






"""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


UpperCamelCase											=     [nn.LayerNorm]

UpperCamelCase											=     logging.get_logger(__name__)

UpperCamelCase											=     version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase											=     parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase											=     parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase											=     parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase											=     parsed_torch_version_base < version.parse("""1.11""")






def 							lowerCAmelCase			(   UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				Optional[Any]  ,			UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				Dict  ,			self:				Any       )							->				Optional[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 							lowerCAmelCase			(   UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				Tuple  ,			UpperCamelCase_:				List[Any] = 0       )							->				Optional[Any]:







  '''simple docstring'''







  _a					      =						index.to(layer.weight.device       )
  _a					      =						layer.weight.index_select(snake_case__  ,			snake_case__       ).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(snake_case__       )
  _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  lowercase_     (nn.Module  ):






      def __init__(     self  ,  a_  ,  a_						)						->Tuple:







        '''simple docstring'''


        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       lowerCamelCase__     (     self  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _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 							lowerCAmelCase			(   UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Optional[int] = 1       )							->				str:







  '''simple docstring'''







  _a					      =						index.to(layer.weight.device       )
  _a					      =						layer.weight.index_select(snake_case__  ,			snake_case__       ).clone().detach()
  if dim == 0:
    _a					      =						layer.bias.clone().detach()
  else:
    _a					      =						layer.bias[index].clone().detach()
  _a					      =						list(layer.weight.size()       )
  _a					      =						len(snake_case__       )
  _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 							lowerCAmelCase			(   UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				Any  ,			UpperCamelCase_:				Optional[Any] = None       )							->				List[Any]:







  '''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 							lowerCAmelCase			(   UpperCamelCase_:				Union[str, Any]  ,			UpperCamelCase_:				Any  ,			UpperCamelCase_:				int  ,			*UpperCamelCase_:				int       )							->				int:







  '''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
  _a					      =						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:
    _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(snake_case__  ,			dim=snake_case__       ) for input_tensor in input_tensors       )
    # apply forward fn to every tuple
    _a					      =						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 							lowerCAmelCase			(   UpperCamelCase_:				Optional[Any]  ,			UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				str  ,			UpperCamelCase_:				Optional[Any]       )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						torch.ones(snake_case__  ,			snake_case__       )
  _a					      =						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
    _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(snake_case__       )       )[mask].long()
  return heads, index






def 							lowerCAmelCase			(   *UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				str = None       )							->				List[str]:







  '''simple docstring'''







  return torch.meshgrid(*snake_case__  ,			indexing=snake_case__       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				Any:







  '''simple docstring'''







  return tensor.device, storage_ptr(snake_case__       ), storage_size(snake_case__       )








'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     int										=  word.upper()
			__UpperCamelCase :     List[str]										=  0
			__UpperCamelCase :     int										=  {}
			__UpperCamelCase :     Tuple										=  []

			for letter in word:
						if letter not in letter_nums:
									__UpperCamelCase :     Tuple										=  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
				import pprint
				import time

				_lowerCAmelCase       =   time.time()
				with open('''dictionary.txt''') as in_file:
								_lowerCAmelCase       =   in_file.read().splitlines()

				_lowerCAmelCase       =   {}
				for word in word_list:
								_lowerCAmelCase       =   get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												_lowerCAmelCase       =   [word]

				with open('''word_patterns.txt''', '''w''') as out_file:
								out_file.write(pprint.pformat(all_patterns))

				_lowerCAmelCase       =   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.



