




"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   (KDPMaDiscreteScheduler,)
				__lowerCAmelCase    :    Optional[int]         =   10







				def 					a_  (							self   :					str      ,     **_lowerCamelCase   :					List[str]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		{
					    '''num_train_timesteps''': 11_00,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**_lowerCamelCase       )
					return config







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,     [0.0002, 0.002, 0.02]       ):
						self.check_over_configs(beta_start=_lowerCamelCase      ,     beta_end=_lowerCamelCase       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.scheduler_classes[0]
					A_  :				str	   =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
					A_  :				Any	   =		scheduler_class(**_lowerCamelCase       )

					scheduler.set_timesteps(self.num_inference_steps       )

					A_  :				str	   =		self.dummy_model()
					A_  :				Tuple	   =		self.dummy_sample_deter * scheduler.init_noise_sigma
					A_  :				Optional[Any]	   =		sample.to(_lowerCamelCase       )

					for i, t in enumerate(scheduler.timesteps       ):
						A_  :				Optional[int]	   =		scheduler.scale_model_input(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				List[Any]	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				Any	   =		scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				Optional[Any]	   =		output.prev_sample

					A_  :				int	   =		torch.sum(torch.abs(_lowerCamelCase       )       )
					A_  :				List[Any]	   =		torch.mean(torch.abs(_lowerCamelCase       )       )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
						assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
					else:
						# CUDA
						assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
						assert abs(result_mean.item() - 0.0002       ) < 1E-3







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					if torch_device == "mps":
						return
					A_  :				Optional[int]	   =		self.scheduler_classes[0]
					A_  :				Optional[Any]	   =		self.get_scheduler_config()
					A_  :				int	   =		scheduler_class(**_lowerCamelCase       )

					scheduler.set_timesteps(self.num_inference_steps       )

					A_  :				Tuple	   =		self.dummy_model()
					A_  :				Dict	   =		self.dummy_sample_deter * scheduler.init_noise_sigma
					A_  :				Tuple	   =		sample.to(_lowerCamelCase       )

					for i, t in enumerate(scheduler.timesteps       ):
						A_  :				List[Any]	   =		scheduler.scale_model_input(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				List[Any]	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				Optional[int]	   =		scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				Optional[int]	   =		output.prev_sample

					A_  :				Union[str, Any]	   =		torch.sum(torch.abs(_lowerCamelCase       )       )
					A_  :				Any	   =		torch.mean(torch.abs(_lowerCamelCase       )       )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 20.4125       ) < 1E-2
						assert abs(result_mean.item() - 0.0266       ) < 1E-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125       ) < 1E-2
						assert abs(result_mean.item() - 0.0266       ) < 1E-3







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					if torch_device == "mps":
						return
					A_  :				Optional[int]	   =		self.scheduler_classes[0]
					A_  :				int	   =		self.get_scheduler_config()
					A_  :				Optional[int]	   =		scheduler_class(**_lowerCamelCase       )

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

					A_  :				Union[str, Any]	   =		self.dummy_model()
					A_  :				Tuple	   =		self.dummy_sample_deter.to(_lowerCamelCase       ) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
						A_  :				Tuple	   =		scheduler.scale_model_input(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				int	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )

						A_  :				Dict	   =		scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				Optional[int]	   =		output.prev_sample

					A_  :				Union[str, Any]	   =		torch.sum(torch.abs(_lowerCamelCase       )       )
					A_  :				Dict	   =		torch.mean(torch.abs(_lowerCamelCase       )       )

					if str(_lowerCamelCase       ).startswith('''cpu'''       ):
						# The following sum varies between 148 and 156 on mps. Why?
						assert abs(result_sum.item() - 20.4125       ) < 1E-2
						assert abs(result_mean.item() - 0.0266       ) < 1E-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125       ) < 1E-2
						assert abs(result_mean.item() - 0.0266       ) < 1E-3











"""simple docstring"""

# Function to print upper half of diamond (pyramid)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(0  ,      _UpperCAmelCase	):
		for _ in range(0  ,      n - i - 1	):  # printing spaces
			print(''' '''  ,      end=''''''	)
		for _ in range(0  ,      i + 1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(_UpperCAmelCase  ,      0  ,      -1	):
		for _ in range(_UpperCAmelCase  ,      0  ,      -1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()
		for _ in range(n - i + 1  ,      0  ,      -1	):  # printing spaces
			print(''' '''  ,      end=''''''	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if n <= 0:
		print('''       ...       ....        nothing printing :('''	)
		return
	floyd(_UpperCAmelCase	)  # upper half
	reverse_floyd(_UpperCAmelCase	)  # lower half


if __name__ == "__main__":
		print(r'| /\ | |- |  |-  |--| |\  /| |-')
		print(r'|/  \| |- |_ |_  |__| | \/ | |_')
		_lowerCamelCase  :			Tuple        =       1
		while K:
				_lowerCamelCase  :			Optional[int]        =       int(input('enter the number and , and see the magic : '))
				print()
				pretty_print(user_number)
				_lowerCamelCase  :			Optional[Any]        =       int(input('press 0 to exit... and 1 to continue...'))

		print('Good Bye...')





