpperCAmelCase			   =     self.scheduler_classes[0]
        __UpperCAmelCase			   =     self.get_scheduler_config(**snake_case__					)
        __UpperCAmelCase			   =     scheduler_class(**snake_case__					)

        __UpperCAmelCase   ,       __UpperCAmelCase			   =     10, 0.0

        __UpperCAmelCase			   =     self.dummy_model()
        __UpperCAmelCase			   =     self.dummy_sample_deter

        scheduler.set_timesteps(snake_case__					)

        for t in scheduler.timesteps:
            __UpperCAmelCase			   =     model(snake_case__       , snake_case__					)
            __UpperCAmelCase			   =     scheduler.step(snake_case__       , snake_case__       , snake_case__       , snake_case__					).prev_sample

        return sample


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


        for timesteps in [100, 500, 1_000]:
            self.check_over_configs(num_train_timesteps=snake_case__					)


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


        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=snake_case__					)

        __UpperCAmelCase			   =     self.scheduler_classes[0]
        __UpperCAmelCase			   =     self.get_scheduler_config(steps_offset=1					)
        __UpperCAmelCase			   =     scheduler_class(**snake_case__					)
        scheduler.set_timesteps(5					)
        assert torch.equal(scheduler.timesteps       , torch.LongTensor([801, 601, 401, 201, 1]					)					)


    def        lowerCAmelCase_  (self					)       -> int:


        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        lowerCAmelCase_  (self					)       -> List[Any]:


        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=snake_case__					)


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


        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=snake_case__					)


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


        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=snake_case__					)


    def        lowerCAmelCase_  (self					)       -> int:


        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=snake_case__					)


    def        lowerCAmelCase_  (self					)       -> Any:


        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=snake_case__					)


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


        for t in [1, 10, 49]:
            self.check_over_forward(time_step=snake_case__					)


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


        for t, num_inference_steps in zip([1, 10, 50]       , [10, 50, 500]					):
            self.check_over_forward(time_step=snake_case__       , num_inference_steps=snake_case__					)


    def        lowerCAmelCase_  (self					)       -> List[Any]:


        for t, eta in zip([1, 10, 49]       , [0.0, 0.5, 1.0]					):
            self.check_over_forward(time_step=snake_case__       , eta=snake_case__					)


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     self.scheduler_classes[0]
        __UpperCAmelCase			   =     self.get_scheduler_config()
        __UpperCAmelCase			   =     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(420       , 400					) - 0.14771					)					) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980       , 960					) - 0.32460					)					) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0       , 0					) - 0.0					)					) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487       , 486					) - 0.00979					)					) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999       , 998					) - 0.02					)					) < 1E-5


    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     self.full_loop()

        __UpperCAmelCase			   =     torch.sum(torch.abs(snake_case__					)					)
        __UpperCAmelCase			   =     torch.mean(torch.abs(snake_case__					)					)

        assert abs(result_sum.item() - 172.0067					) < 1E-2
        assert abs(result_mean.item() - 0.223967					) < 1E-3


    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     self.full_loop(prediction_type='''v_prediction'''					)

        __UpperCAmelCase			   =     torch.sum(torch.abs(snake_case__					)					)
        __UpperCAmelCase			   =     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        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     self.full_loop(set_alpha_to_one=snake_case__       , beta_start=0.01					)
        __UpperCAmelCase			   =     torch.sum(torch.abs(snake_case__					)					)
        __UpperCAmelCase			   =     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        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     self.full_loop(set_alpha_to_one=snake_case__       , beta_start=0.01					)
        __UpperCAmelCase			   =     torch.sum(torch.abs(snake_case__					)					)
        __UpperCAmelCase			   =     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

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




