

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


lowerCAmelCase       :				Any         =					[nn.LayerNorm]

lowerCAmelCase       :				Dict         =					logging.get_logger(__name__)

lowerCAmelCase       :				Tuple         =					version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase       :				List[str]         =					parsed_torch_version_base >= version.parse('2.0')
lowerCAmelCase       :				List[str]         =					parsed_torch_version_base >= version.parse('1.12')
lowerCAmelCase       :				Optional[int]         =					parsed_torch_version_base >= version.parse('1.11')
lowerCAmelCase       :				str         =					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(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE   ,				parent.dim   ,				self					)
      else:
            return _softmax_backward_data(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE   ,				parent.dim   ,				self.dtype					)

def 				A_					(							a   ,				a   ,				a = 0					):

      """simple docstring"""



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

  def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







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



  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







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

def 				A_					(							a   ,				a   ,				a = 1					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       index.to(layer.weight.device					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       layer.weight.index_select(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE					).clone().detach()
      if dim == 0:
            SCREAMING_SNAKE_CASE_			:					Tuple          =       layer.bias.clone().detach()
      else:
            SCREAMING_SNAKE_CASE_			:					str          =       layer.bias[index].clone().detach()
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       list(layer.weight.size()					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       len(_SCREAMING_SNAKE_CASE					)
      SCREAMING_SNAKE_CASE_			:					List[str]          =       ConvaD(new_size[1]   ,				new_size[0]					).to(layer.weight.device					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       False
      new_layer.weight.copy_(W.contiguous()					)
      SCREAMING_SNAKE_CASE_			:					Dict          =       True
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       False
      new_layer.bias.copy_(b.contiguous()					)
      SCREAMING_SNAKE_CASE_			:					Any          =       True
      return new_layer

def 				A_					(							a   ,				a   ,				a = None					):

      """simple docstring"""



      if isinstance(_SCREAMING_SNAKE_CASE   ,				nn.Linear					):
            return prune_linear_layer(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE   ,				dim=0 if dim is None else dim					)
      elif isinstance(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE					):
            return prune_convad_layer(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE   ,				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(_SCREAMING_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
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       len(inspect.signature(_SCREAMING_SNAKE_CASE					).parameters					)
      if num_args_in_forward_chunk_fn != len(_SCREAMING_SNAKE_CASE					):
            raise ValueError(
                f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_SCREAMING_SNAKE_CASE					)} input "
                'tensors are given'					)

      if chunk_size > 0:
            SCREAMING_SNAKE_CASE_			:					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}"					)

            SCREAMING_SNAKE_CASE_			:					Dict          =       input_tensors[0].shape[chunk_dim] // chunk_size

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

      return forward_fn(*_SCREAMING_SNAKE_CASE					)

def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       torch.ones(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE					)
      SCREAMING_SNAKE_CASE_			:					int          =       set(_SCREAMING_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
            SCREAMING_SNAKE_CASE_			:					str          =       head - sum(1 if h < head else 0 for h in already_pruned_heads					)
            SCREAMING_SNAKE_CASE_			:					Dict          =       0
      SCREAMING_SNAKE_CASE_			:					Dict          =       mask.view(-1					).contiguous().eq(1					)
      SCREAMING_SNAKE_CASE_			:					torch.LongTensor          =       torch.arange(len(_SCREAMING_SNAKE_CASE					)					)[mask].long()
      return heads, index

def 				A_					(							*a   ,				a = None					):

      """simple docstring"""



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



def 				A_					(							a					):

      """simple docstring"""



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





from math import factorial

def 				A_					(							a   ,				a					):

      """simple docstring"""



      return factorial(a					) / (factorial(a					) * factorial(n - r					))

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       0

      for i in range(1   ,				1_0_1					):
            for j in range(1   ,				i + 1					):
                  if combinations(a   ,				a					) > 1e6:
                        total += 1
      return total


if __name__ == "__main__":
    print(solution())






