





'''simple docstring'''







def 		SCREAMING_SNAKE_CASE   (       )      ->    Dict:

     '''simple docstring'''




     return [list(range(10_00 - i							,	-10_00 - i							,	-1)) for i in range(10_00)]


A         =				generate_large_matrix()
A         =				(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Optional[Any])      ->    Any:

     '''simple docstring'''




     assert all(row == sorted(_UpperCamelCase							,	reverse=_UpperCamelCase) for row in grid)
     assert all(list(_UpperCamelCase) == sorted(_UpperCamelCase							,	reverse=_UpperCamelCase) for col in zip(*_UpperCamelCase))

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Union[str, Any])      ->    List[str]:

     '''simple docstring'''




     _lowercase				:   Dict	    =				0
     _lowercase				:   str	    =				len(_UpperCamelCase) - 1

     # Edge cases such as no values or all numbers are negative.
     if not array or array[0] < 0:
          return 0

     while right + 1 > left:
          _lowercase				:   List[Any]	    =				(left + right) // 2
          _lowercase				:   Dict	    =				array[mid]

          # Num must be negative and the index must be greater than or equal to 0.
          if num < 0 and array[mid - 1] >= 0:
               return mid

          if num >= 0:
               _lowercase				:   int	    =				mid + 1
          else:
               _lowercase				:   List[Any]	    =				mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
     return len(_UpperCamelCase)

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Optional[Any])      ->    Optional[Any]:

     '''simple docstring'''




     _lowercase				:   Tuple	    =				0
     _lowercase				:   List[str]	    =				len(grid[0])

     for i in range(len(_UpperCamelCase)):
          _lowercase				:   int	    =				find_negative_index(grid[i][:bound])
          total += bound
     return (len(_UpperCamelCase) * len(grid[0])) - total

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Tuple)      ->    int:

     '''simple docstring'''




     return len([number for row in grid for number in row if number < 0])

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Any)      ->    Optional[Any]:

     '''simple docstring'''




     _lowercase				:   List[str]	    =				0
     for row in grid:
          for i, number in enumerate(_UpperCamelCase):
               if number < 0:
                    total += len(_UpperCamelCase) - i
                    break
     return total

def 		SCREAMING_SNAKE_CASE   (       )      ->    Tuple:

     '''simple docstring'''




     from timeit import timeit

     print('Running benchmarks')
     _lowercase				:   Dict	    =				(
         'from __main__ import count_negatives_binary_search, '
         'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
     )
     for func in (
         "count_negatives_binary_search",  # took 0.7727 seconds
         "count_negatives_brute_force_with_break",  # took 4.6505 seconds
         "count_negatives_brute_force",  # took 12.8160 seconds
     ):
          _lowercase				:   Optional[Any]	    =				timeit(F'''{func}(grid=grid)'''							,	setup=_UpperCamelCase							,	number=5_00)
          print(F'''{func}() took {time:0.4f} seconds''')


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()



"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =(KDPMaDiscreteScheduler,)
							__UpperCAmelCase		:       Optional[Any]							 =1_0

							def      snake_case							(	self       ,  **__a      ):
										__lowerCAmelCase							       =     {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__a      )
										return config

							def      snake_case							(	self      ):
										for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a      )

							def      snake_case							(	self      ):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
													self.check_over_configs(beta_start=__a       ,  beta_end=__a      )

							def      snake_case							(	self      ):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__a      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config(prediction_type="v_prediction"      )
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 6.1_1_1_2e-1_0      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3

							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3



							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps       ,  device=__a      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter.to(__a      ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if str(__a      ).startswith("cpu"      ):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




