






"""simple docstring"""
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     UpperCamelCase_ (snake_case_  ):
	__magic_name__			   =       (EulerDiscreteScheduler,)
	__magic_name__			   =       10





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			**lowerCAmelCase_      :				Optional[int]		)		->    Dict:
				UpperCAmelCase_  :				int      =      {
				    "num_train_timesteps": 1_100,
				    "beta_start": 0.0_0_0_1,
				    "beta_end": 0.0_2,
				    "beta_schedule": "linear",
				}

				config.update(**lowerCAmelCase_		)
				return config





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Tuple:
				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=lowerCAmelCase_			,			beta_end=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Tuple:
				for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    str:
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config()
				UpperCAmelCase_  :				List[str]      =      scheduler_class(**lowerCAmelCase_		)

				scheduler.set_timesteps(self.num_inference_steps		)

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

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

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

							UpperCAmelCase_  :				Optional[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Optional[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			generator=lowerCAmelCase_		)
							UpperCAmelCase_  :				Union[str, Any]      =      output.prev_sample

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

				assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1e-2
				assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    str:
				UpperCAmelCase_  :				str      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				List[Any]      =      self.get_scheduler_config(prediction_type="v_prediction"		)
				UpperCAmelCase_  :				Optional[int]      =      scheduler_class(**lowerCAmelCase_		)

				scheduler.set_timesteps(self.num_inference_steps		)

				UpperCAmelCase_  :				Tuple      =      torch.manual_seed(0		)

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

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

							UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)

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

				UpperCAmelCase_  :				Tuple      =      torch.sum(torch.abs(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				List[Any]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_sum.item() - 0.0_0_0_2		) < 1e-2
				assert abs(result_mean.item() - 2.2_676e-06		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Tuple      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				List[str]      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Optional[int]      =      scheduler_class(**lowerCAmelCase_		)

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

				UpperCAmelCase_  :				List[str]      =      torch.manual_seed(0		)

				UpperCAmelCase_  :				Tuple      =      self.dummy_model()
				UpperCAmelCase_  :				str      =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				UpperCAmelCase_  :				Dict      =      sample.to(lowerCAmelCase_		)

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

							UpperCAmelCase_  :				Optional[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Any      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			generator=lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      output.prev_sample

				UpperCAmelCase_  :				Optional[int]      =      torch.sum(torch.abs(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				Optional[Any]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1e-2
				assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				Dict      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Union[str, Any]      =      scheduler_class(**lowerCAmelCase_			,			use_karras_sigmas=lowerCAmelCase_		)

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

				UpperCAmelCase_  :				Any      =      torch.manual_seed(0		)

				UpperCAmelCase_  :				str      =      self.dummy_model()
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				UpperCAmelCase_  :				Dict      =      sample.to(lowerCAmelCase_		)

				for t in scheduler.timesteps:
							UpperCAmelCase_  :				Any      =      scheduler.scale_model_input(lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				int      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)

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

				UpperCAmelCase_  :				List[Any]      =      torch.sum(torch.abs(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				str      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9		) < 1e-2
				assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3		) < 1e-3











"""simple docstring"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



