





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   [
						    '''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(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   list(s_dict.keys()					)
						for key in keys:
												if "transformer_layers" in key:
																		__SCREAMING_SNAKE_CASE:				Any           =   s_dict.pop(UpperCamelCase__					)
												elif "subsample" in key:
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   s_dict.pop(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				Dict           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   Any					)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   mam_aaa['''args''']
						__SCREAMING_SNAKE_CASE:				Tuple           =   mam_aaa['''model''']
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   state_dict['''decoder.output_projection.weight''']

						remove_ignore_keys_(UpperCamelCase__					)
						rename_keys(UpperCamelCase__					)

						__SCREAMING_SNAKE_CASE:				str           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[str]           =   args.share_decoder_input_output_embed

						__SCREAMING_SNAKE_CASE:				Dict           =   [int(UpperCamelCase__					) for i in args.conv_kernel_sizes.split(''','''					)]
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   SpeechaTextConfig(
						    vocab_size=UpperCamelCase__       ,			max_source_positions=args.max_source_positions       ,			max_target_positions=args.max_target_positions       ,			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       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''relu'''       ,			num_conv_layers=len(UpperCamelCase__					)       ,			conv_channels=args.conv_channels       ,			conv_kernel_sizes=UpperCamelCase__       ,			input_feat_per_channel=args.input_feat_per_channel       ,			input_channels=args.input_channels       ,			tie_word_embeddings=UpperCamelCase__       ,			num_beams=5       ,			max_length=200       ,			use_cache=UpperCamelCase__       ,			decoder_start_token_id=2       ,			early_stopping=UpperCamelCase__       ,			)

						__SCREAMING_SNAKE_CASE:				Optional[int]           =   SpeechaTextForConditionalGeneration(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   model.model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						if len(UpperCamelCase__					) > 0 and not set(UpperCamelCase__					) <= {
						    "encoder.embed_positions.weights",
						    "decoder.embed_positions.weights",
						}:
												raise ValueError(
												    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
												    F""" but all the following weights are missing {missing}"""					)

						if tie_embeds:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   make_linear_from_emb(model.model.decoder.embed_tokens					)
						else:
												__SCREAMING_SNAKE_CASE:				Dict           =   lm_head_weights

						model.save_pretrained(UpperCamelCase__					)


if __name__ == "__main__":
							lowerCAmelCase							:       str					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       str					 =	parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)










from __future__ import annotations






def 		lowerCAmelCase  (	UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[list[str]]       ,			UpperCamelCase__   :   int       ,			)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   len(UpperCamelCase__					)

						# 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(UpperCamelCase__					):
												# 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]       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						depth_first_search([]       ,			[]       ,			[]       ,			UpperCamelCase__       ,			UpperCamelCase__					)

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							n_queens_solution(4)



