






'''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__		(					a_  :     int			,						a_  :     Optional[Any]			,						a_  :     str			,						a_  :     Tuple=None			)   -> Any:
       UpperCAmelCase__	:   Dict												=	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.
              UpperCAmelCase__	:   Optional[Any]												=	weight.view(torch.intaa			)
              UpperCAmelCase__	:   Dict												=	"bfloat16"
       UpperCAmelCase__	:   Optional[int]												=	weight.cpu().numpy()
       UpperCAmelCase__	:   Tuple												=	os.path.join(__snake_case			,						f"""{weight_name}.dat"""			)
       if index is not None:
              if dtype is None:
                     UpperCAmelCase__	:   int												=	str(array.dtype			)
              UpperCAmelCase__	:   Any												=	{"dtype": dtype, "shape": list(array.shape			)}
       if array.ndim == 0:
              UpperCAmelCase__	:   List[str]												=	array[None]
       UpperCAmelCase__	:   List[Any]												=	np.memmap(__snake_case			,						dtype=array.dtype			,						mode='''w+'''			,						shape=array.shape			)
       UpperCAmelCase__	:   List[str]												=	array[:]
       file_array.flush()
       return index






def  lowerCAmelCase__		(					a_  :     List[Any]			,						a_  :     Optional[Any]			)   -> Any:
       UpperCAmelCase__	:   Union[str, Any]												=	tuple(weight_info['''shape''']			)
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              UpperCAmelCase__	:   Dict												=	(1,)

       UpperCAmelCase__	:   Tuple												=	weight_info["dtype"]
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              UpperCAmelCase__	:   Tuple												=	"int16"

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

       if len(weight_info['''shape''']			) == 0:
              UpperCAmelCase__	:   Optional[Any]												=	weight[0]
       UpperCAmelCase__	:   List[str]												=	torch.tensor(__snake_case			)
       if weight_info["dtype"] == "bfloat16":
              UpperCAmelCase__	:   Union[str, Any]												=	weight.view(torch.bfloataa			)

       return weight






def  lowerCAmelCase__		(					a_  :     List[str]			,						a_  :     Optional[Any]			)   -> List[Any]:







       if index is None or len(__snake_case			) == 0:
              # Nothing to save
              return

       UpperCAmelCase__	:   str												=	os.path.join(__snake_case			,						'''index.json'''			)
       if os.path.isfile(__snake_case			):
              with open(__snake_case			,						'''r'''			,						encoding='''utf-8'''			) as f:
                     UpperCAmelCase__	:   str												=	json.load(__snake_case			)
       else:
              UpperCAmelCase__	:   Dict												=	{}
       current_index.update(__snake_case			)

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






def  lowerCAmelCase__		(					a_  :     Optional[int]			,						a_  :     Dict			)   -> Union[str, Any]:
       os.makedirs(__snake_case			,						exist_ok=__snake_case			)
       UpperCAmelCase__	:   Any												=	{}
       for name, parameter in state_dict.items():
              UpperCAmelCase__	:   Tuple												=	offload_weight(__snake_case			,						__snake_case			,						__snake_case			,						index=__snake_case			)

       # Update index
       save_offload_index(__snake_case			,						__snake_case			)




class 					__UpperCAmelCase (		lowercase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Optional[Any]												=	dataset
             UpperCAmelCase__	:   Any												=	prefix




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




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




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




class 					__UpperCAmelCase (		lowercase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase=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.'''						)

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




      def __getitem__(					self  ,		_UpperCAmelCase						):





             if key in self.state_dict:
                    return self.state_dict[key]
             UpperCAmelCase__	:   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

                    UpperCAmelCase__	:   Any												=	"cpu" if self.device is None else self.device
                    with safe_open(weight_info['''safetensors_file''']  ,		framework='''pt'''  ,		device=_UpperCAmelCase						) as f:
                           UpperCAmelCase__	:   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

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




      def __iter__(					self						):
             return iter(self.all_keys						)




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






def  lowerCAmelCase__		(					a_  :     str			,						a_  :     Union[str, Any]			)   -> Optional[Any]:
       UpperCAmelCase__	:   str												=	{}
       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




import heapq







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[(0, start)]  # cost from start node,end node
      UpperCAmelCase_	:						List[Any]					   =		set()
      while heap:
            ((UpperCAmelCase_)   ,   (UpperCAmelCase_))	:						str					   =		heapq.heappop(__snake_case     )
            if u in visited:
                  continue
            visited.add(__snake_case     )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  UpperCAmelCase_	:						Tuple					   =		cost + c
                  heapq.heappush(__snake_case       ,	(next_item, v)     )
      return -1


__lowerCamelCase								=      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase								=      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()
