


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DPMSolverSDEScheduler,)
					__UpperCAmelCase            =				10






					def A      (				self ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_1_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''noise_sampler_seed''': 0,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






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







								'''simple docstring'''

								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=snake_case_ ,       beta_end=snake_case_						)






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







								'''simple docstring'''

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

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






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								scheduler.set_timesteps(self.num_inference_steps						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6						) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(prediction_type='''v_prediction'''						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								scheduler.set_timesteps(self.num_inference_steps						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3						) < 1e-2
											assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3						) < 1e-2
											assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5						) < 1e-2
											assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1						) < 1e-3






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

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

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_ ,       use_karras_sigmas=snake_case_						)

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

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for t in scheduler.timesteps:
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
								else:
											assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   {
			    '''^''': 3,
			    '''*''': 2,
			    '''/''': 2,
			    '''%''': 2,
			    '''+''': 1,
			    '''-''': 1,
			}  # Priority of each operator
			__lowercase 					=   len(_UpperCamelCase  ) if (len(_UpperCamelCase  ) > 7) else 7

			# Print table header for output
			print(
			    '''Symbol'''.center(8  )       ,			'''Stack'''.center(_UpperCamelCase  )       ,			'''Postfix'''.center(_UpperCamelCase  )       ,			sep=''' | '''       ,			)
			print('''-''' * (print_width * 3 + 7)  )

			for x in infix:
						if x.isalpha() or x.isdigit():
									post_fix.append(_UpperCamelCase  )  # if x is Alphabet / Digit, add it to Postfix
						elif x == "(":
									stack.append(_UpperCamelCase  )  # if x is "(" push to Stack
						elif x == ")":  # if x is ")" pop stack until "(" is encountered
									while stack[-1] != "(":
												post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
									stack.pop()
						else:
									if len(_UpperCamelCase  ) == 0:
												stack.append(_UpperCamelCase  )  # If stack is empty, push x to stack
									else:  # while priority of x is not > priority of element in the stack
												while len(_UpperCamelCase  ) > 0 and priority[x] <= priority[stack[-1]]:
															post_fix.append(stack.pop()  )  # pop stack & add to Postfix
												stack.append(_UpperCamelCase  )  # push x to stack

						print(
						    x.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			while len(_UpperCamelCase  ) > 0:  # while stack is not empty
						post_fix.append(stack.pop()  )  # pop stack & add to Postfix
						print(
						    ''' '''.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			return "".join(_UpperCamelCase  )  # return Postfix as str


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   list(infix[::-1]  )  # reverse the infix equation

			for i in range(len(_UpperCamelCase  )  ):
						if infix[i] == "(":
									__lowercase 					=   ''')'''  # change "(" to ")"
						elif infix[i] == ")":
									__lowercase 					=   '''('''  # change ")" to "("

			return (infix_2_postfix(''''''.join(_UpperCamelCase  )  ))[
			    ::-1
			]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	a : Union[str, Any]		=				input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
	a : Optional[Any]		=				''''''.join(Infix.split())  # Remove spaces from the input
	print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')


