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
  
  
  SCREAMING_SNAKE_CASE       = [nn.LayerNorm]
  
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE       = version.parse(version.parse(torch.__version__).base_version)
  
  SCREAMING_SNAKE_CASE       = parsed_torch_version_base >= version.parse("2.0")
  SCREAMING_SNAKE_CASE       = parsed_torch_version_base >= version.parse("1.12")
  SCREAMING_SNAKE_CASE       = parsed_torch_version_base >= version.parse("1.11")
  SCREAMING_SNAKE_CASE       = parsed_torch_version_base < version.parse("1.11")
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				self		)		->	Optional[int]:
   from torch import _softmax_backward_data
  
   if is_torch_less_than_1_11:
    return _softmax_backward_data(lowercase_    ,				lowercase_    ,				parent.dim    ,				self		)
   else:
    return _softmax_backward_data(lowercase_    ,				lowercase_    ,				parent.dim    ,				self.dtype		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_ = 0		)		->	nn.Linear:
   A__ =       index.to(layer.weight.device		)
   A__ =       layer.weight.index_select(lowercase_    ,				lowercase_		).clone().detach()
   if layer.bias is not None:
    if dim == 1:
     A__ =       layer.bias.clone().detach()
    else:
     A__ =       layer.bias[index].clone().detach()
   A__ =       list(layer.weight.size()		)
   A__ =       len(lowercase_		)
   A__ =       nn.Linear(new_size[1]    ,				new_size[0]    ,				bias=layer.bias is not None		).to(layer.weight.device		)
   A__ =       False
   new_layer.weight.copy_(W.contiguous()		)
   A__ =       True
   if layer.bias is not None:
    A__ =       False
    new_layer.bias.copy_(b.contiguous()		)
    A__ =       True
   return new_layer
  
  
  
  
  class 			UpperCAmelCase_						(		nn.Module							):
  
  
  
  
      def __init__(					self		: int			,				snake_case_		: List[Any]			,				snake_case_		: str   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       super().__init__()
       A__ =       nf
       A__ =       nn.Parameter(torch.empty(snake_case_			,				snake_case_   )   )
       A__ =       nn.Parameter(torch.zeros(snake_case_   )   )
       nn.init.normal_(self.weight			,				std=0.02   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: List[Any]   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       x.size()[:-1] + (self.nf,)
       A__ =       torch.addmm(self.bias			,				x.view(-1			,				x.size(-1   )   )			,				self.weight   )
       A__ =       x.view(snake_case_   )
       return x
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_ = 1		)		->	ConvaD:
   A__ =       index.to(layer.weight.device		)
   A__ =       layer.weight.index_select(lowercase_    ,				lowercase_		).clone().detach()
   if dim == 0:
    A__ =       layer.bias.clone().detach()
   else:
    A__ =       layer.bias[index].clone().detach()
   A__ =       list(layer.weight.size()		)
   A__ =       len(lowercase_		)
   A__ =       ConvaD(new_size[1]    ,				new_size[0]		).to(layer.weight.device		)
   A__ =       False
   new_layer.weight.copy_(W.contiguous()		)
   A__ =       True
   A__ =       False
   new_layer.bias.copy_(b.contiguous()		)
   A__ =       True
   return new_layer
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_ = None		)		->	Union[nn.Linear, ConvaD]:
   if isinstance(lowercase_    ,				nn.Linear		):
    return prune_linear_layer(lowercase_    ,				lowercase_    ,				dim=0 if dim is None else dim		)
   elif isinstance(lowercase_    ,				lowercase_		):
    return prune_convad_layer(lowercase_    ,				lowercase_    ,				dim=1 if dim is None else dim		)
   else:
    raise ValueError(f"""Can't prune layer of class {layer.__class__}"""		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				*lowercase_		)		->	torch.Tensor:
   assert len(lowercase_		) > 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
   A__ =       len(inspect.signature(lowercase_		).parameters		)
   if num_args_in_forward_chunk_fn != len(lowercase_		):
    raise ValueError(
        f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowercase_		)} input """
        "tensors are given"		)
  
   if chunk_size > 0:
    A__ =       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}"""		)
  
    A__ =       input_tensors[0].shape[chunk_dim] // chunk_size
  
    # chunk input tensor into tuples
    A__ =       tuple(input_tensor.chunk(lowercase_    ,				dim=lowercase_		) for input_tensor in input_tensors		)
    # apply forward fn to every tuple
    A__ =       tuple(forward_fn(*lowercase_		) for input_tensors_chunk in zip(*lowercase_		)		)
    # concatenate output at same dimension
    return torch.cat(lowercase_    ,				dim=lowercase_		)
  
   return forward_fn(*lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Tuple[Set[int], torch.LongTensor]:
   A__ =       torch.ones(lowercase_    ,				lowercase_		)
   A__ =       set(lowercase_		) - 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
    A__ =       head - sum(1 if h < head else 0 for h in already_pruned_heads		)
    A__ =       0
   A__ =       mask.view(-1		).contiguous().eq(1		)
   A__ =       torch.arange(len(lowercase_		)		)[mask].long()
   return heads, index
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							*lowercase_    ,				lowercase_ = None		)		->	Tuple[torch.Tensor, ...]:
   return torch.meshgrid(*lowercase_    ,				indexing=lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Tuple[torch.device, int, int]:
   return tensor.device, storage_ptr(lowercase_		), storage_size(lowercase_		)
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from transformers import PretrainedConfig
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: List[Any]=1			,				**snake_case_		: Any   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
       super().__init__(**snake_case_   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: Tuple=1			,				**snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
  
  
  
  
  
  