'''simple docstring'''




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types






def     snake_case__							(      _A: torch.Tensor				,       _A: torch.Tensor				,       _A: bool			)   ->     torch.Tensor:

		'''simple docstring'''


		if not inplace:
				lowerCAmelCase        =  ma + ma
		else:
				ma += ma

		return ma






def     snake_case__							(      _A: torch.Tensor				,       _A: List[int]			)   ->     torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase        =  -1 * len(_A			)
		lowerCAmelCase        =  list(range(len(tensor.shape[:zero_index]			)			)			)
		return tensor.permute(first_inds + [zero_index + i for i in inds]			)






def     snake_case__							(      _A: torch.Tensor				,       _A: int			)   ->     torch.Tensor:

		'''simple docstring'''


		return t.reshape(t.shape[:-no_dims] + (-1,)			)






def     snake_case__							(      _A: torch.Tensor				,       _A: torch.Tensor				,       _A: int				,       _A: float = 1e-4			)   ->     torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase        =  mask.expand(*value.shape			)
		return torch.sum(mask * value				,       dim=_A			) / (eps + torch.sum(_A				,       dim=_A			))






def     snake_case__							(      _A: torch.Tensor				,       _A: torch.types.Number = 2.3_125				,       _A: torch.types.Number = 21.6_875				,       _A: int = 64			)   ->     torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase        =  torch.linspace(_A				,       _A				,       no_bins - 1				,       device=pts.device			)
		lowerCAmelCase        =  torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2				,       dim=-1			)			)
		return torch.bucketize(_A				,       _A			)






def     snake_case__							(      _A: Callable[[list], Any]				,       _A: List[dict]			)   ->     dict:

		'''simple docstring'''


		lowerCAmelCase        =  dicts[0]
		lowerCAmelCase        =  {}
		for k, v in first.items():
				lowerCAmelCase        =  [d[k] for d in dicts]
				if isinstance(_A				,       _A			):
						lowerCAmelCase        =  dict_multimap(_A				,       _A			)
				else:
						lowerCAmelCase        =  fn(_A			)

		return new_dict






def     snake_case__							(      _A: torch.Tensor				,       _A: torch.Tensor			)   ->     torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase        =  v_bins.view(((1,) * len(x.shape			)) + (len(_A			),)			)
		lowerCAmelCase        =  x[..., None] - reshaped_bins
		lowerCAmelCase        =  torch.argmin(torch.abs(_A			)				,       dim=-1			)
		return nn.functional.one_hot(_A				,       num_classes=len(_A			)			).float()






def     snake_case__							(      _A: torch.Tensor				,       _A: torch.Tensor				,       _A: int = 0				,       _A: int = 0			)   ->     torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase        =  []
		for i, s in enumerate(data.shape[:no_batch_dims]			):
				lowerCAmelCase        =  torch.arange(_A			)
				lowerCAmelCase        =  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
				ranges.append(_A			)

		lowerCAmelCase        =  [slice(_A			) for _ in range(len(data.shape			) - no_batch_dims			)]
		lowerCAmelCase        =  inds
		ranges.extend(_A			)
		# Matt note: Editing this to get around the behaviour of using a list as an array index changing
		# in recent Numpy versions
		return data[tuple(_A			)]


__lowercase				   =  TypeVar('''T''')






def     snake_case__							(      _A: Callable[[T], Any]				,       _A: Dict[Any, Union[dict, list, tuple, T]]				,       _A: Type[T]			)   ->     Dict[Any, Union[dict, list, tuple, Any]]:

		'''simple docstring'''


		lowerCAmelCase        =  {}
		for k, v in dic.items():
				if isinstance(_A				,       _A			):
						lowerCAmelCase        =  dict_map(_A				,       _A				,       _A			)
				else:
						lowerCAmelCase        =  tree_map(_A				,       _A				,       _A			)

		return new_dict






@overload
def     snake_case__							(      _A: Callable[[T], Any]				,       _A: T				,       _A: Type[T]			)   ->     Any:

		'''simple docstring'''


		...






@overload
def     snake_case__							(      _A: Callable[[T], Any]				,       _A: dict				,       _A: Type[T]			)   ->     dict:

		'''simple docstring'''


		...






@overload
def     snake_case__							(      _A: Callable[[T], Any]				,       _A: list				,       _A: Type[T]			)   ->     list:

		'''simple docstring'''


		...






@overload
def     snake_case__							(      _A: Callable[[T], Any]				,       _A: tuple				,       _A: Type[T]			)   ->     tuple:

		'''simple docstring'''


		...






def     snake_case__							(      _A: int				,       _A: str				,       _A: Dict			)   ->     List[Any]:

		'''simple docstring'''


		if isinstance(_A				,       _A			):
				return dict_map(_A				,       _A				,       _A			)
		elif isinstance(_A				,       _A			):
				return [tree_map(_A				,       _A				,       _A			) for x in tree]
		elif isinstance(_A				,       _A			):
				return tuple(tree_map(_A				,       _A				,       _A			) for x in tree			)
		elif isinstance(_A				,       _A			):
				return fn(_A			)
		else:
				print(type(_A			)			)
				raise ValueError("""Not supported"""			)




__lowercase				   =  partial(tree_map, leaf_type=torch.Tensor)





'''simple docstring'''




from ..utils import DummyObject, requires_backends






class     a__( metaclass=lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Optional[int]								=	['''torch''', '''transformers''', '''k_diffusion''']



			def __init__(       self	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(self	,   ["""torch""", """transformers""", """k_diffusion"""])



			@classmethod
			def 							a_	(       cls	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(cls	,   ["""torch""", """transformers""", """k_diffusion"""])







			@classmethod
			def 							a_	(       cls	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(cls	,   ["""torch""", """transformers""", """k_diffusion"""])




