





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   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__=None       ):
   lowercase__    =  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__    =  weight.view(torch.intaa       )
      lowercase__    =  "bfloat16"
   lowercase__    =  weight.cpu().numpy()
   lowercase__    =  os.path.join(__magic_name__  ,					f'''{weight_name}.dat'''       )
   if index is not None:
      if dtype is None:
         lowercase__    =  str(array.dtype       )
      lowercase__    =  {"dtype": dtype, "shape": list(array.shape       )}
   if array.ndim == 0:
      lowercase__    =  array[None]
   lowercase__    =  np.memmap(__magic_name__  ,					dtype=array.dtype  ,					mode="w+"  ,					shape=array.shape       )
   lowercase__    =  array[:]
   file_array.flush()
   return index


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  tuple(weight_info["shape"]       )
   if shape == ():
      # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
      lowercase__    =  (1,)

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

   lowercase__    =  np.memmap(__magic_name__  ,					dtype=__magic_name__  ,					shape=__magic_name__  ,					mode="r"       )

   if len(weight_info["shape"]       ) == 0:
      lowercase__    =  weight[0]
   lowercase__    =  torch.tensor(__magic_name__       )
   if weight_info["dtype"] == "bfloat16":
      lowercase__    =  weight.view(torch.bfloataa       )

   return weight


def _A   (    __magic_name__  ,					__magic_name__       ):
   if index is None or len(__magic_name__       ) == 0:
      # Nothing to save
      return

   lowercase__    =  os.path.join(__magic_name__  ,					"index.json"       )
   if os.path.isfile(__magic_name__       ):
      with open(__magic_name__  ,					"r"  ,					encoding="utf-8"       ) as f:
         lowercase__    =  json.load(__magic_name__       )
   else:
      lowercase__    =  {}
   current_index.update(__magic_name__       )

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


def _A   (    __magic_name__  ,					__magic_name__       ):
   os.makedirs(__magic_name__  ,					exist_ok=__magic_name__       )
   lowercase__    =  {}
   for name, parameter in state_dict.items():
      lowercase__    =  offload_weight(__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					index=__magic_name__       )

   # Update index
   save_offload_index(__magic_name__  ,					__magic_name__       )





class 					lowerCAmelCase							(   lowercase_					):







  def __init__(			self  :Dict    ,							_lowercase  :Mapping    ,							_lowercase  :str					):






     '''simple docstring'''





     lowercase__    =  dataset
     lowercase__    =  prefix







  def __getitem__(			self  :int    ,							_lowercase  :Union[str, Any]					):






     '''simple docstring'''





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







  def __iter__(			self  :Optional[Any]					):






     '''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 					lowerCAmelCase							(   lowercase_					):







  def __init__(			self  :Optional[Any]    ,							_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__    =  {} if state_dict is None else state_dict
     lowercase__    =  save_folder
     if index is None and save_folder is not None:
        with open(os.path.join(_lowercase    ,							"index.json"					)					) as f:
           lowercase__    =  json.load(_lowercase					)
     lowercase__    =  {} if index is None else index
     lowercase__    =  list(self.state_dict.keys()					)
     self.all_keys.extend([key for key in self.index if key not in self.all_keys]					)
     lowercase__    =  device







  def __getitem__(			self  :Optional[int]    ,							_lowercase  :str					):






     '''simple docstring'''





     if key in self.state_dict:
        return self.state_dict[key]
     lowercase__    =  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__    =  "cpu" if self.device is None else self.device
        with safe_open(weight_info["safetensors_file"]    ,							framework="pt"    ,							device=_lowercase					) as f:
           lowercase__    =  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__    =  os.path.join(self.save_folder    ,							f'''{key}.dat'''					)
     return load_offloaded_weight(_lowercase    ,							_lowercase					)







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






     '''simple docstring'''





     return iter(self.all_keys					)







  def __len__(			self  :Optional[Any]					):






     '''simple docstring'''





     return len(self.all_keys					)


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  {}
   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






