




def       __magic_name__    (			A		: int,  A		: str       ):






			'''simple docstring'''







			a   =      len(A       )
			# Create a stack and push index of fist element to it
			a   =      []
			st.append(0       )

			# Span value of first element is always 1
			a   =      1

			# Calculate span values for rest of the elements
			for i in range(1,  A       ):
						# Pop elements from stack while stack is not
						# empty and top of stack is smaller than price[i]
						while len(A       ) > 0 and price[st[0]] <= price[i]:
									st.pop()

						# If stack becomes empty, then price[i] is greater
						# than all elements on left of it, i.e. price[0],
						# price[1], ..price[i-1]. Else the price[i]  is
						# greater than elements after top of stack
						a   =      i + 1 if len(A       ) <= 0 else (i - st[0])

						# Push this element to stack
						st.append(A       )




def       __magic_name__    (			A		: Union[str, Any],  A		: Tuple       ):






			'''simple docstring'''







			for i in range(0,  A       ):
						print(arr[i],  end=" "       )


# Driver program to test above function
__lowerCAmelCase		:    Tuple	      =		[10, 4, 5, 90, 120, 80]
__lowerCAmelCase		:    int	      =		[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)






print_array(S, len(price))












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= (VQDiffusionScheduler,)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      {
							    "num_vec_classes": 40_97,
							    "num_train_timesteps": 1_00,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Union[str, Any]:
							a   =      4
							a   =      8
							a   =      8

							a   =      torch.randint(0 ,				__lowerCamelCase ,				(batch_size, height * width)							)

							return sample





				@property
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							assert False





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					int:
							def model(__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				*__lowerCamelCase    :					Optional[Any]							):
										a   ,		a   =      sample.shape
										a   =      torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
										a   =      F.log_softmax(logits.double() ,				dim=1							).float()
										return return_value

							return model





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							for timesteps in [2, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							for num_vec_classes in [5, 1_00, 10_00, 40_00]:
										self.check_over_configs(num_vec_classes=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
							pass






