





'''simple docstring'''


from ..utils import DummyObject, requires_backends


lowerCAmelCase      :  Union[str, Any]      							=None
def 							UpperCAmelCase_						(		*__lowerCamelCase      :      Tuple			,**__lowerCamelCase      :      Optional[int]				):
   requires_backends(__lowerCamelCase			,["sentencepiece", "tokenizers"]				)









'''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 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]			,__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      str=None				):
   lowercase_    :List[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.
      lowercase_    :int					    =				weight.view(torch.intaa				)
      lowercase_    :List[Any]					    =				"bfloat16"
   lowercase_    :Optional[Any]					    =				weight.cpu().numpy()
   lowercase_    :Optional[int]					    =				os.path.join(__lowerCamelCase			,F'{weight_name}.dat'				)
   if index is not None:
      if dtype is None:
         lowercase_    :str					    =				str(array.dtype				)
      lowercase_    :int					    =				{"dtype": dtype, "shape": list(array.shape				)}
   if array.ndim == 0:
      lowercase_    :int					    =				array[None]
   lowercase_    :List[Any]					    =				np.memmap(__lowerCamelCase			,dtype=array.dtype			,mode="w+"			,shape=array.shape				)
   lowercase_    :List[Any]					    =				array[:]
   file_array.flush()
   return index
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int			,__lowerCamelCase      :      Dict				):
   lowercase_    :int					    =				tuple(weight_info["shape"]				)
   if shape == ():
      # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
      lowercase_    :List[str]					    =				(1,)

   lowercase_    :List[str]					    =				weight_info["dtype"]
   if dtype == "bfloat16":
      # NumPy does not support bfloat16 so this was saved as a int16
      lowercase_    :Optional[Any]					    =				"int16"

   lowercase_    :str					    =				np.memmap(__lowerCamelCase			,dtype=__lowerCamelCase			,shape=__lowerCamelCase			,mode="r"				)

   if len(weight_info["shape"]				) == 0:
      lowercase_    :Union[str, Any]					    =				weight[0]
   lowercase_    :str					    =				torch.tensor(__lowerCamelCase				)
   if weight_info["dtype"] == "bfloat16":
      lowercase_    :Any					    =				weight.view(torch.bfloataa				)

   return weight
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict			,__lowerCamelCase      :      List[str]				):
   if index is None or len(__lowerCamelCase				) == 0:
      # Nothing to save
      return

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

   with open(__lowerCamelCase			,"w"			,encoding="utf-8"				) as f:
      json.dump(__lowerCamelCase			,__lowerCamelCase			,indent=2				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, os.PathLike]			,__lowerCamelCase      :      Dict[str, torch.Tensor]				):
   os.makedirs(__lowerCamelCase			,exist_ok=__lowerCamelCase				)
   lowercase_    :List[Any]					    =				{}
   for name, parameter in state_dict.items():
      lowercase_    :Tuple					    =				offload_weight(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,index=__lowerCamelCase				)

   # Update index
   save_offload_index(__lowerCamelCase			,__lowerCamelCase				)
class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      int			,   lowercase	:      Mapping			,   lowercase	:      str		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				dataset
        lowercase_    :List[str]					    =				prefix





     def __getitem__(				self	:      Any			,   lowercase	:      List[str]		):





        """simple docstring"""




        return self.dataset[F'{self.prefix}{key}']





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




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







     def __len__(				self	:      Any		):





        """simple docstring"""




        return len(self.dataset		)







class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      Tuple			,   lowercase	:      Dict[str, torch.Tensor] = None			,   lowercase	:      Optional[Union[str, os.PathLike]] = None			,   lowercase	:      Mapping = None			,   lowercase	:      Optional[Any]=None			,   ):





        """simple docstring"""




        if state_dict is None and save_folder is None:
           raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."		)

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





     def __getitem__(				self	:      Any			,   lowercase	:      str		):





        """simple docstring"""




        if key in self.state_dict:
           return self.state_dict[key]
        lowercase_    :int					    =				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

           lowercase_    :Dict					    =				"cpu" if self.device is None else self.device
           with safe_open(weight_info["safetensors_file"]			,   framework="pt"			,   device=lowercase		) as f:
              lowercase_    :Tuple					    =				f.get_tensor(weight_info.get("weight_name"			,   lowercase		)		)

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

        lowercase_    :str					    =				os.path.join(self.save_folder			,   F'{key}.dat'		)
        return load_offloaded_weight(lowercase			,   lowercase		)





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




        return iter(self.all_keys		)







     def __len__(				self	:      Tuple		):





        """simple docstring"""




        return len(self.all_keys		)







def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict[str, torch.Tensor]			,__lowerCamelCase      :      List[str]				):
   lowercase_    :List[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


