code: 
  
  
  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 						__a      (  a,		a,		a,		a,		self					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         from torch import _softmax_backward_data
  
         if is_torch_less_than_1_11:
                return _softmax_backward_data(a,		a,		parent.dim,		self					)
         else:
                return _softmax_backward_data(a,		a,		parent.dim,		self.dtype					)
  
  
  
  def 						__a      (  a,		a,		a = 0					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				index.to(layer.weight.device					)
         _a          =				layer.weight.index_select(a,		a					).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(a					)
         _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 __snake_case		(					nn.Module      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:int				,  UpperCamelCase__							:List[Any]       ):
            super().__init__()
            _a          =				nf
            _a          =				nn.Parameter(torch.empty(UpperCamelCase__				,  UpperCamelCase__       )       )
            _a          =				nn.Parameter(torch.zeros(UpperCamelCase__       )       )
            nn.init.normal_(self.weight				,  std=0.02       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:List[str]       ):
            _a          =				x.size()[:-1] + (self.nf,)
            _a          =				torch.addmm(self.bias				,  x.view(-1				,  x.size(-1       )       )				,  self.weight       )
            _a          =				x.view(UpperCamelCase__       )
            return x
  
  
  
  
  
  
  def 						__a      (  a,		a,		a = 1					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				index.to(layer.weight.device					)
         _a          =				layer.weight.index_select(a,		a					).clone().detach()
         if dim == 0:
                _a          =				layer.bias.clone().detach()
         else:
                _a          =				layer.bias[index].clone().detach()
         _a          =				list(layer.weight.size()					)
         _a          =				len(a					)
         _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 						__a      (  a,		a,		a = None					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if isinstance(a,		nn.Linear					):
                return prune_linear_layer(a,		a,		dim=0 if dim is None else dim					)
         elif isinstance(a,		a					):
                return prune_convad_layer(a,		a,		dim=1 if dim is None else dim					)
         else:
                raise ValueError(F'Can\'t prune layer of class {layer.__class__}'					)
  
  
  
  def 						__a      (  a,		a,		a,		*a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert len(a					) > 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(a					).parameters					)
         if num_args_in_forward_chunk_fn != len(a					):
                raise ValueError(
                    F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a					)} 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(a,		dim=a					) for input_tensor in input_tensors					)
                # apply forward fn to every tuple
                _a          =				tuple(forward_fn(*a					) for input_tensors_chunk in zip(*a					)					)
                # concatenate output at same dimension
                return torch.cat(a,		dim=a					)
  
         return forward_fn(*a					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				torch.ones(a,		a					)
         _a          =				set(a					) - 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(a					)					)[mask].long()
         return heads, index
  
  
  
  def 						__a      (  *a,		a = None					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return torch.meshgrid(*a,		indexing=a					)
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return tensor.device, storage_ptr(a					), storage_size(a					)
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				data
            _a          =				None
  
  
  
  
     def __str__(		self							:Any       ):
            return f'{self.data}'
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Any       ):
            _a          =				None
            _a          =				None
  
  
  
  
     def __iter__(		self							:List[str]       ):
            _a          =				self.front
            while node:
                   yield node.data
                   _a          =				node.next
  
  
  
  
     def __len__(		self							:List[str]       ):
            return len(tuple(iter(self       )       )       )
  
  
  
  
     def __str__(		self							:Union[str, Any]       ):
            return " <- ".join(str(UpperCamelCase__       ) for item in self       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return len(self       ) == 0
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
            _a          =				Node(UpperCamelCase__       )
            if self.is_empty():
                   _a          =				_a          =				node
            else:
                   assert isinstance(self.rear				,  UpperCamelCase__       )
                   _a          =				node
                   _a          =				node
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            if self.is_empty():
                   raise IndexError("dequeue from empty queue"       )
            assert isinstance(self.front				,  UpperCamelCase__       )
            _a          =				self.front
            _a          =				self.front.next
            if self.front is None:
                   _a          =				None
            return node.data
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				_a          =				None
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
  