





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict			)   ->							int:




		'''simple docstring'''
		lowercase__					:      int      =		test_file.split(os.path.sep			)
		if components[0:2] != ["tests", "models"]:
				raise ValueError(
				    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
				    f"""{test_file} instead."""			)
		lowercase__					:      Union[str, Any]      =		components[-1]
		if not test_fn.endswith('py'			):
				raise ValueError(f"""`test_file` should be a python file. Got {test_fn} instead."""			)
		if not test_fn.startswith('test_modeling_'			):
				raise ValueError(
				    f"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""			)

		lowercase__					:      Optional[Any]      =		components[:-1] + [test_fn.replace('.py'   ,    ''			)]
		lowercase__					:      Any      =		'.'.join(_lowercase			)

		return test_module_path


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		get_module_path(_lowercase			)
		lowercase__					:      Optional[Any]      =		importlib.import_module(_lowercase			)

		return test_module


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int			)   ->							int:




		'''simple docstring'''
		lowercase__					:      Any      =		[]
		lowercase__					:      Optional[Any]      =		get_test_module(_lowercase			)
		for attr in dir(_lowercase			):
				if attr.endswith('ModelTester'			):
						tester_classes.append(getattr(_lowercase   ,    _lowercase			)			)

    # sort with class names
		return sorted(_lowercase   ,    key=lambda _lowercase			: x.__name__			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							int:




		'''simple docstring'''
		lowercase__					:      Tuple      =		[]
		lowercase__					:      Union[str, Any]      =		get_test_module(_lowercase			)
		for attr in dir(_lowercase			):
				lowercase__					:      Tuple      =		getattr(_lowercase   ,    _lowercase			)
				# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
				# `all_model_classes` is not empty (which also excludes other special classes).
				lowercase__					:      Union[str, Any]      =		getattr(_lowercase   ,    'all_model_classes'   ,    []			)
				if len(_lowercase			) > 0:
						test_classes.append(_lowercase			)

    # sort with class names
		return sorted(_lowercase   ,    key=lambda _lowercase			: x.__name__			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      Tuple      =		get_test_classes(_lowercase			)
		lowercase__					:      Union[str, Any]      =		set()
		for test_class in test_classes:
				model_classes.update(test_class.all_model_classes			)

		# sort with class names
		return sorted(_lowercase   ,    key=lambda _lowercase			: x.__name__			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]			)   ->							Any:




		'''simple docstring'''
		lowercase__					:      str      =		test_class()
		if hasattr(_lowercase   ,    'setUp'			):
				test.setUp()

		lowercase__					:      Union[str, Any]      =		None
		if hasattr(_lowercase   ,    'model_tester'			):
				# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
				if test.model_tester is not None:
						lowercase__					:      int      =		test.model_tester.__class__

		return model_tester


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]   ,    _lowercase     : Any			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		get_test_classes(_lowercase			)

		lowercase__					:      Union[str, Any]      =		[]
		for test_class in test_classes:
				if model_class in test_class.all_model_classes:
						target_test_classes.append(_lowercase			)

    # sort with class names
		return sorted(_lowercase   ,    key=lambda _lowercase			: x.__name__			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Optional[int]			)   ->							Tuple:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		get_test_classes_for_model(_lowercase   ,    _lowercase			)

		lowercase__					:      str      =		[]
		for test_class in test_classes:
				lowercase__					:      Tuple      =		get_model_tester_from_test_class(_lowercase			)
				if tester_class is not None:
						tester_classes.append(_lowercase			)

    # sort with class names
		return sorted(_lowercase   ,    key=lambda _lowercase			: x.__name__			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      int      =		get_test_classes(_lowercase			)
		lowercase__					:      int      =		{test_class: get_model_tester_from_test_class(_lowercase			) for test_class in test_classes}
		return test_tester_mapping


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		get_model_classes(_lowercase			)
		lowercase__					:      int      =		{
		    model_class: get_test_classes_for_model(_lowercase   ,    _lowercase			) for model_class in model_classes
		}
		return model_test_mapping


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict			)   ->							Dict:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		get_model_classes(_lowercase			)
		lowercase__					:      Optional[Any]      =		{
		    model_class: get_tester_classes_for_model(_lowercase   ,    _lowercase			) for model_class in model_classes
		}
		return model_to_tester_mapping





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if isinstance(_lowercase   ,    _lowercase			):
				return o
		elif isinstance(_lowercase   ,    _lowercase			):
				return o.__name__
		elif isinstance(_lowercase   ,    (list, tuple)			):
				return [to_json(_lowercase			) for x in o]
		elif isinstance(_lowercase   ,    _lowercase			):
				return {to_json(_lowercase			): to_json(_lowercase			) for k, v in o.items()}
		else:
				return o









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


