


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		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	Dict=None		):




      """simple docstring"""





      a		:int											= 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		:Optional[int]											= weight.view(torch.intaa		)
            a		:str											= '''bfloat16'''
      a		:Tuple											= weight.cpu().numpy()
      a		:Union[str, Any]											= os.path.join(UpperCAmelCase_				,						F'''{weight_name}.dat'''		)
      if index is not None:
            if dtype is None:
                  a		:List[str]											= str(array.dtype		)
            a		:Dict											= {'''dtype''': dtype, '''shape''': list(array.shape		)}
      if array.ndim == 0:
            a		:Dict											= array[None]
      a		:List[Any]											= np.memmap(UpperCAmelCase_				,						dtype=array.dtype				,						mode='''w+'''				,						shape=array.shape		)
      a		:List[str]											= array[:]
      file_array.flush()
      return index
def        __lowerCamelCase		(				UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Tuple		):




      """simple docstring"""





      a		:Dict											= tuple(weight_info['''shape''']		)
      if shape == ():
            # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
            a		:int											= (1,)

      a		:Optional[int]											= weight_info['''dtype''']
      if dtype == "bfloat16":
            # NumPy does not support bfloat16 so this was saved as a int16
            a		:Dict											= '''int16'''

      a		:str											= np.memmap(UpperCAmelCase_				,						dtype=UpperCAmelCase_				,						shape=UpperCAmelCase_				,						mode='''r'''		)

      if len(weight_info['''shape''']		) == 0:
            a		:str											= weight[0]
      a		:List[Any]											= torch.tensor(UpperCAmelCase_		)
      if weight_info["dtype"] == "bfloat16":
            a		:Dict											= weight.view(torch.bfloataa		)

      return weight
def        __lowerCamelCase		(				UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





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

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

      with open(UpperCAmelCase_				,						'''w'''				,						encoding='''utf-8'''		) as f:
            json.dump(UpperCAmelCase_				,						UpperCAmelCase_				,						indent=2		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, os.PathLike]				,						UpperCAmelCase_		:	Dict[str, torch.Tensor]		):




      """simple docstring"""





      os.makedirs(UpperCAmelCase_				,						exist_ok=UpperCAmelCase_		)
      a		:Tuple											= {}
      for name, parameter in state_dict.items():
            a		:List[str]											= offload_weight(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_				,						index=UpperCAmelCase_		)

      # Update index
      save_offload_index(UpperCAmelCase_				,						UpperCAmelCase_		)







class 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Union[str, Any]											= dataset
       a		:Dict											= prefix




 def __getitem__(	self      ,						_lowerCamelCase					):
       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 	_snake_case						(   _snake_case							):




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




 def __getitem__(	self      ,						_lowerCamelCase					):
       # State dict gets priority
       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		:List[Any]											= '''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					):
       return iter(self.all_keys					)




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







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




      """simple docstring"""





      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 numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a							,       a		:Union[str, Any]											= a.shape
      a		:Any											= min(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Tuple											= np.eye(UpperCAmelCase_		)
      a		:Any											= a.copy()

      for k in range(t - 1		):
            # select a column of modified matrix A':
            a		:List[str]											= r[k:, [k]]
            # construct first basis vector
            a		:List[str]											= np.zeros_like(UpperCAmelCase_		)
            a		:List[Any]											= 1.0
            # determine scaling factor
            a		:Union[str, Any]											= np.linalg.norm(UpperCAmelCase_		)
            # construct vector v for Householder reflection
            a		:List[str]											= x + np.sign(x[0]		) * alpha * ea
            v /= np.linalg.norm(UpperCAmelCase_		)

            # construct the Householder matrix
            a		:Union[str, Any]											= np.eye(m - k		) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            a		:Tuple											= np.block([[np.eye(UpperCAmelCase_		), np.zeros((k, m - k)		)], [np.zeros((m - k, k)		), q_k]]		)

            a		:List[Any]											= q @ q_k.T
            a		:int											= q_k @ r

      return q, r


if __name__ == "__main__":
     import doctest

     doctest.testmod()





