





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   _A (	SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     List[str] , SCREAMING_SNAKE_CASE  :     List[str]=None		):





  """simple docstring"""



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





  """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__			:      str            =(1,)

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

  a__			:      Dict            =np.memmap(SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE , shape=SCREAMING_SNAKE_CASE , mode="r"		)

  if len(weight_info["shape"]		) == 0:
    a__			:      List[Any]            =weight[0]
  a__			:      Tuple            =torch.tensor(SCREAMING_SNAKE_CASE		)
  if weight_info["dtype"] == "bfloat16":
    a__			:      Dict            =weight.view(torch.bfloataa		)

  return weight
def   _A (	SCREAMING_SNAKE_CASE  :     List[Any] , SCREAMING_SNAKE_CASE  :     Union[str, Any]		):





  """simple docstring"""



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

  a__			:      Optional[int]            =os.path.join(SCREAMING_SNAKE_CASE , "index.json"		)
  if os.path.isfile(SCREAMING_SNAKE_CASE		):
    with open(SCREAMING_SNAKE_CASE , "r" , encoding="utf-8"		) as f:
      a__			:      Any            =json.load(SCREAMING_SNAKE_CASE		)
  else:
    a__			:      Any            ={}
  current_index.update(SCREAMING_SNAKE_CASE		)

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





  """simple docstring"""



  os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE		)
  a__			:      Any            ={}
  for name, parameter in state_dict.items():
    a__			:      List[str]            =offload_weight(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , index=SCREAMING_SNAKE_CASE		)

  # Update index
  save_offload_index(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)







class        __lowerCAmelCase (					UpperCamelCase__):







      def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Optional[Any]:



        '''simple docstring'''




        a__			:      Optional[Any]            =dataset
        a__			:      str            =prefix







      def __getitem__(					self	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




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







      def __iter__(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




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







      def __len__(					self							)     ->		Optional[int]:



        '''simple docstring'''




        return len(self.dataset							)







class        __lowerCAmelCase (					UpperCamelCase__):







      def __init__(					self	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__=None	,   )     ->		Any:



        '''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__			:      int            ={} 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__			:      str            =json.load(lowerCAmelCase__							)
        a__			:      Tuple            ={} 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	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




        if key in self.state_dict:
          return self.state_dict[key]
        a__			:      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__			:      Optional[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[int]            =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__			:      Any            =os.path.join(self.save_folder	,   F'''{key}.dat'''							)
        return load_offloaded_weight(lowerCAmelCase__	,   lowerCAmelCase__							)







      def __iter__(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        return iter(self.all_keys							)







      def __len__(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        return len(self.all_keys							)
def   _A (	SCREAMING_SNAKE_CASE  :     Dict[str, torch.Tensor] , SCREAMING_SNAKE_CASE  :     List[str]		):





  """simple docstring"""



  a__			:      Optional[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













from __future__ import annotations
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):





  """simple docstring"""



  a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
  a__			:      str            =[]

  for character in message_part:
    tmp.append(character_to_number[character]		)

  for each in tmp:
    one += each[0]
    two += each[1]
    three += each[2]

  return one + two + three
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):





  """simple docstring"""



  a__      ,							a__			:      Any            ="", ""
  a__			:      Optional[int]            =[]

  for character in message_part:
    this_part += character_to_number[character]

  for digit in this_part:
    tmp += digit
    if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
      result.append(SCREAMING_SNAKE_CASE		)
      a__			:      str            =""

  return result[0], result[1], result[2]
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
  a__			:      List[str]            =message.replace(" " , ""		).upper()

  # Check length and characters
  if len(SCREAMING_SNAKE_CASE		) != 27:
    raise KeyError("Length of alphabet has to be 27."		)
  for each in message:
    if each not in alphabet:
      raise ValueError("Each message character has to be included in alphabet!"		)

    # Generate dictionares
  a__			:      Any            =(
      "111",
      "112",
      "113",
      "121",
      "122",
      "123",
      "131",
      "132",
      "133",
      "211",
      "212",
      "213",
      "221",
      "222",
      "223",
      "231",
      "232",
      "233",
      "311",
      "312",
      "313",
      "321",
      "322",
      "323",
      "331",
      "332",
      "333",
  )
  a__			:      Tuple            ={}
  a__			:      List[str]            ={}
  for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
    a__			:      Dict            =number
    a__			:      Union[str, Any]            =letter

  return message, alphabet, character_to_number, number_to_character
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):





  """simple docstring"""



  a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  a__      ,							a__			:      Union[str, Any]            ="", ""

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
    encrypted_numeric += __encrypt_part(
        message[i : i + period] , SCREAMING_SNAKE_CASE		)

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
    encrypted += number_to_character[encrypted_numeric[i : i + 3]]

  return encrypted
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):





  """simple docstring"""



  a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  a__			:      Optional[Any]            =[]
  a__			:      Any            =""

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
    a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)

    for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
      decrypted_numeric.append(a[j] + b[j] + c[j]		)

  for each in decrypted_numeric:
    decrypted += number_to_character[each]

  return decrypted


if __name__ == "__main__":
     UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
     UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
     UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
     print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")






