





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

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : Any	) ->						List[str]:
   A_					:				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
      A_					:				Optional[Any]          =			(1,)

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

   A_					:				int          =			np.memmap(_lowerCAmelCase				,				dtype=_lowerCAmelCase				,				shape=_lowerCAmelCase				,				mode="r"	)

   if len(weight_info["shape"]	) == 0:
      A_					:				Dict          =			weight[0]
   A_					:				Any          =			torch.tensor(_lowerCAmelCase	)
   if weight_info["dtype"] == "bfloat16":
      A_					:				Dict          =			weight.view(torch.bfloataa	)

   return weight

def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : Any	) ->						str:



   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_					:				Any          =			json.load(_lowerCAmelCase	)
   else:
      A_					:				Optional[Any]          =			{}
   current_index.update(_lowerCAmelCase	)

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

def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : Dict	) ->						Optional[Any]:
   os.makedirs(_lowerCAmelCase				,				exist_ok=_lowerCAmelCase	)
   A_					:				List[Any]          =			{}
   for name, parameter in state_dict.items():
      A_					:				Union[str, Any]          =			offload_weight(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				index=_lowerCAmelCase	)

   # Update index
   save_offload_index(_lowerCAmelCase				,				_lowerCAmelCase	)


class 					__magic_name__  (							lowerCAmelCase__							):





  """simple docstring"""



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





     '''simple docstring'''




     A_					:				List[Any]          =			dataset
     A_					:				Dict          =			prefix



  def __getitem__(							self       :Optional[int]			,      snake_case       :Optional[int]  ):





     '''simple docstring'''




     return self.dataset[f"{self.prefix}{key}"]



  def __iter__(							self       :Tuple  ):





     '''simple docstring'''




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





  def __len__(							self       :Dict  ):





     '''simple docstring'''




     return len(self.dataset  )




class 					__magic_name__  (							lowerCAmelCase__							):





  """simple docstring"""



  def __init__(							self       :int			,      snake_case       :Dict[str, torch.Tensor] = None			,      snake_case       :Optional[Union[str, os.PathLike]] = None			,      snake_case       :Mapping = None			,      snake_case       :int=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."  )

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



  def __getitem__(							self       :str			,      snake_case       :str  ):





     '''simple docstring'''




     if key in self.state_dict:
        return self.state_dict[key]
     A_					:				List[Any]          =			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_					:				Optional[int]          =			"cpu" if self.device is None else self.device
        with safe_open(weight_info["safetensors_file"]			,      framework="pt"			,      device=lowerCAmelCase_  ) as f:
           A_					:				Optional[Any]          =			f.get_tensor(weight_info.get("weight_name"			,      lowerCAmelCase_  )  )

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

     A_					:				str          =			os.path.join(self.save_folder			,      f"{key}.dat"  )
     return load_offloaded_weight(lowerCAmelCase_			,      lowerCAmelCase_  )



  def __iter__(							self       :str  ):





     '''simple docstring'''




     return iter(self.all_keys  )





  def __len__(							self       :Optional[Any]  ):





     '''simple docstring'''




     return len(self.all_keys  )




def      __snake_case						(					_lowerCAmelCase : Any				,				_lowerCAmelCase : List[Any]	) ->						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







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
