





"""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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						self					:						str      ):







      '''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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						nn.Linear							,						SCREAMING_SNAKE_CASE					:						torch.LongTensor							,						SCREAMING_SNAKE_CASE					:						int = 0      ):







      '''simple docstring'''




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

class 		lowercase ( nn.Module ):





      def __init__(	self				,						lowercase				,						lowercase			)		->					str:
            super().__init__()
            lowerCAmelCase  						=	nf
            lowerCAmelCase  						=	nn.Parameter(torch.empty(lowercase				,						lowercase			)			)
            lowerCAmelCase  						=	nn.Parameter(torch.zeros(lowercase			)			)
            nn.init.normal_(self.weight				,						std=0.02			)





      def    _snake_case      (	self				,						lowercase			)		->					Any:
            lowerCAmelCase  						=	x.size()[:-1] + (self.nf,)
            lowerCAmelCase  						=	torch.addmm(self.bias				,						x.view(-1				,						x.size(-1			)			)				,						self.weight			)
            lowerCAmelCase  						=	x.view(lowercase			)
            return x







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						ConvaD							,						SCREAMING_SNAKE_CASE					:						torch.LongTensor							,						SCREAMING_SNAKE_CASE					:						int = 1      ):







      '''simple docstring'''




      lowerCAmelCase  						=	index.to(layer.weight.device      )
      lowerCAmelCase  						=	layer.weight.index_select(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ).clone().detach()
      if dim == 0:
            lowerCAmelCase  						=	layer.bias.clone().detach()
      else:
            lowerCAmelCase  						=	layer.bias[index].clone().detach()
      lowerCAmelCase  						=	list(layer.weight.size()      )
      lowerCAmelCase  						=	len(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	ConvaD(new_size[1]							,						new_size[0]      ).to(layer.weight.device      )
      lowerCAmelCase  						=	False
      new_layer.weight.copy_(W.contiguous()      )
      lowerCAmelCase  						=	True
      lowerCAmelCase  						=	False
      new_layer.bias.copy_(b.contiguous()      )
      lowerCAmelCase  						=	True
      return new_layer


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Union[nn.Linear, ConvaD]							,						SCREAMING_SNAKE_CASE					:						torch.LongTensor							,						SCREAMING_SNAKE_CASE					:						Optional[int] = 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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Callable[..., torch.Tensor]							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int							,						*SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):







      '''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
      lowerCAmelCase  						=	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:
            lowerCAmelCase  						=	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}'      )

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

            # chunk input tensor into tuples
            lowerCAmelCase  						=	tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE							,						dim=SCREAMING_SNAKE_CASE      ) for input_tensor in input_tensors      )
            # apply forward fn to every tuple
            lowerCAmelCase  						=	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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[int]							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						Set[int]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	torch.ones(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	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
            lowerCAmelCase  						=	head - sum(1 if h < head else 0 for h in already_pruned_heads      )
            lowerCAmelCase  						=	0
      lowerCAmelCase  						=	mask.view(-1      ).contiguous().eq(1      )
      lowerCAmelCase  						=	torch.arange(len(SCREAMING_SNAKE_CASE      )      )[mask].long()
      return heads, index


def       UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE					:						Union[torch.Tensor, List[torch.Tensor]]							,						SCREAMING_SNAKE_CASE					:						Optional[str] = None      ):







      '''simple docstring'''




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






def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						torch.Tensor      ):







      '''simple docstring'''




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













"""simple docstring"""





def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	1.0
      for i in range(1							,						r + 1      ):
            ret *= (n + 1 - i) / i
      return round(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return choose(8 + n							,						SCREAMING_SNAKE_CASE      ) + choose(9 + n							,						SCREAMING_SNAKE_CASE      ) - 10


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return sum(non_bouncy_exact(SCREAMING_SNAKE_CASE      ) for i in range(1							,						n + 1      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int = 1_00      ):







      '''simple docstring'''




      return non_bouncy_upto(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       print(f'{solution() = }')






