import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 						__snake_case						(			__UpperCamelCase		):





      '''simple docstring'''






      _snake_case     		=							(EulerAncestralDiscreteScheduler,)
      _snake_case     		=							10



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	**_UpperCamelCase  :					List[str])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0_0_0_1,
              '''beta_end''': 0.0_2,
              '''beta_schedule''': '''linear''',
          }

          config.update(**a_)
          return config



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


          """simple docstring"""


          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=a_)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->List[str]:


          """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=a_     ,	beta_end=a_)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Dict:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->List[Any]:


          """simple docstring"""


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



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


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		Optional[Any]			    =    self.get_scheduler_config()
          _lowerCamelCase  :		Dict			    =    scheduler_class(**a_)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase  :		Optional[int]			    =    torch.manual_seed(0)

          _lowerCamelCase  :		Optional[int]			    =    self.dummy_model()
          _lowerCamelCase  :		Tuple			    =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase  :		Optional[Any]			    =    sample.to(a_)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		int			    =    scheduler.scale_model_input(a_     ,	a_)

              _lowerCamelCase  :		List[str]			    =    model(a_     ,	a_)

              _lowerCamelCase  :		Any			    =    scheduler.step(a_     ,	a_     ,	a_     ,	generator=a_)
              _lowerCamelCase  :		List[Any]			    =    output.prev_sample

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

          assert abs(result_sum.item() - 152.3192) < 1E-2
          assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		Any			    =    self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase  :		Dict			    =    scheduler_class(**a_)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase  :		Any			    =    torch.manual_seed(0)

          _lowerCamelCase  :		int			    =    self.dummy_model()
          _lowerCamelCase  :		List[Any]			    =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase  :		str			    =    sample.to(a_)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		int			    =    scheduler.scale_model_input(a_     ,	a_)

              _lowerCamelCase  :		Dict			    =    model(a_     ,	a_)

              _lowerCamelCase  :		List[str]			    =    scheduler.step(a_     ,	a_     ,	a_     ,	generator=a_)
              _lowerCamelCase  :		Tuple			    =    output.prev_sample

          _lowerCamelCase  :		List[Any]			    =    torch.sum(torch.abs(a_))
          _lowerCamelCase  :		List[str]			    =    torch.mean(torch.abs(a_))

          assert abs(result_sum.item() - 108.4439) < 1E-2
          assert abs(result_mean.item() - 0.1_4_1_2) < 1E-3




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


          """simple docstring"""


          _lowerCamelCase  :		str			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		Tuple			    =    self.get_scheduler_config()
          _lowerCamelCase  :		Union[str, Any]			    =    scheduler_class(**a_)

          scheduler.set_timesteps(self.num_inference_steps     ,	device=a_)
          _lowerCamelCase  :		Optional[int]			    =    torch.manual_seed(0)

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

          for t in scheduler.timesteps:
              _lowerCamelCase  :		Optional[Any]			    =    scheduler.scale_model_input(a_     ,	a_)

              _lowerCamelCase  :		int			    =    model(a_     ,	a_)

              _lowerCamelCase  :		Dict			    =    scheduler.step(a_     ,	a_     ,	a_     ,	generator=a_)
              _lowerCamelCase  :		int			    =    output.prev_sample

          _lowerCamelCase  :		int			    =    torch.sum(torch.abs(a_))
          _lowerCamelCase  :		Dict			    =    torch.mean(torch.abs(a_))

          assert abs(result_sum.item() - 152.3192) < 1E-2
          assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3









import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





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

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      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(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''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       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				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		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      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       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

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

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''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       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





