






"""simple docstring"""





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							(KDPMaAncestralDiscreteScheduler,)
   UpperCamelCase__     	=							10





   def __lowercase							(       self					, **UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     {
              "num_train_timesteps": 1_100,
              "beta_start": 0.00_01,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**UpperCAmelCase_)
          return config





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=UpperCAmelCase_)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					, [0.00_02, 0.0_02, 0.02]):
                 self.check_over_configs(beta_start=UpperCAmelCase_					, beta_end=UpperCAmelCase_)





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          if torch_device == "mps":
                 return
          lowercase__:							List[Any]         =     self.scheduler_classes[0]
          lowercase__:							Optional[Any]         =     self.get_scheduler_config()
          lowercase__:							Dict         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							Optional[Any]         =     torch.manual_seed(0)

          lowercase__:							str         =     self.dummy_model()
          lowercase__:							List[Any]         =     self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase__:							List[str]         =     sample.to(UpperCAmelCase_)

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

                 lowercase__:							str         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							str         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[int]         =     output.prev_sample

          lowercase__:							Union[str, Any]         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Union[str, Any]         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_38_49.38_77) < 1E-2
          assert abs(result_mean.item() - 18.03_31) < 5E-3





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




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





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




          '''simple docstring'''




          if torch_device == "mps":
                 return
          lowercase__:							Optional[int]         =     self.scheduler_classes[0]
          lowercase__:							Dict         =     self.get_scheduler_config(prediction_type="v_prediction")
          lowercase__:							List[Any]         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							Dict         =     self.dummy_model()
          lowercase__:							List[Any]         =     self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase__:							Optional[Any]         =     sample.to(UpperCAmelCase_)

          lowercase__:							Any         =     torch.manual_seed(0)

          for i, t in enumerate(scheduler.timesteps):
                 lowercase__:							str         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Dict         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							List[Any]         =     output.prev_sample

          lowercase__:							List[Any]         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Dict         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 3_28.99_70) < 1E-2
          assert abs(result_mean.item() - 0.42_84) < 1E-3





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




          '''simple docstring'''




          if torch_device == "mps":
                 return
          lowercase__:							int         =     self.scheduler_classes[0]
          lowercase__:							List[str]         =     self.get_scheduler_config()
          lowercase__:							List[str]         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps					, device=UpperCAmelCase_)
          lowercase__:							str         =     torch.manual_seed(0)

          lowercase__:							int         =     self.dummy_model()
          lowercase__:							Optional[Any]         =     self.dummy_sample_deter.to(UpperCAmelCase_) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                 lowercase__:							List[str]         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							str         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[int]         =     output.prev_sample

          lowercase__:							Optional[int]         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Tuple         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_38_49.38_18) < 1E-1
          assert abs(result_mean.item() - 18.03_31) < 1E-3







"""simple docstring"""






import numpy as np



def     a_		(   lowerCamelCase   ,     lowerCamelCase="utf-8"   ,     lowerCamelCase="surrogatepass"			):
				UpperCAmelCase__			 =				bin(int.from_bytes(text.encode(lowerCamelCase   ,     lowerCamelCase			)   ,     'big'			)			)[2:]
				return bits.zfill(8 * ((len(lowerCamelCase			) + 7) // 8)			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase="utf-8"   ,     lowerCamelCase="surrogatepass"			):
				UpperCAmelCase__			 =				int(lowerCamelCase   ,     2			)
				return n.to_bytes((n.bit_length() + 7) // 8   ,     'big'			).decode(lowerCamelCase   ,     lowerCamelCase			) or "\0"



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				if size_par + len(lowerCamelCase			) <= 2**size_par - (len(lowerCamelCase			) - 1):
								raise ValueError('size of parity don\'t match with size of data'			)

				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[bin(lowerCamelCase			)[2:] for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			)]

				# sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# data position template + parity
				UpperCAmelCase__			 =				[]
				# parity bit counter
				UpperCAmelCase__			 =				0
				# counter position of data bits
				UpperCAmelCase__			 =				0

				for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			):
								# Performs a template of bit positions - who should be given,
								# and who should be parity
								if qtd_bp < size_par:
												if (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
																data_out_gab.append('P'			)
																UpperCAmelCase__			 =				qtd_bp + 1
												else:
																data_out_gab.append('D'			)
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data[cont_data]			)
												cont_data += 1
								else:
												data_ord.append(lowerCamelCase			)

    # Calculates parity
				UpperCAmelCase__			 =				0  # parity bit counter
				for bp in range(1   ,     size_par + 1			):
								# Bit counter one for a given parity
								UpperCAmelCase__			 =				0
								# counter to control the loop reading
								UpperCAmelCase__			 =				0
								for x in data_ord:
												if x is not None:
																try:
																				UpperCAmelCase__			 =				(bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				UpperCAmelCase__			 =				'0'
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(cont_bo % 2			)

								qtd_bp += 1

				# Mount the message
				UpperCAmelCase__			 =				0  # parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]			)			)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]			)

				return data_out



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0
				# list of parity received
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]

				for x in range(1   ,     len(lowerCamelCase			) + 1			):
								# Performs a template of bit positions - who should be given,
								#  and who should be parity
								if qtd_bp < size_par and (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
												data_out_gab.append('P'			)
												UpperCAmelCase__			 =				qtd_bp + 1
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_output.append(data[cont_data]			)
								else:
												parity_received.append(data[cont_data]			)
								cont_data += 1

				# -----------calculates the parity with the data
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[bin(lowerCamelCase			)[2:] for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			)]

				#  sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# Data position feedback + parity
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0

				for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			):
								# Performs a template position of bits - who should be given,
								# and who should be parity
								if qtd_bp < size_par and (np.log(lowerCamelCase			) / np.log(2			)).is_integer():
												data_out_gab.append('P'			)
												UpperCAmelCase__			 =				qtd_bp + 1
								else:
												data_out_gab.append('D'			)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data_output[cont_data]			)
												cont_data += 1
								else:
												data_ord.append(lowerCamelCase			)

    # Calculates parity
				UpperCAmelCase__			 =				0  # parity bit counter
				for bp in range(1   ,     size_par + 1			):
								# Bit counter one for a certain parity
								UpperCAmelCase__			 =				0
								# Counter to control loop reading
								UpperCAmelCase__			 =				0
								for x in data_ord:
												if x is not None:
																try:
																				UpperCAmelCase__			 =				(bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				UpperCAmelCase__			 =				'0'
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(str(cont_bo % 2			)			)

								qtd_bp += 1

				# Mount the message
				UpperCAmelCase__			 =				0  # Parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]			)			)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]			)

				UpperCAmelCase__			 =				parity_received == parity
				return data_output, ack



