





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









import argparse
import os
import re


__UpperCamelCase:	Any = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase:	Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase:	List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : bool = False			)   ->							List[str]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Union[str, Any]      =		f.read()

		lowercase__					:      Optional[Any]      =		content.split('\n'			)
		lowercase__					:      Optional[int]      =		[]
		lowercase__					:      int      =		0
		while line_idx < len(_lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						lowercase__					:      Tuple      =		len(re.search(r'^(\s*)\S'   ,    lines[line_idx]			).groups()[0]			) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(' ' * indent + '('			):
								new_lines.append(lines[line_idx]			)
								line_idx += 1

						lowercase__					:      Tuple      =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										lowercase__					:      Any      =		line_idx
										while not lines[line_idx].startswith(' ' * indent + ')'			):
												line_idx += 1
										blocks.append('\n'.join(lines[start_idx : line_idx + 1]			)			)
								else:
										blocks.append(lines[line_idx]			)
								line_idx += 1

						# Sort blocks by their identifiers
						lowercase__					:      List[str]      =		sorted(_lowercase   ,    key=lambda _lowercase			: _re_identifier.search(_lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

		if overwrite:
				with open(_lowercase   ,    'w'   ,    encoding='utf-8'			) as f:
						f.write('\n'.join(_lowercase			)			)
		elif "\n".join(_lowercase			) != content:
				return True


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




		'''simple docstring'''
		lowercase__					:      List[Any]      =		[os.path.join(_lowercase   ,    _lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
		lowercase__					:      str      =		[sort_auto_mapping(_lowercase   ,    overwrite=_lowercase			) for fname in fnames]

		if not overwrite and any(_lowercase			):
				lowercase__					:      List[Any]      =		[f for f, d in zip(_lowercase   ,    _lowercase			) if d]
				raise ValueError(
				    f"""The following files have auto mappings that need sorting: {", ".join(_lowercase			)}. Run `make style` to fix"""
				    ' this.'			)


if __name__ == "__main__":
			__UpperCamelCase:	Tuple = argparse.ArgumentParser()
			parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
			__UpperCamelCase:	List[Any] = parser.parse_args()

			sort_all_auto_mappings(not args.check_only)


