






"""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 :    List[Any]      =			[nn.LayerNorm]

_UpperCamelCase :    List[str]      =			logging.get_logger(__name__)

_UpperCamelCase :    List[Any]      =			version.parse(version.parse(torch.__version__).base_version)

_UpperCamelCase :    str      =			parsed_torch_version_base >= version.parse("""2.0""")
_UpperCamelCase :    Union[str, Any]      =			parsed_torch_version_base >= version.parse("""1.12""")
_UpperCamelCase :    List[str]      =			parsed_torch_version_base >= version.parse("""1.11""")
_UpperCamelCase :    List[str]      =			parsed_torch_version_base < version.parse("""1.11""")




def 					a			(  UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					str  ,						self				:					int		)	->		Dict:
 from torch import _softmax_backward_data

 if is_torch_less_than_1_11:
  return _softmax_backward_data(UpperCamelCase_  ,						UpperCamelCase_  ,						parent.dim  ,						self		)
 else:
  return _softmax_backward_data(UpperCamelCase_  ,						UpperCamelCase_  ,						parent.dim  ,						self.dtype		)




def 					a			(  UpperCamelCase_				:					nn.Linear  ,						UpperCamelCase_				:					torch.LongTensor  ,						UpperCamelCase_				:					int = 0		)	->		nn.Linear:
 __A						:							str      =						index.to(layer.weight.device		)
 __A						:							Optional[int]      =						layer.weight.index_select(UpperCamelCase_  ,						UpperCamelCase_		).clone().detach()
 if layer.bias is not None:
  if dim == 1:
   __A						:							str      =						layer.bias.clone().detach()
  else:
   __A						:							Any      =						layer.bias[index].clone().detach()
 __A						:							Dict      =						list(layer.weight.size()		)
 __A						:							Union[str, Any]      =						len(UpperCamelCase_		)
 __A						:							Dict      =						nn.Linear(new_size[1]  ,						new_size[0]  ,						bias=layer.bias is not None		).to(layer.weight.device		)
 __A						:							List[str]      =						False
 new_layer.weight.copy_(W.contiguous()		)
 __A						:							List[str]      =						True
 if layer.bias is not None:
  __A						:							Union[str, Any]      =						False
  new_layer.bias.copy_(b.contiguous()		)
  __A						:							List[str]      =						True
 return new_layer






class __a ( nn.Module					):



  def __init__(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		Any )		->				Any:
   super().__init__()
   __A						:							str      =						nf
   __A						:							List[Any]      =						nn.Parameter(torch.empty(lowercase_					,      lowercase_ ) )
   __A						:							Optional[Any]      =						nn.Parameter(torch.zeros(lowercase_ ) )
   nn.init.normal_(self.weight					,      std=0.02 )



  def     A_					(self	:		List[str]					,      lowercase_	:		Union[str, Any] )		->				Dict:
   __A						:							str      =						x.size()[:-1] + (self.nf,)
   __A						:							Dict      =						torch.addmm(self.bias					,      x.view(-1					,      x.size(-1 ) )					,      self.weight )
   __A						:							List[Any]      =						x.view(lowercase_ )
   return x






def 					a			(  UpperCamelCase_				:					ConvaD  ,						UpperCamelCase_				:					torch.LongTensor  ,						UpperCamelCase_				:					int = 1		)	->		ConvaD:
 __A						:							Optional[Any]      =						index.to(layer.weight.device		)
 __A						:							Dict      =						layer.weight.index_select(UpperCamelCase_  ,						UpperCamelCase_		).clone().detach()
 if dim == 0:
  __A						:							int      =						layer.bias.clone().detach()
 else:
  __A						:							Dict      =						layer.bias[index].clone().detach()
 __A						:							Tuple      =						list(layer.weight.size()		)
 __A						:							Optional[Any]      =						len(UpperCamelCase_		)
 __A						:							Optional[int]      =						ConvaD(new_size[1]  ,						new_size[0]		).to(layer.weight.device		)
 __A						:							Dict      =						False
 new_layer.weight.copy_(W.contiguous()		)
 __A						:							str      =						True
 __A						:							Optional[Any]      =						False
 new_layer.bias.copy_(b.contiguous()		)
 __A						:							Dict      =						True
 return new_layer




def 					a			(  UpperCamelCase_				:					Union[nn.Linear, ConvaD]  ,						UpperCamelCase_				:					torch.LongTensor  ,						UpperCamelCase_				:					Optional[int] = None		)	->		Union[nn.Linear, ConvaD]:
 if isinstance(UpperCamelCase_  ,						nn.Linear		):
  return prune_linear_layer(UpperCamelCase_  ,						UpperCamelCase_  ,						dim=0 if dim is None else dim		)
 elif isinstance(UpperCamelCase_  ,						UpperCamelCase_		):
  return prune_convad_layer(UpperCamelCase_  ,						UpperCamelCase_  ,						dim=1 if dim is None else dim		)
 else:
  raise ValueError(F"Can't prune layer of class {layer.__class__}"		)




def 					a			(  UpperCamelCase_				:					Callable[..., torch.Tensor]  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						*UpperCamelCase_				:					List[Any]		)	->		torch.Tensor:
 assert len(UpperCamelCase_		) > 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						:							Dict      =						len(inspect.signature(UpperCamelCase_		).parameters		)
 if num_args_in_forward_chunk_fn != len(UpperCamelCase_		):
  raise ValueError(
      F"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase_		)} input "
      'tensors are given'		)

 if chunk_size > 0:
  __A						:							Any      =						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						:							Union[str, Any]      =						input_tensors[0].shape[chunk_dim] // chunk_size

  # chunk input tensor into tuples
  __A						:							List[str]      =						tuple(input_tensor.chunk(UpperCamelCase_  ,						dim=UpperCamelCase_		) for input_tensor in input_tensors		)
  # apply forward fn to every tuple
  __A						:							List[Any]      =						tuple(forward_fn(*UpperCamelCase_		) for input_tensors_chunk in zip(*UpperCamelCase_		)		)
  # concatenate output at same dimension
  return torch.cat(UpperCamelCase_  ,						dim=UpperCamelCase_		)

 return forward_fn(*UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					List[int]  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					Set[int]		)	->		Tuple[Set[int], torch.LongTensor]:
 __A						:							str      =						torch.ones(UpperCamelCase_  ,						UpperCamelCase_		)
 __A						:							List[Any]      =						set(UpperCamelCase_		) - 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						:							Union[str, Any]      =						head - sum(1 if h < head else 0 for h in already_pruned_heads		)
  __A						:							List[Any]      =						0
 __A						:							List[Any]      =						mask.view(-1		).contiguous().eq(1		)
 __A						:							torch.LongTensor      =						torch.arange(len(UpperCamelCase_		)		)[mask].long()
 return heads, index




def 					a			(  *UpperCamelCase_				:					Union[torch.Tensor, List[torch.Tensor]]  ,						UpperCamelCase_				:					Optional[str] = None		)	->		Tuple[torch.Tensor, ...]:
 return torch.meshgrid(*UpperCamelCase_  ,						indexing=UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					torch.Tensor		)	->		Tuple[torch.device, int, int]:
 return tensor.device, storage_ptr(UpperCamelCase_		), storage_size(UpperCamelCase_		)













"""simple docstring"""


import numpy as np

_UpperCamelCase :    int      =			[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]






class __a :



  def __init__(self	:		Any )		->				None:
   __A						:							Any      =						np.array(lowercase_ )



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				np.ndarray:
   __A     ,       __A						:							Any      =						np.where(letter == self.SQUARE )
   __A						:							Tuple      =						np.concatenate([indexa + 1, indexa + 1] )
   return indexes



  def     A_					(self	:		Tuple					,      lowercase_	:		int					,      lowercase_	:		int )		->				str:
   return self.SQUARE[indexa - 1, indexa - 1]



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		str )		->				str:
   __A						:							Any      =						message.lower()
   __A						:							Optional[Any]      =						message.replace('j'					,      'i' )

   __A						:							Union[str, Any]      =						''
   for letter_index in range(len(lowercase_ ) ):
    if message[letter_index] != " ":
     __A						:							Union[str, Any]      =						self.letter_to_numbers(message[letter_index] )
     __A						:							Union[str, Any]      =						encoded_message + str(numbers[0] ) + str(numbers[1] )
    elif message[letter_index] == " ":
     __A						:							Optional[Any]      =						encoded_message + ' '

   return encoded_message



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				str:
   __A						:							List[str]      =						message.replace(' '					,      '  ' )
   __A						:							List[Any]      =						''
   for numbers_index in range(int(len(lowercase_ ) / 2 ) ):
    if message[numbers_index * 2] != " ":
     __A						:							Any      =						message[numbers_index * 2]
     __A						:							Tuple      =						message[numbers_index * 2 + 1]

     __A						:							Dict      =						self.numbers_to_letter(int(lowercase_ )					,      int(lowercase_ ) )
     __A						:							Union[str, Any]      =						decoded_message + letter
    elif message[numbers_index * 2] == " ":
     __A						:							int      =						decoded_message + ' '

   return decoded_message





