from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[Any]        =		len(SCREAMING_SNAKE_CASE     )

    # If row is equal to the size of the board it means there are a queen in each row in
    # the current board (possible_board)
    if row == n:
        # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
        # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
        boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]     )
        return

    # We iterate each column in the row to find all possible results in each row
    for col in range(SCREAMING_SNAKE_CASE     ):
        # We apply that we learned previously. First we check that in the current board
        # (possible_board) there are not other same value because if there is it means
        # that there are a collision in vertical. Then we apply the two formulas we
        # learned before:
        #
        # 45º: y - x = b or 45: row - col = b
        # 135º: y + x = b or row + col = b.
        #
        # And we verify if the results of this two formulas not exist in their variables
        # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
        #
        # If any or these are True it means there is a collision so we continue to the
        # next value in the for loop.
        if (
            col in possible_board
            or row - col in diagonal_right_collisions
            or row + col in diagonal_left_collisions
        ):
            continue

        # If it is False we call dfs function again and we update the inputs
        depth_first_search(
            [*possible_board, col]  ,      [*diagonal_right_collisions, row - col]  ,      [*diagonal_left_collisions, row + col]  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:list[list[str]]        =		[]
    depth_first_search([]  ,      []  ,      []  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    # Print all the boards
    for board in boards:
        for column in board:
            print(SCREAMING_SNAKE_CASE     )
        print(''''''     )

    print(len(SCREAMING_SNAKE_CASE     )  ,      '''solutions were found.'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    n_queens_solution(4)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      encoder_layers=args.encoder_layers  ,      decoder_layers=args.decoder_layers  ,      encoder_attention_heads=args.encoder_attention_heads  ,      decoder_attention_heads=args.decoder_attention_heads  ,      encoder_ffn_dim=args.encoder_ffn_embed_dim  ,      decoder_ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.encoder_embed_dim  ,      encoder_layerdrop=args.encoder_layerdrop  ,      decoder_layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''relu'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





