"""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			(__UpperCamelCase							:    Dict							,					__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    int							,					self							:    List[Any]			):
							"""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							:    List[str]							,					__UpperCamelCase							:    Dict							,					__UpperCamelCase							:    str = 0			):
							"""simple docstring"""


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






class 		_lowercase (						nn.Module      ):



						"""simple docstring"""




						def __init__(				self							:    Optional[Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    str				)				->				Optional[int]:
													'''simple docstring'''



													super().__init__()
													__UpperCamelCase							=nf
													__UpperCamelCase							=nn.Parameter(torch.empty(__A     , __A				)				)
													__UpperCamelCase							=nn.Parameter(torch.zeros(__A				)				)
													nn.init.normal_(self.weight     , std=0.02				)





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    str				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=x.size()[:-1] + (self.nf,)
													__UpperCamelCase							=torch.addmm(self.bias     , x.view(-1     , x.size(-1				)				)     , self.weight				)
													__UpperCamelCase							=x.view(__A				)
													return x






def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    Any = 1			):
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    Tuple							,					__UpperCamelCase							:    Any = None			):
							"""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							:    str							,					__UpperCamelCase							:    Any							,					__UpperCamelCase							:    Optional[Any]							,					*__UpperCamelCase							:    int			):
							"""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
							__UpperCamelCase							=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:
														__UpperCamelCase							=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}"""			)

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

														# chunk input tensor into tuples
														__UpperCamelCase							=tuple(input_tensor.chunk(__UpperCamelCase							,					dim=__UpperCamelCase			) for input_tensor in input_tensors			)
														# apply forward fn to every tuple
														__UpperCamelCase							=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							:    Optional[Any]							,					__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    Optional[int]			):
							"""simple docstring"""


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


def    lowerCAmelCase			(*__UpperCamelCase							:    Union[str, Any]							,					__UpperCamelCase							:    str = None			):
							"""simple docstring"""


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






def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


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







"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

    a											={char for login in split_logins for char in login}

    for permutation in itertools.permutations(lowercase						):
        a											=True
        for login in logins:
            if not (
                permutation.index(login[0]						)
                < permutation.index(login[1]						)
                < permutation.index(login[2]						)
            ):
                a											=False
                break

        if satisfied:
            return int(''''''.join(lowercase						)						)

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


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