"""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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	bool						)  ->							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    		=					ma + ma
   else:
      ma += ma

   return ma



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	List[int]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					-1 * len(lowercase_						)
   __lowerCamelCase    		=					list(range(len(tensor.shape[:zero_index]						)						)						)
   return tensor.permute(first_inds + [zero_index + i for i in inds]						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	int						)  ->							torch.Tensor:
   return t.reshape(t.shape[:-no_dims] + (-1,)						)



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



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.types.Number = 2.3_1_2_5      , lowercase_     :	torch.types.Number = 2_1.6_8_7_5      , lowercase_     :	int = 64						)  ->							torch.Tensor:
   __lowerCamelCase    		=					torch.linspace(lowercase_      , lowercase_      , no_bins - 1      , device=pts.device						)
   __lowerCamelCase    		=					torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2      , dim=-1						)						)
   return torch.bucketize(lowercase_      , lowercase_						)



def       A__    (	lowercase_     :	Callable[[list], Any]      , lowercase_     :	List[dict]						)  ->							dict:
   __lowerCamelCase    		=					dicts[0]
   __lowerCamelCase    		=					{}
   for k, v in first.items():
      __lowerCamelCase    		=					[d[k] for d in dicts]
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_multimap(lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					fn(lowercase_						)

   return new_dict



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



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int = 0      , lowercase_     :	int = 0						)  ->							torch.Tensor:
   __lowerCamelCase    		=					[]
   for i, s in enumerate(data.shape[:no_batch_dims]						):
      __lowerCamelCase    		=					torch.arange(lowercase_						)
      __lowerCamelCase    		=					r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
      ranges.append(lowercase_						)

   __lowerCamelCase    		=					[slice(lowercase_						) for _ in range(len(data.shape						) - no_batch_dims						)]
   __lowerCamelCase    		=					inds
   ranges.extend(lowercase_						)
   # 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(lowercase_						)]


_SCREAMING_SNAKE_CASE	:		Dict   =   TypeVar('''T''')



def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	Dict[Any, Union[dict, list, tuple, T]]      , lowercase_     :	Type[T]						)  ->							Dict[Any, Union[dict, list, tuple, Any]]:
   __lowerCamelCase    		=					{}
   for k, v in dic.items():
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_map(lowercase_      , lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					tree_map(lowercase_      , lowercase_      , lowercase_						)

   return new_dict



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	T      , lowercase_     :	Type[T]						)  ->							Any:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	dict      , lowercase_     :	Type[T]						)  ->							dict:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	list      , lowercase_     :	Type[T]						)  ->							list:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	tuple      , lowercase_     :	Type[T]						)  ->							tuple:
   ...



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


_SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   partial(tree_map, leaf_type=torch.Tensor)











"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     ArgumentParser(
						    description=(
						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
						    )      )

						# Optional arguments for the launch helper
						parser.add_argument("""--num_cores""",   type=_lowercase,   default=1,   help="""Number of TPU cores to use (1 or 8)."""      )

						# positional
						parser.add_argument(
						    """training_script""",   type=_lowercase,   help=(
						        """The full path to the single TPU training """
						        """program/script to be launched in parallel, """
						        """followed by all the arguments for the """
						        """training script"""
						    ),   )

						# rest from the training program
						parser.add_argument("""training_script_args""",   nargs=_lowercase      )

						return parser.parse_args()
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[Any]		=     [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]

						xmp.spawn(mod._mp_fn,   args=(),   nprocs=args.num_cores      )


if __name__ == "__main__":
						main()
