


"""simple docstring"""
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
			import torch

SCREAMING_SNAKE_CASE					:       Any							   =							logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
			from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






			def 						lowercase				(	cls		: Dict	)	->str:
				"""simple docstring"""





				return cls






def 						lowercase				(	_snake_case		: Union[Tuple, List]	,  _snake_case		: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None	,  _snake_case		: Optional["torch.device"] = None	,  _snake_case		: Optional["torch.dtype"] = None	,  _snake_case		: Optional["torch.layout"] = None	,  )	->Any:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							device
	__snake_case      :		Optional[int]         =							shape[0]

	__snake_case      :		Dict         =							layout or torch.strided
	__snake_case      :		Optional[Any]         =							device or torch.device('''cpu'''	)

	if generator is not None:
		__snake_case      :		Dict         =							generator.device.type if not isinstance(_snake_case	,  _snake_case	) else generator[0].device.type
		if gen_device_type != device.type and gen_device_type == "cpu":
			__snake_case      :		str         =							'''cpu'''
			if device != "mps":
				logger.info(
				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""	)
		elif gen_device_type != device.type and gen_device_type == "cuda":
			raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""	)

	if isinstance(_snake_case	,  _snake_case	):
		__snake_case      :		List[Any]         =							(1,) + shape[1:]
		__snake_case      :		Dict         =							[
		    torch.randn(_snake_case	,  generator=generator[i]	,  device=_snake_case	,  dtype=_snake_case	,  layout=_snake_case	)
		    for i in range(_snake_case	)
		]
		__snake_case      :		Dict         =							torch.cat(_snake_case	,  dim=0	).to(_snake_case	)
	else:
		__snake_case      :		Union[str, Any]         =							torch.randn(_snake_case	,  generator=_snake_case	,  device=_snake_case	,  dtype=_snake_case	,  layout=_snake_case	).to(_snake_case	)

	return latents






def 						lowercase				(	_snake_case		: Union[str, Any]	)	->Tuple:
	"""simple docstring"""





	if is_torch_version('''<'''	,  '''2.0.0'''	) or not hasattr(_snake_case	,  '''_dynamo'''	):
		return False
	return isinstance(_snake_case	,  torch._dynamo.eval_frame.OptimizedModule	)








'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowerCAmelCase__      				=			TypeVar("T", bound=bool)



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   T													=       value
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								UpperCAmelCase_		:   SkewNode[T] | None													=       None


							@property
							def     _snake_case				(      self)				->					T:
								return self._value





							@staticmethod
							def     _snake_case				(      _snake_case     ,      _snake_case)				->					SkewNode[T] | None:
								if not roota:
									return roota

								if not roota:
									return roota

								if roota.value > roota.value:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       roota, roota

								UpperCAmelCase_		:   Dict													=       roota
								UpperCAmelCase_		:   Dict													=       roota.right
								UpperCAmelCase_		:   Optional[int]													=       roota.left
								UpperCAmelCase_		:   Dict													=       SkewNode.merge(_snake_case     ,      _snake_case)

								return result



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case = ())				->					None:
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								if data:
									for item in data:
										self.insert(_snake_case)


							def __bool__(      self)				->					bool:
								return self._root is not None


							def __iter__(      self)				->					Iterator[T]:
								UpperCAmelCase_		:   list[Any]													=       []
								while self:
									result.append(self.pop())

								# Pushing items back to the heap not to clear it.
								for item in result:
									self.insert(_snake_case)

								return iter(_snake_case)


							def     _snake_case				(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   Any													=       SkewNode.merge(self._root     ,      SkewNode(_snake_case))


							def     _snake_case				(      self)				->					T | None:
								UpperCAmelCase_		:   Union[str, Any]													=       self.top()
								UpperCAmelCase_		:   Tuple													=       (
								    SkewNode.merge(self._root.left     ,      self._root.right) if self._root else None
								)

								return result


							def     _snake_case				(      self)				->					T:
								if not self._root:
									raise IndexError('Can\'t get top element for the empty heap.')
								return self._root.value





							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Union[str, Any]													=       None


if __name__ == "__main__":
						import doctest

						doctest.testmod()






