





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__  (  UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     self      :							int    )      ->			Optional[int]:



					'''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     lowerCamelCase__  (  UpperCamelCase__      :							nn.Linear							,     UpperCamelCase__      :							torch.LongTensor							,     UpperCamelCase__      :							int = 0    )      ->			nn.Linear:



					'''simple docstring'''




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




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Union[str, Any]:
										super().__init__()
										_snake_case						=					nf
										_snake_case						=					nn.Parameter(torch.empty(lowerCAmelCase_					,    lowerCAmelCase_	)	)
										_snake_case						=					nn.Parameter(torch.zeros(lowerCAmelCase_	)	)
										nn.init.normal_(self.weight					,    std=0.02	)






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





def     lowerCamelCase__  (  UpperCamelCase__      :							ConvaD							,     UpperCamelCase__      :							torch.LongTensor							,     UpperCamelCase__      :							int = 1    )      ->			ConvaD:



					'''simple docstring'''




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





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[nn.Linear, ConvaD]							,     UpperCamelCase__      :							torch.LongTensor							,     UpperCamelCase__      :							Optional[int] = 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     lowerCamelCase__  (  UpperCamelCase__      :							Callable[..., torch.Tensor]							,     UpperCamelCase__      :							int							,     UpperCamelCase__      :							int							,     *UpperCamelCase__      :							Tuple    )      ->			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
					_snake_case						=					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:
										_snake_case						=					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}'''    )

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

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



					'''simple docstring'''




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





def     lowerCamelCase__  (  *UpperCamelCase__      :							Union[torch.Tensor, List[torch.Tensor]]							,     UpperCamelCase__      :							Optional[str] = None    )      ->			Tuple[torch.Tensor, ...]:



					'''simple docstring'''




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





def     lowerCamelCase__  (  UpperCamelCase__      :							torch.Tensor    )      ->			Tuple[torch.device, int, int]:



					'''simple docstring'''




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










def     lowerCamelCase__  (  UpperCamelCase__      :							int = 100    )      ->			int:



					'''simple docstring'''




					_snake_case						=					1
					_snake_case						=					0
					for i in range(1							,     num + 1    ):
										fact *= i

					for j in str(UpperCamelCase__    ):
										result += int(UpperCamelCase__    )

					return result


if __name__ == "__main__":
					print(solution(int(input("""Enter the Number: """).strip())))



