


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Dict        =						(DDPMScheduler,)
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Tuple:
             a_					:		str							=  {
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'variance_type': 'fixed_small',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__     )
             return config
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Tuple:
             for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             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=SCREAMING_SNAKE_CASE__     ,							beta_end=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[int]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Union[str, Any]:
             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Union[str, Any]:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__     )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__     ,							prediction_type=SCREAMING_SNAKE_CASE__     ,							sample_max_value=SCREAMING_SNAKE_CASE__     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Any:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             for t in [0, 5_0_0, 9_9_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			str:
             a_					:		Optional[Any]							=  self.scheduler_classes[0]
             a_					:		Tuple							=  self.get_scheduler_config()
             a_					:		Any							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.00979     )     ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.02     )     ) < 1E-5
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Optional[int]:
             a_					:		Optional[int]							=  self.scheduler_classes[0]
             a_					:		str							=  self.get_scheduler_config()
             a_					:		Tuple							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		List[str]							=  len(SCREAMING_SNAKE_CASE__     )

             a_					:		List[str]							=  self.dummy_model()
             a_					:		List[str]							=  self.dummy_sample_deter
             a_					:		str							=  torch.manual_seed(0     )

             for t in reversed(range(SCREAMING_SNAKE_CASE__     )     ):
                    # 1. predict noise residual
                    a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

                    # 2. predict previous mean of sample x_t-1
                    a_					:		int							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                    # if t > 0:
                    #     noise = self.dummy_sample_deter
                    #     variance = scheduler.get_variance(t) ** (0.5) * noise
                    #
                    # sample = pred_prev_sample + variance
                    a_					:		List[Any]							=  pred_prev_sample

             a_					:		List[Any]							=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Optional[Any]							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_sum.item() - 258.9606     ) < 1E-2
             assert abs(result_mean.item() - 0.3372     ) < 1E-3
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Optional[int]:
             a_					:		List[str]							=  self.scheduler_classes[0]
             a_					:		Dict							=  self.get_scheduler_config(prediction_type='v_prediction'     )
             a_					:		Union[str, Any]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  len(SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  self.dummy_model()
             a_					:		Any							=  self.dummy_sample_deter
             a_					:		Any							=  torch.manual_seed(0     )

             for t in reversed(range(SCREAMING_SNAKE_CASE__     )     ):
                    # 1. predict noise residual
                    a_					:		Optional[Any]							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

                    # 2. predict previous mean of sample x_t-1
                    a_					:		Union[str, Any]							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                    # if t > 0:
                    #     noise = self.dummy_sample_deter
                    #     variance = scheduler.get_variance(t) ** (0.5) * noise
                    #
                    # sample = pred_prev_sample + variance
                    a_					:		Optional[Any]							=  pred_prev_sample

             a_					:		List[str]							=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Dict							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_sum.item() - 202.0296     ) < 1E-2
             assert abs(result_mean.item() - 0.2631     ) < 1E-3
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Tuple:
             a_					:		Union[str, Any]							=  self.scheduler_classes[0]
             a_					:		Optional[Any]							=  self.get_scheduler_config()
             a_					:		List[str]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  [1_0_0, 8_7, 5_0, 1, 0]

             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  scheduler.timesteps

             for i, timestep in enumerate(SCREAMING_SNAKE_CASE__     ):
                    if i == len(SCREAMING_SNAKE_CASE__     ) - 1:
                           a_					:		Union[str, Any]							=  -1
                    else:
                           a_					:		Union[str, Any]							=  timesteps[i + 1]

                    a_					:		str							=  scheduler.previous_timestep(SCREAMING_SNAKE_CASE__     )
                    a_					:		Any							=  prev_t.item()

                    self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[str]:
             a_					:		List[Any]							=  self.scheduler_classes[0]
             a_					:		Optional[int]							=  self.get_scheduler_config()
             a_					:		Any							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  [1_0_0, 8_7, 5_0, 5_1, 0]

             with self.assertRaises(SCREAMING_SNAKE_CASE__     ,							msg='`custom_timesteps` must be in descending order.'     ):
                    scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			int:
             a_					:		List[Any]							=  self.scheduler_classes[0]
             a_					:		int							=  self.get_scheduler_config()
             a_					:		int							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  [1_0_0, 8_7, 5_0, 1, 0]
             a_					:		Optional[Any]							=  len(SCREAMING_SNAKE_CASE__     )

             with self.assertRaises(SCREAMING_SNAKE_CASE__     ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'     ):
                    scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE__     ,							timesteps=SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             a_					:		List[str]							=  self.scheduler_classes[0]
             a_					:		List[Any]							=  self.get_scheduler_config()
             a_					:		List[str]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  [scheduler.config.num_train_timesteps]

             with self.assertRaises(
                 SCREAMING_SNAKE_CASE__     ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'     ,							):
                    scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )






from __future__ import annotations

UpperCAmelCase_      : Tuple  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int  ,    __A :			int				)	->		bool:

       """simple docstring"""

       for i in range(len(__A				)				):
              if board[row][i] == 1:
                     return False
       for i in range(len(__A				)				):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    -1  ,    -1				)				):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    len(__A				)				)				):
              if board[i][j] == 1:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int				)	->		bool:

       """simple docstring"""

       if row >= len(__A				):
              solution.append(__A				)
              printboard(__A				)
              print()
              return True
       for i in range(len(__A				)				):
              if is_safe(__A  ,    __A  ,    __A				):
                     a_					:		Any							=  1
                     solve(__A  ,    row + 1				)
                     a_					:		Tuple							=  0
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]				)	->		None:

       """simple docstring"""

       for i in range(len(__A				)				):
              for j in range(len(__A				)				):
                     if board[i][j] == 1:
                            print('Q'  ,    end=' '				)
                     else:
                            print('.'  ,    end=' '				)
              print()


# n=int(input("The no. of queens"))
UpperCAmelCase_      : List[str]  					=       8
UpperCAmelCase_      : str  					=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


