





import copy
import re


class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				"hp"
	_A													=				{}
	_A													=				None

	@classmethod
	def    snake_case__(						cls:						Dict,					lowerCamelCase_:						Any,					lowerCamelCase_:						List[Any]			):
			lowercase__					:      Any      =		prefix
			lowercase__					:      Union[str, Any]      =		defaults
			cls.build_naming_info()

	@staticmethod
	def    snake_case__(						lowerCamelCase_:						List[Any],					lowerCamelCase_:						Dict			):
			if len(lowerCamelCase_			) == 0:
					return ""
			lowercase__					:      str      =		None
			if any(char.isdigit() for char in word			):
					raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number"""			)
			if word in info["short_word"]:
					return info["short_word"][word]
			for prefix_len in range(1,					len(lowerCamelCase_			) + 1			):
					lowercase__					:      List[Any]      =		word[:prefix_len]
					if prefix in info["reverse_short_word"]:
							continue
					else:
							lowercase__					:      str      =		prefix
							break

			if short_word is None:
					# Paranoid fallback
					def int_to_alphabetic(lowerCamelCase_:						Dict			):
							lowercase__					:      str      =		''
							while integer != 0:
									lowercase__					:      List[str]      =		chr(ord('A'			) + integer % 10			) + s
									integer //= 10
							return s

					lowercase__					:      Any      =		0
					while True:
							lowercase__					:      Optional[Any]      =		word + '#' + int_to_alphabetic(lowerCamelCase_			)
							if sword in info["reverse_short_word"]:
									continue
							else:
									lowercase__					:      int      =		sword
									break

			lowercase__					:      List[str]      =		short_word
			lowercase__					:      Tuple      =		word
			return short_word

	@staticmethod
	def    snake_case__(						lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						int			):
			lowercase__					:      Optional[int]      =		param_name.split('_'			)

			lowercase__					:      Union[str, Any]      =		[TrialShortNamer.shortname_for_word(lowerCamelCase_,					lowerCamelCase_			) for word in words]

			# We try to create a separatorless short name, but if there is a collision we have to fallback
			# to a separated short name
			lowercase__					:      Union[str, Any]      =		['', '_']

			for separator in separators:
					lowercase__					:      Optional[int]      =		separator.join(lowerCamelCase_			)
					if shortname not in info["reverse_short_param"]:
							lowercase__					:      str      =		shortname
							lowercase__					:      int      =		param_name
							return shortname

			return param_name

	@staticmethod
	def    snake_case__(						lowerCamelCase_:						Dict,					lowerCamelCase_:						Any			):
			lowercase__					:      Optional[Any]      =		TrialShortNamer.shortname_for_key(lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		short_name
			lowercase__					:      Dict      =		param_name

	@classmethod
	def    snake_case__(						cls:						Union[str, Any]			):
			if cls.NAMING_INFO is not None:
					return

			lowercase__					:      Tuple      =		{
			    'short_word': {},
			    'reverse_short_word': {},
			    'short_param': {},
			    'reverse_short_param': {},
			}

			lowercase__					:      int      =		list(cls.DEFAULTS.keys()			)

			for k in field_keys:
					cls.add_new_param_name(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      List[Any]      =		info

	@classmethod
	def    snake_case__(						cls:						str,					lowerCamelCase_:						Dict			):
			cls.build_naming_info()
			assert cls.PREFIX is not None
			lowercase__					:      Optional[int]      =		[copy.copy(cls.PREFIX			)]

			for k, v in params.items():
					if k not in cls.DEFAULTS:
							raise Exception(F"""You should provide a default value for the param name {k} with value {v}"""			)
					if v == cls.DEFAULTS[k]:
							# The default value is not added to the name
							continue

					lowercase__					:      List[Any]      =		cls.NAMING_INFO['short_param'][k]

					if isinstance(lowerCamelCase_,					lowerCamelCase_			):
							lowercase__					:      List[str]      =		1 if v else 0

					lowercase__					:      Optional[int]      =		'' if isinstance(lowerCamelCase_,					(int, float)			) else '-'
					lowercase__					:      List[Any]      =		F"""{key}{sep}{v}"""
					name.append(lowerCamelCase_			)

			return "_".join(lowerCamelCase_			)





	@classmethod
	def    snake_case__(						cls:						Tuple,					lowerCamelCase_:						Dict			):
			lowercase__					:      Any      =		repr[len(cls.PREFIX			) + 1 :]
			if repr == "":
					lowercase__					:      Any      =		[]
			else:
					lowercase__					:      Optional[int]      =		repr.split('_'			)

			lowercase__					:      Any      =		{}

			for value in values:
					if "-" in value:
							lowercase__   ,				lowercase__					:      int      =		value.split('-'			)
					else:
							lowercase__					:      List[str]      =		re.sub('[0-9.]',					'',					lowerCamelCase_			)
							lowercase__					:      List[str]      =		float(re.sub('[^0-9.]',					'',					lowerCamelCase_			)			)

					lowercase__					:      Optional[int]      =		cls.NAMING_INFO['reverse_short_param'][p_k]

					lowercase__					:      Union[str, Any]      =		p_v

			for k in cls.DEFAULTS:
					if k not in parameters:
							lowercase__					:      Any      =		cls.DEFAULTS[k]

			return parameters









import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			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    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

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

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      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    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

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

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

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

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

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

			lowercase__					:      Dict      =		torch.manual_seed(0			)

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

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, 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    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

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

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		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


