






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Dict							=							(DDPMParallelScheduler,)

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       **SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  str									=     {
					    'num_train_timesteps': 1_0_0_0,
					    'beta_start': 0.0_001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					    'variance_type': 'fixed_small',
					    'clip_sample': True,
					}

					config.update(**SCREAMING_SNAKE_CASE__    )
					return config

		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					for beta_start, beta_end in zip([0.0_001, 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    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					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    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					for t in [0, 5_0_0, 9_9_9]:
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.scheduler_classes[0]
					__a						:  Dict									=     self.get_scheduler_config()
					__a						:  Optional[int]									=     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    ) - 0.00_979    )    ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ) - 0.02    )    ) < 1e-5

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					__a						:  int									=     self.scheduler_classes[0]
					__a						:  Dict									=     self.get_scheduler_config()
					__a						:  Union[str, Any]									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  Optional[int]									=     len(SCREAMING_SNAKE_CASE__    )

					__a						:  Dict									=     self.dummy_model()
					__a						:  Optional[int]									=     self.dummy_sample_deter
					__a						:  Any									=     self.dummy_sample_deter + 0.1
					__a						:  Any									=     self.dummy_sample_deter - 0.1

					__a						:  Optional[int]									=     samplea.shape[0]
					__a						:  int									=     torch.stack([samplea, samplea, samplea]    ,       dim=0    )
					__a						:  Dict									=     torch.arange(SCREAMING_SNAKE_CASE__    )[0:3, None].repeat(1    ,       SCREAMING_SNAKE_CASE__    )

					__a						:  List[Any]									=     model(samples.flatten(0    ,       1    )    ,       timesteps.flatten(0    ,       1    )    )
					__a						:  Optional[int]									=     scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__    ,       timesteps.flatten(0    ,       1    )    ,       samples.flatten(0    ,       1    )    )

					__a						:  Tuple									=     torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
					__a						:  Dict									=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

					assert abs(result_sum.item() - 1_153.1_833    ) < 1e-2
					assert abs(result_mean.item() - 0.5_005    ) < 1e-3

		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.scheduler_classes[0]
					__a						:  int									=     self.get_scheduler_config()
					__a						:  Dict									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  List[str]									=     len(SCREAMING_SNAKE_CASE__    )

					__a						:  Dict									=     self.dummy_model()
					__a						:  Any									=     self.dummy_sample_deter
					__a						:  List[str]									=     torch.manual_seed(0    )

					for t in reversed(range(SCREAMING_SNAKE_CASE__    )    ):
								# 1. predict noise residual
								__a						:  str									=     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

								__a						:  List[Any]									=     pred_prev_sample

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

					assert abs(result_sum.item() - 258.9_606    ) < 1e-2
					assert abs(result_mean.item() - 0.3_372    ) < 1e-3

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  Tuple									=     self.scheduler_classes[0]
					__a						:  int									=     self.get_scheduler_config(prediction_type='v_prediction'    )
					__a						:  str									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  str									=     len(SCREAMING_SNAKE_CASE__    )

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

					for t in reversed(range(SCREAMING_SNAKE_CASE__    )    ):
								# 1. predict noise residual
								__a						:  Dict									=     model(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

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

								__a						:  List[Any]									=     pred_prev_sample

					__a						:  Optional[int]									=     torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
					__a						:  List[str]									=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

					assert abs(result_sum.item() - 202.0_296    ) < 1e-2
					assert abs(result_mean.item() - 0.2_631    ) < 1e-3

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  List[Any]									=     self.scheduler_classes[0]
					__a						:  Tuple									=     self.get_scheduler_config()
					__a						:  List[Any]									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  List[str]									=     [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						:  List[str]									=     -1
								else:
											__a						:  Dict									=     timesteps[i + 1]

								__a						:  Any									=     scheduler.previous_timestep(SCREAMING_SNAKE_CASE__    )
								__a						:  Optional[Any]									=     prev_t.item()

								self.assertEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  List[str]									=     self.scheduler_classes[0]
					__a						:  Union[str, Any]									=     self.get_scheduler_config()
					__a						:  Union[str, Any]									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  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    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  Tuple									=     self.scheduler_classes[0]
					__a						:  Dict									=     self.get_scheduler_config()
					__a						:  List[Any]									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  str									=     [1_0_0, 8_7, 5_0, 1, 0]
					__a						:  int									=     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    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					__a						:  str									=     self.scheduler_classes[0]
					__a						:  Optional[int]									=     self.get_scheduler_config()
					__a						:  Any									=     scheduler_class(**SCREAMING_SNAKE_CASE__    )

					__a						:  Tuple									=     [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__    )











import operator as op

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Any:
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_      		=     lambda __UpperCAmelCase     ,     __UpperCAmelCase				: int(x / y				)  # noqa: E731 integer division operation
      SCREAMING_SNAKE_CASE_      		=     {
          '^': op.pow,
          '*': op.mul,
          '/': div,
          '+': op.add,
          '-': op.sub,
      }  # operators & their respective operation

      # print table header
      print('Symbol'.center(8				)     ,     'Action'.center(12				)     ,     'Stack'     ,     sep=' | '				)
      print('-' * (30 + len(__UpperCAmelCase				))				)

      for x in post_fix:
            if x.isdigit():  # if x in digit
                  stack.append(__UpperCAmelCase				)  # append x to stack
                  # output in tabular format
                  print(x.rjust(8				)     ,     ('push(' + x + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)
            else:
                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + a + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  stack.append(
                      str(opr[x](int(__UpperCAmelCase				)     ,     int(__UpperCAmelCase				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
                  # output in tabular format
                  print(
                      x.rjust(8				)     ,     ('push(' + a + x + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '     ,     )

      return int(stack[0]				)


if __name__ == "__main__":
   lowerCamelCase__					:  Tuple      							=				input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
   print('\n\tResult = ', solve(Postfix))