


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      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				# The first operation in a checkpoint can't be in-place, but it's
				# nice to have in-place addition during inference. Thus...
				if not inplace:
								lowerCAmelCase_	: Dict      			=			ma + ma
				else:
								ma += ma

				return ma



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				lowerCAmelCase_	: Tuple      			=			-1 * len(lowerCAmelCase_	)
				lowerCAmelCase_	: str      			=			list(range(len(tensor.shape[:zero_index]	)	)	)
				return tensor.permute(first_inds + [zero_index + i for i in inds]	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				return t.reshape(t.shape[:-no_dims] + (-1,)	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_ = 1e-4	)->	torch.Tensor:
				lowerCAmelCase_	: int      			=			mask.expand(*value.shape	)
				return torch.sum(mask * value	,				dim=lowerCAmelCase_	) / (eps + torch.sum(lowerCAmelCase_	,				dim=lowerCAmelCase_	))



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_ = 2.3125	,				lowerCAmelCase_ = 21.6875	,				lowerCAmelCase_ = 64	)->	torch.Tensor:
				lowerCAmelCase_	: Any      			=			torch.linspace(lowerCAmelCase_	,				lowerCAmelCase_	,				no_bins - 1	,				device=pts.device	)
				lowerCAmelCase_	: List[str]      			=			torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2	,				dim=-1	)	)
				return torch.bucketize(lowerCAmelCase_	,				lowerCAmelCase_	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	dict:
				lowerCAmelCase_	: Dict      			=			dicts[0]
				lowerCAmelCase_	: Dict      			=			{}
				for k, v in first.items():
								lowerCAmelCase_	: Optional[Any]      			=			[d[k] for d in dicts]
								if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
												lowerCAmelCase_	: Optional[Any]      			=			dict_multimap(lowerCAmelCase_	,				lowerCAmelCase_	)
								else:
												lowerCAmelCase_	: str      			=			fn(lowerCAmelCase_	)

				return new_dict



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	torch.Tensor:
				lowerCAmelCase_	: str      			=			v_bins.view(((1,) * len(x.shape	)) + (len(lowerCAmelCase_	),)	)
				lowerCAmelCase_	: Optional[int]      			=			x[..., None] - reshaped_bins
				lowerCAmelCase_	: str      			=			torch.argmin(torch.abs(lowerCAmelCase_	)	,				dim=-1	)
				return nn.functional.one_hot(lowerCAmelCase_	,				num_classes=len(lowerCAmelCase_	)	).float()



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_ = 0	,				lowerCAmelCase_ = 0	)->	torch.Tensor:
				lowerCAmelCase_	: List[Union[slice, torch.Tensor]]      			=			[]
				for i, s in enumerate(data.shape[:no_batch_dims]	):
								lowerCAmelCase_	: List[str]      			=			torch.arange(lowerCAmelCase_	)
								lowerCAmelCase_	: List[Any]      			=			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
								ranges.append(lowerCAmelCase_	)

				lowerCAmelCase_	: List[Union[slice, torch.Tensor]]      			=			[slice(lowerCAmelCase_	) for _ in range(len(data.shape	) - no_batch_dims	)]
				lowerCAmelCase_	: str      			=			inds
				ranges.extend(lowerCAmelCase_	)
				# 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(lowerCAmelCase_	)]


_UpperCAmelCase     :					List[Any]       =TypeVar("""T""")



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Dict[Any, Union[dict, list, tuple, Any]]:
				lowerCAmelCase_	: Dict[Any, Union[dict, list, tuple, Any]]      			=			{}
				for k, v in dic.items():
								if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
												lowerCAmelCase_	: Optional[int]      			=			dict_map(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)
								else:
												lowerCAmelCase_	: Dict      			=			tree_map(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)

				return new_dict



@overload
def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				...



@overload
def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	dict:
				...



@overload
def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	list:
				...



@overload
def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	tuple:
				...



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[Any]:
				if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								return dict_map(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)
				elif isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								return [tree_map(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	) for x in tree]
				elif isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								return tuple(tree_map(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	) for x in tree	)
				elif isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								return fn(lowerCAmelCase_	)
				else:
								print(type(lowerCAmelCase_	)	)
								raise ValueError('''Not supported'''	)






_UpperCAmelCase     :					Dict       =partial(tree_map, leaf_type=torch.Tensor)



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

_UpperCAmelCase     :					List[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      lowerCAmelCase	(						cls	)->	int:
										return cls



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				)->	int:
				lowerCAmelCase_	: Tuple      			=			device
				lowerCAmelCase_	: List[str]      			=			shape[0]

				lowerCAmelCase_	: Union[str, Any]      			=			layout or torch.strided
				lowerCAmelCase_	: Union[str, Any]      			=			device or torch.device('''cpu'''	)

				if generator is not None:
								lowerCAmelCase_	: List[str]      			=			generator.device.type if not isinstance(lowerCAmelCase_	,				lowerCAmelCase_	) else generator[0].device.type
								if gen_device_type != device.type and gen_device_type == "cpu":
												lowerCAmelCase_	: Optional[Any]      			=			'''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(lowerCAmelCase_	,				lowerCAmelCase_	):
								lowerCAmelCase_	: Dict      			=			(1,) + shape[1:]
								lowerCAmelCase_	: Union[str, Any]      			=			[
								    torch.randn(lowerCAmelCase_	,				generator=generator[i]	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	)
								    for i in range(lowerCAmelCase_	)
								]
								lowerCAmelCase_	: Tuple      			=			torch.cat(lowerCAmelCase_	,				dim=0	).to(lowerCAmelCase_	)
				else:
								lowerCAmelCase_	: int      			=			torch.randn(lowerCAmelCase_	,				generator=lowerCAmelCase_	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	).to(lowerCAmelCase_	)

				return latents




def      lowerCAmelCase	(						lowerCAmelCase_	)->	Optional[Any]:
				if is_torch_version('''<'''	,				'''2.0.0'''	) or not hasattr(lowerCAmelCase_	,				'''_dynamo'''	):
								return False
				return isinstance(lowerCAmelCase_	,				torch._dynamo.eval_frame.OptimizedModule	)