


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  (		A_  :	Optional[int]							,  A_  :	List[str]							,  A_  :	Dict							,  A_  :	Dict							,  self  :	List[Any]      )			->			Dict:

   '''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 	_lowerCamelCase  (		A_  :	nn.Linear							,  A_  :	torch.LongTensor							,  A_  :	int = 0      )			->			str:

   '''simple docstring'''






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



class 							lowercase__(						nn.Module			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         super().__init__()
         UpperCamelCase__ :			Union[str, Any] 						=nf
         UpperCamelCase__ :			Dict 						=nn.Parameter(torch.empty(lowerCamelCase__   ,			lowerCamelCase__))
         UpperCamelCase__ :			str 						=nn.Parameter(torch.zeros(lowerCamelCase__))
         nn.init.normal_(self.weight   ,			std=0.02)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=x.size()[:-1] + (self.nf,)
         UpperCamelCase__ :			List[str] 						=torch.addmm(self.bias   ,			x.view(-1   ,			x.size(-1))   ,			self.weight)
         UpperCamelCase__ :			Optional[int] 						=x.view(lowerCamelCase__)
         return x




def 	_lowerCamelCase  (		A_  :	ConvaD							,  A_  :	torch.LongTensor							,  A_  :	int = 1      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			Tuple 						=index.to(layer.weight.device      )
   UpperCamelCase__ :			Dict 						=layer.weight.index_select(a__							,  a__      ).clone().detach()
   if dim == 0:
      UpperCamelCase__ :			List[str] 						=layer.bias.clone().detach()
   else:
      UpperCamelCase__ :			Any 						=layer.bias[index].clone().detach()
   UpperCamelCase__ :			Dict 						=list(layer.weight.size()      )
   UpperCamelCase__ :			Optional[int] 						=len(a__      )
   UpperCamelCase__ :			str 						=ConvaD(new_size[1]							,  new_size[0]      ).to(layer.weight.device      )
   UpperCamelCase__ :			Optional[Any] 						=False
   new_layer.weight.copy_(W.contiguous()      )
   UpperCamelCase__ :			List[str] 						=True
   UpperCamelCase__ :			Optional[int] 						=False
   new_layer.bias.copy_(b.contiguous()      )
   UpperCamelCase__ :			Optional[int] 						=True
   return new_layer
def 	_lowerCamelCase  (		A_  :	Union[nn.Linear, ConvaD]							,  A_  :	torch.LongTensor							,  A_  :	Optional[int] = None      )			->			int:

   '''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 	_lowerCamelCase  (		A_  :	Callable[..., torch.Tensor]							,  A_  :	int							,  A_  :	int							,  *A_  :	Dict      )			->			Union[str, Any]:

   '''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
   UpperCamelCase__ :			Dict 						=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:
      UpperCamelCase__ :			Dict 						=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}'''      )

      UpperCamelCase__ :			List[str] 						=input_tensors[0].shape[chunk_dim] // chunk_size

      # chunk input tensor into tuples
      UpperCamelCase__ :			List[str] 						=tuple(input_tensor.chunk(a__							,  dim=a__      ) for input_tensor in input_tensors      )
      # apply forward fn to every tuple
      UpperCamelCase__ :			int 						=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 	_lowerCamelCase  (		A_  :	List[int]							,  A_  :	int							,  A_  :	int							,  A_  :	Set[int]      )			->			List[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=torch.ones(a__							,  a__      )
   UpperCamelCase__ :			List[str] 						=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
      UpperCamelCase__ :			int 						=head - sum(1 if h < head else 0 for h in already_pruned_heads      )
      UpperCamelCase__ :			List[str] 						=0
   UpperCamelCase__ :			Optional[Any] 						=mask.view(-1      ).contiguous().eq(1      )
   UpperCamelCase__ :			Tuple 						=torch.arange(len(a__      )      )[mask].long()
   return heads, index
def 	_lowerCamelCase  (		*A_  :	Union[torch.Tensor, List[torch.Tensor]]							,  A_  :	Optional[str] = None      )			->			str:

   '''simple docstring'''






   return torch.meshgrid(*a__							,  indexing=a__      )
def 	_lowerCamelCase  (		A_  :	torch.Tensor      )			->			List[Any]:

   '''simple docstring'''






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







'''simple docstring'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

    for k in range(t - 1     ):
        # select a column of modified matrix A':
        __lowercase     							=		r[k:, [k]]
        # construct first basis vector
        __lowercase     							=		np.zeros_like(a__     )
        __lowercase     							=		1.0
        # determine scaling factor
        __lowercase     							=		np.linalg.norm(a__     )
        # construct vector v for Householder reflection
        __lowercase     							=		x + np.sign(x[0]     ) * alpha * ea
        v /= np.linalg.norm(a__     )

        # construct the Householder matrix
        __lowercase     							=		np.eye(m - k     ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __lowercase     							=		np.block([[np.eye(a__     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




