


"""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


__SCREAMING_SNAKE_CASE					=						[nn.LayerNorm]

__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)

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

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



def 						__a      (  a,		a,		a,		a,		self					):





       """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 						__a      (  a,		a,		a = 0					):





       """simple docstring"""







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







   """simple docstring"""




   def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:int				,  UpperCamelCase__							:List[Any]       ):
          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 SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:List[str]       ):
          _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 						__a      (  a,		a,		a = 1					):





       """simple docstring"""







       _a          =				index.to(layer.weight.device					)
       _a          =				layer.weight.index_select(a,		a					).clone().detach()
       if dim == 0:
              _a          =				layer.bias.clone().detach()
       else:
              _a          =				layer.bias[index].clone().detach()
       _a          =				list(layer.weight.size()					)
       _a          =				len(a					)
       _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 						__a      (  a,		a,		a = None					):





       """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 						__a      (  a,		a,		a,		*a					):





       """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
       _a          =				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:
              _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(a,		dim=a					) for input_tensor in input_tensors					)
              # apply forward fn to every tuple
              _a          =				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 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







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



def 						__a      (  *a,		a = None					):





       """simple docstring"""







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



def 						__a      (  a					):





       """simple docstring"""







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




"""simple docstring"""




from __future__ import annotations



def 						__a      (  a,		a					):





       """simple docstring"""







       assert len(a					) == len(a					)

       _a          =				len(a					)
       _a          =				list(range(a					)					)
       _a          =				[-1] * n  # who the donor has donated to
       _a          =				[-1] * n  # who the recipient has received from
       _a          =				[0] * n

       while unmatched_donors:
              _a          =				unmatched_donors[0]
              _a          =				donor_pref[donor]
              _a          =				donor_preference[num_donations[donor]]
              num_donations[donor] += 1
              _a          =				recipient_pref[recipient]
              _a          =				rec_record[recipient]

              if prev_donor != -1:
                     if rec_preference.index(a					) > rec_preference.index(a					):
                            _a          =				donor
                            _a          =				recipient
                            unmatched_donors.append(a					)
                            unmatched_donors.remove(a					)
              else:
                     _a          =				donor
                     _a          =				recipient
                     unmatched_donors.remove(a					)
       return donor_record
