



'''simple docstring'''

import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_=None						)    ->   Any:
		_a  :   Optional[Any]				 =							None
		# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
		if str(weight.dtype						) == "torch.bfloat16":
				# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
				_a  :   int				 =							weight.view(torch.intaa						)
				_a  :   List[str]				 =							'bfloat16'
		_a  :   List[str]				 =							weight.cpu().numpy()
		_a  :   int				 =							os.path.join(lowerCAmelCase_		,    f"""{weight_name}.dat"""						)
		if index is not None:
				if dtype is None:
						_a  :   Dict				 =							str(array.dtype						)
				_a  :   Tuple				 =							{'dtype': dtype, 'shape': list(array.shape						)}
		if array.ndim == 0:
				_a  :   Tuple				 =							array[None]
		_a  :   str				 =							np.memmap(lowerCAmelCase_		,    dtype=array.dtype		,    mode='w+'		,    shape=array.shape						)
		_a  :   Dict				 =							array[:]
		file_array.flush()
		return index



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   str:
		_a  :   str				 =							tuple(weight_info['shape']						)
		if shape == ():
				# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
				_a  :   Tuple				 =							(1,)

		_a  :   Union[str, Any]				 =							weight_info['dtype']
		if dtype == "bfloat16":
				# NumPy does not support bfloat16 so this was saved as a int16
				_a  :   int				 =							'int16'

		_a  :   Union[str, Any]				 =							np.memmap(lowerCAmelCase_		,    dtype=lowerCAmelCase_		,    shape=lowerCAmelCase_		,    mode='r'						)

		if len(weight_info['shape']						) == 0:
				_a  :   int				 =							weight[0]
		_a  :   Optional[Any]				 =							torch.tensor(lowerCAmelCase_						)
		if weight_info["dtype"] == "bfloat16":
				_a  :   Dict				 =							weight.view(torch.bfloataa						)

		return weight



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Any:
		if index is None or len(lowerCAmelCase_						) == 0:
				# Nothing to save
				return

		_a  :   Union[str, Any]				 =							os.path.join(lowerCAmelCase_		,    'index.json'						)
		if os.path.isfile(lowerCAmelCase_						):
				with open(lowerCAmelCase_		,    'r'		,    encoding='utf-8'						) as f:
						_a  :   Dict				 =							json.load(lowerCAmelCase_						)
		else:
				_a  :   List[str]				 =							{}
		current_index.update(lowerCAmelCase_						)

		with open(lowerCAmelCase_		,    'w'		,    encoding='utf-8'						) as f:
				json.dump(lowerCAmelCase_		,    lowerCAmelCase_		,    indent=2						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Tuple:
		os.makedirs(lowerCAmelCase_		,    exist_ok=lowerCAmelCase_						)
		_a  :   Optional[Any]				 =							{}
		for name, parameter in state_dict.items():
				_a  :   Optional[Any]				 =							offload_weight(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    index=lowerCAmelCase_						)

		# Update index
		save_offload_index(lowerCAmelCase_		,    lowerCAmelCase_						)

class 			__magic_name__		(					_UpperCamelCase     ):







							def __init__(							self      : int			,_UpperCAmelCase      : Mapping			,_UpperCAmelCase      : str	):
									_a  :   int				 =							dataset
									_a  :   List[Any]				 =							prefix







							def __getitem__(							self      : Optional[Any]			,_UpperCAmelCase      : List[str]	):
									return self.dataset[F"""{self.prefix}{key}"""]







							def __iter__(							self      : List[str]	):
									return iter([key for key in self.dataset if key.startswith(self.prefix	)]	)







							def __len__(							self      : Tuple	):
									return len(self.dataset	)

class 			__magic_name__		(					_UpperCamelCase     ):







							def __init__(							self      : List[str]			,_UpperCAmelCase      : Dict[str, torch.Tensor] = None			,_UpperCAmelCase      : Optional[Union[str, os.PathLike]] = None			,_UpperCAmelCase      : Mapping = None			,_UpperCAmelCase      : int=None			,):
									if state_dict is None and save_folder is None:
											raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'	)

									_a  :   Optional[Any]				 =							{} if state_dict is None else state_dict
									_a  :   Union[str, Any]				 =							save_folder
									if index is None and save_folder is not None:
											with open(os.path.join(_UpperCAmelCase			,'index.json'	)	) as f:
													_a  :   Union[str, Any]				 =							json.load(_UpperCAmelCase	)
									_a  :   List[str]				 =							{} if index is None else index
									_a  :   Union[str, Any]				 =							list(self.state_dict.keys()	)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]	)
									_a  :   List[Any]				 =							device







							def __getitem__(							self      : str			,_UpperCAmelCase      : str	):
									# State dict gets priority
									if key in self.state_dict:
											return self.state_dict[key]
									_a  :   List[str]				 =							self.index[key]
									if weight_info.get('safetensors_file'	) is not None:
											if not is_safetensors_available():
													raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'	)

											from safetensors import safe_open

											_a  :   Tuple				 =							'cpu' if self.device is None else self.device
											with safe_open(weight_info['safetensors_file']			,framework='pt'			,device=_UpperCAmelCase	) as f:
													_a  :   int				 =							f.get_tensor(weight_info.get('weight_name'			,_UpperCAmelCase	)	)

											if "dtype" in weight_info:
													return tensor.to(getattr(_UpperCAmelCase			,weight_info['dtype']	)	)
											else:
													return tensor

									_a  :   Optional[int]				 =							os.path.join(self.save_folder			,F"""{key}.dat"""	)
									return load_offloaded_weight(_UpperCAmelCase			,_UpperCAmelCase	)







							def __iter__(							self      : Optional[int]	):
									return iter(self.all_keys	)







							def __len__(							self      : Dict	):
									return len(self.all_keys	)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   List[str]:
		_a  :   Any				 =							{}
		for module_name in submodule_names:
				# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
				# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
				result.update(
				    {
				        key: param
				        for key, param in state_dict.items()
				        if key == module_name or key.startswith(module_name + '.'						)
				    }						)
		return result









'''simple docstring'''

from __future__ import annotations

# Define the Gravitational Constant G and the function
__lowerCAmelCase		    =     6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   dict[str, float]:
		_a  :   str				 =							mass_a * mass_a

		if (force, mass_a, mass_a, distance).count(0						) != 1:
				raise ValueError('One and only one argument must be 0'						)
		if force < 0:
				raise ValueError('Gravitational force can not be negative'						)
		if distance < 0:
				raise ValueError('Distance can not be negative'						)
		if mass_a < 0 or mass_a < 0:
				raise ValueError('Mass can not be negative'						)
		if force == 0:
				_a  :   List[Any]				 =							GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
				return {"force": force}
		elif mass_a == 0:
				_a  :   Any				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_1": mass_a}
		elif mass_a == 0:
				_a  :   Tuple				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_2": mass_a}
		elif distance == 0:
				_a  :   str				 =							(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
				return {"distance": distance}
		raise ValueError('One and only one argument must be 0'						)


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()




