code: 
  
  
  
  
  
  
  """simple docstring"""
  import inspect
  from typing import Callable, List, Optional, Set, Tuple, Union
  
  import torch
  from packaging import version
  from safetensors.torch import storage_ptr, storage_size
  from torch import nn
  
  from .utils import logging
  
  
  snake_case   :   List[str]    		=		[nn.LayerNorm]
  
  snake_case   :   Optional[Any]    		=		logging.get_logger(__name__)
  
  snake_case   :   List[Any]    		=		version.parse(version.parse(torch.__version__).base_version)
  
  snake_case   :   List[Any]    		=		parsed_torch_version_base >= version.parse("""2.0""")
  snake_case   :   Dict    		=		parsed_torch_version_base >= version.parse("""1.12""")
  snake_case   :   Optional[int]    		=		parsed_torch_version_base >= version.parse("""1.11""")
  snake_case   :   Dict    		=		parsed_torch_version_base < version.parse("""1.11""")
  
  
  def        A	(							__snake_case:   Optional[int]   ,    __snake_case:   Tuple   ,    __snake_case:   str   ,    __snake_case:   Dict   ,    self:   List[Any]     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         from torch import _softmax_backward_data
  
         if is_torch_less_than_1_11:
                return _softmax_backward_data(__snake_case   ,    __snake_case   ,    parent.dim   ,    self     )
         else:
                return _softmax_backward_data(__snake_case   ,    __snake_case   ,    parent.dim   ,    self.dtype     )
  
  
  def        A	(							__snake_case:   nn.Linear   ,    __snake_case:   torch.LongTensor   ,    __snake_case:   int = 0     )						->		nn.Linear:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						index.to(layer.weight.device     )
         __magic_name__										=						layer.weight.index_select(__snake_case   ,    __snake_case     ).clone().detach()
         if layer.bias is not None:
                if dim == 1:
                       __magic_name__										=						layer.bias.clone().detach()
                else:
                       __magic_name__										=						layer.bias[index].clone().detach()
         __magic_name__										=						list(layer.weight.size()     )
         __magic_name__										=						len(__snake_case     )
         __magic_name__										=						nn.Linear(new_size[1]   ,    new_size[0]   ,    bias=layer.bias is not None     ).to(layer.weight.device     )
         __magic_name__										=						False
         new_layer.weight.copy_(W.contiguous()     )
         __magic_name__										=						True
         if layer.bias is not None:
                __magic_name__										=						False
                new_layer.bias.copy_(b.contiguous()     )
                __magic_name__										=						True
         return new_layer
  
  
  
  class 					UpperCamelCase__   (							nn.Module):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Optional[int]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__()
             __magic_name__										=						nf
             __magic_name__										=						nn.Parameter(torch.empty(UpperCamelCase_							,						UpperCamelCase_   )   )
             __magic_name__										=						nn.Parameter(torch.zeros(UpperCamelCase_   )   )
             nn.init.normal_(self.weight							,						std=0.02   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						x.size()[:-1] + (self.nf,)
             __magic_name__										=						torch.addmm(self.bias							,						x.view(-1							,						x.size(-1   )   )							,						self.weight   )
             __magic_name__										=						x.view(UpperCamelCase_   )
             return x
  
  
  def        A	(							__snake_case:   ConvaD   ,    __snake_case:   torch.LongTensor   ,    __snake_case:   int = 1     )						->		ConvaD:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						index.to(layer.weight.device     )
         __magic_name__										=						layer.weight.index_select(__snake_case   ,    __snake_case     ).clone().detach()
         if dim == 0:
                __magic_name__										=						layer.bias.clone().detach()
         else:
                __magic_name__										=						layer.bias[index].clone().detach()
         __magic_name__										=						list(layer.weight.size()     )
         __magic_name__										=						len(__snake_case     )
         __magic_name__										=						ConvaD(new_size[1]   ,    new_size[0]     ).to(layer.weight.device     )
         __magic_name__										=						False
         new_layer.weight.copy_(W.contiguous()     )
         __magic_name__										=						True
         __magic_name__										=						False
         new_layer.bias.copy_(b.contiguous()     )
         __magic_name__										=						True
         return new_layer
  
  
  def        A	(							__snake_case:   Union[nn.Linear, ConvaD]   ,    __snake_case:   torch.LongTensor   ,    __snake_case:   Optional[int] = None     )						->		Union[nn.Linear, ConvaD]:
  
         """simple docstring"""
  
  
  
  
  
         if isinstance(__snake_case   ,    nn.Linear     ):
                return prune_linear_layer(__snake_case   ,    __snake_case   ,    dim=0 if dim is None else dim     )
         elif isinstance(__snake_case   ,    __snake_case     ):
                return prune_convad_layer(__snake_case   ,    __snake_case   ,    dim=1 if dim is None else dim     )
         else:
                raise ValueError(F"""Can't prune layer of class {layer.__class__}"""     )
  
  
  def        A	(							__snake_case:   Callable[..., torch.Tensor]   ,    __snake_case:   int   ,    __snake_case:   int   ,    *__snake_case:   Union[str, Any]     )						->		torch.Tensor:
  
         """simple docstring"""
  
  
  
  
  
         assert len(__snake_case     ) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""
  
         # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
         __magic_name__										=						len(inspect.signature(__snake_case     ).parameters     )
         if num_args_in_forward_chunk_fn != len(__snake_case     ):
                raise ValueError(
                    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__snake_case     )} input """
                    'tensors are given'     )
  
         if chunk_size > 0:
                __magic_name__										=						input_tensors[0].shape[chunk_dim]
                for input_tensor in input_tensors:
                       if input_tensor.shape[chunk_dim] != tensor_shape:
                              raise ValueError(
                                  F"""All input tenors have to be of the same shape: {tensor_shape}, """
                                  F"""found shape {input_tensor.shape[chunk_dim]}"""     )
  
                if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
                       raise ValueError(
                           F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                           F"""size {chunk_size}"""     )
  
                __magic_name__										=						input_tensors[0].shape[chunk_dim] // chunk_size
  
                # chunk input tensor into tuples
                __magic_name__										=						tuple(input_tensor.chunk(__snake_case   ,    dim=__snake_case     ) for input_tensor in input_tensors     )
                # apply forward fn to every tuple
                __magic_name__										=						tuple(forward_fn(*__snake_case     ) for input_tensors_chunk in zip(*__snake_case     )     )
                # concatenate output at same dimension
                return torch.cat(__snake_case   ,    dim=__snake_case     )
  
         return forward_fn(*__snake_case     )
  
  
  def        A	(							__snake_case:   List[int]   ,    __snake_case:   int   ,    __snake_case:   int   ,    __snake_case:   Set[int]     )						->		Tuple[Set[int], torch.LongTensor]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						torch.ones(__snake_case   ,    __snake_case     )
         __magic_name__										=						set(__snake_case     ) - already_pruned_heads  # Convert to set and remove already pruned heads
         for head in heads:
                # Compute how many pruned heads are before the head and move the index accordingly
                __magic_name__										=						head - sum(1 if h < head else 0 for h in already_pruned_heads     )
                __magic_name__										=						0
         __magic_name__										=						mask.view(-1     ).contiguous().eq(1     )
         __magic_name__										=						torch.arange(len(__snake_case     )     )[mask].long()
         return heads, index
  
  
  def        A	(							*__snake_case:   Union[torch.Tensor, List[torch.Tensor]]   ,    __snake_case:   Optional[str] = None     )						->		Tuple[torch.Tensor, ...]:
  
         """simple docstring"""
  
  
  
  
  
         return torch.meshgrid(*__snake_case   ,    indexing=__snake_case     )
  
  
  
  
  
  
  def        A	(							__snake_case:   torch.Tensor     )						->		Tuple[torch.device, int, int]:
  
         """simple docstring"""
  
  
  
  
  
         return tensor.device, storage_ptr(__snake_case     ), storage_size(__snake_case     )
  
  
  
  
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_   )