


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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		self		)	->     Dict:







 """simple docstring"""


 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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case = 0		)	->     nn.Linear:







 """simple docstring"""


 _lowercase				 =index.to(layer.weight.device		)
 _lowercase				 =layer.weight.index_select(UpperCamelCase_	,		UpperCamelCase_		).clone().detach()
 if layer.bias is not None:
  if dim == 1:
   _lowercase				 =layer.bias.clone().detach()
  else:
   _lowercase				 =layer.bias[index].clone().detach()
 _lowercase				 =list(layer.weight.size()		)
 _lowercase				 =len(UpperCamelCase_		)
 _lowercase				 =nn.Linear(new_size[1]	,		new_size[0]	,		bias=layer.bias is not None		).to(layer.weight.device		)
 _lowercase				 =False
 new_layer.weight.copy_(W.contiguous()		)
 _lowercase				 =True
 if layer.bias is not None:
  _lowercase				 =False
  new_layer.bias.copy_(b.contiguous()		)
  _lowercase				 =True
 return new_layer







class  lowerCamelCase__  (			nn.Module):





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Any:
      super().__init__()
      _lowercase				 =nf
      _lowercase				 =nn.Parameter(torch.empty(lowercase_						,	lowercase_			)			)
      _lowercase				 =nn.Parameter(torch.zeros(lowercase_			)			)
      nn.init.normal_(self.weight						,	std=0.02			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =x.size()[:-1] + (self.nf,)
      _lowercase				 =torch.addmm(self.bias						,	x.view(-1						,	x.size(-1			)			)						,	self.weight			)
      _lowercase				 =x.view(lowercase_			)
      return x



def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case = 1		)	->     ConvaD:







 """simple docstring"""


 _lowercase				 =index.to(layer.weight.device		)
 _lowercase				 =layer.weight.index_select(UpperCamelCase_	,		UpperCamelCase_		).clone().detach()
 if dim == 0:
  _lowercase				 =layer.bias.clone().detach()
 else:
  _lowercase				 =layer.bias[index].clone().detach()
 _lowercase				 =list(layer.weight.size()		)
 _lowercase				 =len(UpperCamelCase_		)
 _lowercase				 =ConvaD(new_size[1]	,		new_size[0]		).to(layer.weight.device		)
 _lowercase				 =False
 new_layer.weight.copy_(W.contiguous()		)
 _lowercase				 =True
 _lowercase				 =False
 new_layer.bias.copy_(b.contiguous()		)
 _lowercase				 =True
 return new_layer
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case = None		)	->     Union[nn.Linear, ConvaD]:







 """simple docstring"""


 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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		*__snake_case		)	->     torch.Tensor:







 """simple docstring"""


 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
 _lowercase				 =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:
  _lowercase				 =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}"		)

  _lowercase				 =input_tensors[0].shape[chunk_dim] // chunk_size

  # chunk input tensor into tuples
  _lowercase				 =tuple(input_tensor.chunk(UpperCamelCase_	,		dim=UpperCamelCase_		) for input_tensor in input_tensors		)
  # apply forward fn to every tuple
  _lowercase				 =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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Tuple[Set[int], torch.LongTensor]:







 """simple docstring"""


 _lowercase				 =torch.ones(UpperCamelCase_	,		UpperCamelCase_		)
 _lowercase				 =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
  _lowercase				 =head - sum(1 if h < head else 0 for h in already_pruned_heads		)
  _lowercase				 =0
 _lowercase				 =mask.view(-1		).contiguous().eq(1		)
 _lowercase				 =torch.arange(len(UpperCamelCase_		)		)[mask].long()
 return heads, index
def 							UpperCAmelCase_   (				*__snake_case	,		__snake_case = None		)	->     Tuple[torch.Tensor, ...]:







 """simple docstring"""


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



def 							UpperCAmelCase_   (				__snake_case		)	->     Tuple[torch.device, int, int]:







 """simple docstring"""


 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





