code: 
  
  
  
  
  
  
  """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        A	(							__snake_case:   List[Any]   ,    __snake_case:   List[Any]   ,    __snake_case:   List[Any]   ,    __snake_case:   Optional[int]=None     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						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.
                __magic_name__										=						weight.view(torch.intaa     )
                __magic_name__										=						'bfloat16'
         __magic_name__										=						weight.cpu().numpy()
         __magic_name__										=						os.path.join(__snake_case   ,    F"""{weight_name}.dat"""     )
         if index is not None:
                if dtype is None:
                       __magic_name__										=						str(array.dtype     )
                __magic_name__										=						{'dtype': dtype, 'shape': list(array.shape     )}
         if array.ndim == 0:
                __magic_name__										=						array[None]
         __magic_name__										=						np.memmap(__snake_case   ,    dtype=array.dtype   ,    mode='w+'   ,    shape=array.shape     )
         __magic_name__										=						array[:]
         file_array.flush()
         return index
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						tuple(weight_info['shape']     )
         if shape == ():
                # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
                __magic_name__										=						(1,)
  
         __magic_name__										=						weight_info['dtype']
         if dtype == "bfloat16":
                # NumPy does not support bfloat16 so this was saved as a int16
                __magic_name__										=						'int16'
  
         __magic_name__										=						np.memmap(__snake_case   ,    dtype=__snake_case   ,    shape=__snake_case   ,    mode='r'     )
  
         if len(weight_info['shape']     ) == 0:
                __magic_name__										=						weight[0]
         __magic_name__										=						torch.tensor(__snake_case     )
         if weight_info["dtype"] == "bfloat16":
                __magic_name__										=						weight.view(torch.bfloataa     )
  
         return weight
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   str     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         if index is None or len(__snake_case     ) == 0:
                # Nothing to save
                return
  
         __magic_name__										=						os.path.join(__snake_case   ,    'index.json'     )
         if os.path.isfile(__snake_case     ):
                with open(__snake_case   ,    'r'   ,    encoding='utf-8'     ) as f:
                       __magic_name__										=						json.load(__snake_case     )
         else:
                __magic_name__										=						{}
         current_index.update(__snake_case     )
  
         with open(__snake_case   ,    'w'   ,    encoding='utf-8'     ) as f:
                json.dump(__snake_case   ,    __snake_case   ,    indent=2     )
  
  
  def        A	(							__snake_case:   Union[str, os.PathLike]   ,    __snake_case:   Dict[str, torch.Tensor]     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         os.makedirs(__snake_case   ,    exist_ok=__snake_case     )
         __magic_name__										=						{}
         for name, parameter in state_dict.items():
                __magic_name__										=						offload_weight(__snake_case   ,    __snake_case   ,    __snake_case   ,    index=__snake_case     )
  
         # Update index
         save_offload_index(__snake_case   ,    __snake_case     )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Optional[Any]							,						UpperCamelCase_   :  Mapping							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						dataset
             __magic_name__										=						prefix
  
  
  
  
  
  
  
      def __getitem__(		self   :  Tuple							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.dataset[f"""{self.prefix}{key}"""]
  
  
  
  
  
  
  
      def __iter__(		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             return iter([key for key in self.dataset if key.startswith(self.prefix   )]   )
  
  
  
  
  
  
  
      def __len__(		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return len(self.dataset   )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  str							,						UpperCamelCase_   :  Dict[str, torch.Tensor] = None							,						UpperCamelCase_   :  Optional[Union[str, os.PathLike]] = None							,						UpperCamelCase_   :  Mapping = None							,						UpperCamelCase_   :  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.'   )
  
             __magic_name__										=						{} if state_dict is None else state_dict
             __magic_name__										=						save_folder
             if index is None and save_folder is not None:
                    with open(os.path.join(UpperCamelCase_							,						'index.json'   )   ) as f:
                           __magic_name__										=						json.load(UpperCamelCase_   )
             __magic_name__										=						{} if index is None else index
             __magic_name__										=						list(self.state_dict.keys()   )
             self.all_keys.extend([key for key in self.index if key not in self.all_keys]   )
             __magic_name__										=						device
  
  
  
  
  
  
  
      def __getitem__(		self   :  Dict							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if key in self.state_dict:
                    return self.state_dict[key]
             __magic_name__										=						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
  
                    __magic_name__										=						'cpu' if self.device is None else self.device
                    with safe_open(weight_info['safetensors_file']							,						framework='pt'							,						device=UpperCamelCase_   ) as f:
                           __magic_name__										=						f.get_tensor(weight_info.get('weight_name'							,						UpperCamelCase_   )   )
  
                    if "dtype" in weight_info:
                           return tensor.to(getattr(UpperCamelCase_							,						weight_info['dtype']   )   )
                    else:
                           return tensor
  
             __magic_name__										=						os.path.join(self.save_folder							,						f"""{key}.dat"""   )
             return load_offloaded_weight(UpperCamelCase_							,						UpperCamelCase_   )
  
  
  
  
  
  
  
      def __iter__(		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             return iter(self.all_keys   )
  
  
  
  
  
  
  
      def __len__(		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             return len(self.all_keys   )
  
  
  def        A	(							__snake_case:   Dict[str, torch.Tensor]   ,    __snake_case:   List[str]     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						{}
         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
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )