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
  
  
  lowercase_			       =				[nn.LayerNorm]
  
  lowercase_			       =				logging.get_logger(__name__)
  
  lowercase_			       =				version.parse(version.parse(torch.__version__).base_version)
  
  lowercase_			       =				parsed_torch_version_base >= version.parse("""2.0""")
  lowercase_			       =				parsed_torch_version_base >= version.parse("""1.12""")
  lowercase_			       =				parsed_torch_version_base >= version.parse("""1.11""")
  lowercase_			       =				parsed_torch_version_base < version.parse("""1.11""")
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					List[Any]      , __lowerCamelCase				:					Dict      , self				:					Tuple					)		->Dict:
  				from torch import _softmax_backward_data
  
  				if is_torch_less_than_1_11:
  								return _softmax_backward_data(__lowerCamelCase      , __lowerCamelCase      , parent.dim      , self					)
  				else:
  								return _softmax_backward_data(__lowerCamelCase      , __lowerCamelCase      , parent.dim      , self.dtype					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					nn.Linear      , __lowerCamelCase				:					torch.LongTensor      , __lowerCamelCase				:					int = 0					)		->nn.Linear:
  				_SCREAMING_SNAKE_CASE             =    index.to(layer.weight.device					)
  				_SCREAMING_SNAKE_CASE             =    layer.weight.index_select(__lowerCamelCase      , __lowerCamelCase					).clone().detach()
  				if layer.bias is not None:
  								if dim == 1:
  												_SCREAMING_SNAKE_CASE             =    layer.bias.clone().detach()
  								else:
  												_SCREAMING_SNAKE_CASE             =    layer.bias[index].clone().detach()
  				_SCREAMING_SNAKE_CASE             =    list(layer.weight.size()					)
  				_SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					)
  				_SCREAMING_SNAKE_CASE             =    nn.Linear(new_size[1]      , new_size[0]      , bias=layer.bias is not None					).to(layer.weight.device					)
  				_SCREAMING_SNAKE_CASE             =    False
  				new_layer.weight.copy_(W.contiguous()					)
  				_SCREAMING_SNAKE_CASE             =    True
  				if layer.bias is not None:
  								_SCREAMING_SNAKE_CASE             =    False
  								new_layer.bias.copy_(b.contiguous()					)
  								_SCREAMING_SNAKE_CASE             =    True
  				return new_layer
  
  
  class       a_  (       nn.Module ):
  
  				'''simple docstring'''
  
  
  				def __init__(	self				,	A				,	A )	->     Union[str, Any]:
  								super().__init__()
  								_SCREAMING_SNAKE_CASE             =    nf
  								_SCREAMING_SNAKE_CASE             =    nn.Parameter(torch.empty(A				,	A ) )
  								_SCREAMING_SNAKE_CASE             =    nn.Parameter(torch.zeros(A ) )
  								nn.init.normal_(self.weight				,	std=0.02 )
  
  
  
  
  
  				def     snake_case_(	self				,	A )	->     Any:
  								_SCREAMING_SNAKE_CASE             =    x.size()[:-1] + (self.nf,)
  								_SCREAMING_SNAKE_CASE             =    torch.addmm(self.bias				,	x.view(-1				,	x.size(-1 ) )				,	self.weight )
  								_SCREAMING_SNAKE_CASE             =    x.view(A )
  								return x
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					ConvaD      , __lowerCamelCase				:					torch.LongTensor      , __lowerCamelCase				:					int = 1					)		->ConvaD:
  				_SCREAMING_SNAKE_CASE             =    index.to(layer.weight.device					)
  				_SCREAMING_SNAKE_CASE             =    layer.weight.index_select(__lowerCamelCase      , __lowerCamelCase					).clone().detach()
  				if dim == 0:
  								_SCREAMING_SNAKE_CASE             =    layer.bias.clone().detach()
  				else:
  								_SCREAMING_SNAKE_CASE             =    layer.bias[index].clone().detach()
  				_SCREAMING_SNAKE_CASE             =    list(layer.weight.size()					)
  				_SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					)
  				_SCREAMING_SNAKE_CASE             =    ConvaD(new_size[1]      , new_size[0]					).to(layer.weight.device					)
  				_SCREAMING_SNAKE_CASE             =    False
  				new_layer.weight.copy_(W.contiguous()					)
  				_SCREAMING_SNAKE_CASE             =    True
  				_SCREAMING_SNAKE_CASE             =    False
  				new_layer.bias.copy_(b.contiguous()					)
  				_SCREAMING_SNAKE_CASE             =    True
  				return new_layer
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[nn.Linear, ConvaD]      , __lowerCamelCase				:					torch.LongTensor      , __lowerCamelCase				:					Optional[int] = None					)		->Union[nn.Linear, ConvaD]:
  				if isinstance(__lowerCamelCase      , nn.Linear					):
  								return prune_linear_layer(__lowerCamelCase      , __lowerCamelCase      , dim=0 if dim is None else dim					)
  				elif isinstance(__lowerCamelCase      , __lowerCamelCase					):
  								return prune_convad_layer(__lowerCamelCase      , __lowerCamelCase      , dim=1 if dim is None else dim					)
  				else:
  								raise ValueError(F'Can\'t prune layer of class {layer.__class__}'					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Callable[..., torch.Tensor]      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , *__lowerCamelCase				:					List[str]					)		->torch.Tensor:
  				assert len(__lowerCamelCase					) > 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
  				_SCREAMING_SNAKE_CASE             =    len(inspect.signature(__lowerCamelCase					).parameters					)
  				if num_args_in_forward_chunk_fn != len(__lowerCamelCase					):
  								raise ValueError(
  								    F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowerCamelCase					)} input '
  								    """tensors are given"""					)
  
  				if chunk_size > 0:
  								_SCREAMING_SNAKE_CASE             =    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}'					)
  
  								_SCREAMING_SNAKE_CASE             =    input_tensors[0].shape[chunk_dim] // chunk_size
  
  								# chunk input tensor into tuples
  								_SCREAMING_SNAKE_CASE             =    tuple(input_tensor.chunk(__lowerCamelCase      , dim=__lowerCamelCase					) for input_tensor in input_tensors					)
  								# apply forward fn to every tuple
  								_SCREAMING_SNAKE_CASE             =    tuple(forward_fn(*__lowerCamelCase					) for input_tensors_chunk in zip(*__lowerCamelCase					)					)
  								# concatenate output at same dimension
  								return torch.cat(__lowerCamelCase      , dim=__lowerCamelCase					)
  
  				return forward_fn(*__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[int]      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					Set[int]					)		->Tuple[Set[int], torch.LongTensor]:
  				_SCREAMING_SNAKE_CASE             =    torch.ones(__lowerCamelCase      , __lowerCamelCase					)
  				_SCREAMING_SNAKE_CASE             =    set(__lowerCamelCase					) - 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
  								_SCREAMING_SNAKE_CASE             =    head - sum(1 if h < head else 0 for h in already_pruned_heads					)
  								_SCREAMING_SNAKE_CASE             =    0
  				_SCREAMING_SNAKE_CASE             =    mask.view(-1					).contiguous().eq(1					)
  				_SCREAMING_SNAKE_CASE             =    torch.arange(len(__lowerCamelCase					)					)[mask].long()
  				return heads, index
  
  
  
  
  
  def lowerCamelCase		(  *__lowerCamelCase				:					Union[torch.Tensor, List[torch.Tensor]]      , __lowerCamelCase				:					Optional[str] = None					)		->Tuple[torch.Tensor, ...]:
  				return torch.meshgrid(*__lowerCamelCase      , indexing=__lowerCamelCase					)
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					torch.Tensor					)		->Tuple[torch.device, int, int]:
  				return tensor.device, storage_ptr(__lowerCamelCase					), storage_size(__lowerCamelCase					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				round(time.time() - start_time, 2)
        print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
        # Done!  9,581 word patterns found in 0.58 seconds.
  
  
  
  