





'''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


lowercase_         =			[nn.LayerNorm]

lowercase_         =			logging.get_logger(__name__)

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

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





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   Optional[int] ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   List[str] ,	self			:   List[str]   )						->Optional[int]:
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(__lowerCamelCase ,	__lowerCamelCase ,	parent.dim ,	self   )
    else:
        return _softmax_backward_data(__lowerCamelCase ,	__lowerCamelCase ,	parent.dim ,	self.dtype   )





def lowerCamelCase						(					__lowerCamelCase			:   nn.Linear ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   int = 0   )						->nn.Linear:
    _SCREAMING_SNAKE_CASE     =      index.to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      layer.weight.index_select(__lowerCamelCase ,	__lowerCamelCase   ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            _SCREAMING_SNAKE_CASE     =      layer.bias.clone().detach()
        else:
            _SCREAMING_SNAKE_CASE     =      layer.bias[index].clone().detach()
    _SCREAMING_SNAKE_CASE     =      list(layer.weight.size()   )
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      nn.Linear(new_size[1] ,	new_size[0] ,	bias=layer.bias is not None   ).to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.weight.copy_(W.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    if layer.bias is not None:
        _SCREAMING_SNAKE_CASE     =      False
        new_layer.bias.copy_(b.contiguous()   )
        _SCREAMING_SNAKE_CASE     =      True
    return new_layer


class  a_					(    nn.Module	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A	)		->       Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE     =      nf
        _SCREAMING_SNAKE_CASE     =      nn.Parameter(torch.empty(A			,		A	)	)
        _SCREAMING_SNAKE_CASE     =      nn.Parameter(torch.zeros(A	)	)
        nn.init.normal_(self.weight			,		std=0.02	)





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





def lowerCamelCase						(					__lowerCamelCase			:   ConvaD ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   int = 1   )						->ConvaD:
    _SCREAMING_SNAKE_CASE     =      index.to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      layer.weight.index_select(__lowerCamelCase ,	__lowerCamelCase   ).clone().detach()
    if dim == 0:
        _SCREAMING_SNAKE_CASE     =      layer.bias.clone().detach()
    else:
        _SCREAMING_SNAKE_CASE     =      layer.bias[index].clone().detach()
    _SCREAMING_SNAKE_CASE     =      list(layer.weight.size()   )
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      ConvaD(new_size[1] ,	new_size[0]   ).to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.weight.copy_(W.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.bias.copy_(b.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    return new_layer





def lowerCamelCase						(					__lowerCamelCase			:   Union[nn.Linear, ConvaD] ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   Optional[int] = None   )						->Union[nn.Linear, ConvaD]:
    if isinstance(__lowerCamelCase ,	nn.Linear   ):
        return prune_linear_layer(__lowerCamelCase ,	__lowerCamelCase ,	dim=0 if dim is None else dim   )
    elif isinstance(__lowerCamelCase ,	__lowerCamelCase   ):
        return prune_convad_layer(__lowerCamelCase ,	__lowerCamelCase ,	dim=1 if dim is None else dim   )
    else:
        raise ValueError(F'Can\'t prune layer of class {layer.__class__}'   )





def lowerCamelCase						(					__lowerCamelCase			:   Callable[..., torch.Tensor] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	*__lowerCamelCase			:   Any   )						->torch.Tensor:
    assert len(__lowerCamelCase   ) > 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     =      len(inspect.signature(__lowerCamelCase   ).parameters   )
    if num_args_in_forward_chunk_fn != len(__lowerCamelCase   ):
        raise ValueError(
            F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowerCamelCase   )} input '
            """tensors are given"""   )

    if chunk_size > 0:
        _SCREAMING_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}'   )

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

        # chunk input tensor into tuples
        _SCREAMING_SNAKE_CASE     =      tuple(input_tensor.chunk(__lowerCamelCase ,	dim=__lowerCamelCase   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        _SCREAMING_SNAKE_CASE     =      tuple(forward_fn(*__lowerCamelCase   ) for input_tensors_chunk in zip(*__lowerCamelCase   )   )
        # concatenate output at same dimension
        return torch.cat(__lowerCamelCase ,	dim=__lowerCamelCase   )

    return forward_fn(*__lowerCamelCase   )





def lowerCamelCase						(					__lowerCamelCase			:   List[int] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Set[int]   )						->Tuple[Set[int], torch.LongTensor]:
    _SCREAMING_SNAKE_CASE     =      torch.ones(__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      set(__lowerCamelCase   ) - 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     =      head - sum(1 if h < head else 0 for h in already_pruned_heads   )
        _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      mask.view(-1   ).contiguous().eq(1   )
    _SCREAMING_SNAKE_CASE     =      torch.arange(len(__lowerCamelCase   )   )[mask].long()
    return heads, index





def lowerCamelCase						(					*__lowerCamelCase			:   Union[torch.Tensor, List[torch.Tensor]] ,	__lowerCamelCase			:   Optional[str] = None   )						->Tuple[torch.Tensor, ...]:
    return torch.meshgrid(*__lowerCamelCase ,	indexing=__lowerCamelCase   )






def lowerCamelCase						(					__lowerCamelCase			:   torch.Tensor   )						->Tuple[torch.device, int, int]:
    return tensor.device, storage_ptr(__lowerCamelCase   ), storage_size(__lowerCamelCase   )










'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher





def lowerCamelCase						(					)						->None:
    _SCREAMING_SNAKE_CASE     =      """Prehistoric Men.txt"""
    _SCREAMING_SNAKE_CASE     =      """Output.txt"""
    _SCREAMING_SNAKE_CASE     =      int(input("""Enter key: """   )   )
    _SCREAMING_SNAKE_CASE     =      input("""Encrypt/Decrypt [e/d]: """   )

    if not os.path.exists(__lowerCamelCase   ):
        print(F'File {input_file} does not exist. Quitting...'   )
        sys.exit()
    if os.path.exists(__lowerCamelCase   ):
        print(F'Overwrite {output_file}? [y/n]'   )
        _SCREAMING_SNAKE_CASE     =      input("""> """   )
        if not response.lower().startswith("""y"""   ):
            sys.exit()

    _SCREAMING_SNAKE_CASE     =      time.time()
    if mode.lower().startswith("""e"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.encrypt_message(__lowerCamelCase ,	__lowerCamelCase   )
    elif mode.lower().startswith("""d"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.decrypt_message(__lowerCamelCase ,	__lowerCamelCase   )

    with open(__lowerCamelCase ,	"""w"""   ) as output_obj:
        output_obj.write(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      round(time.time() - start_time ,	2   )
    print(("""Done (""", total_time, """seconds )""")   )


if __name__ == "__main__":
      main()



