
"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str   =     field(
						    metadata={'''help''': '''The output directory where the model will be written.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    }		,       )
						_lowerCamelCase:     str   =     field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    }		,       )
						_lowerCamelCase:     Optional[str]   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}     )
						_lowerCamelCase:     Optional[str]   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}     )






def    _snake_case					(		):
	A     = HfArgumentParser((ModelArguments,)  )
	((A) ,   )     = parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

	# Use explicit specified encoder config
	if model_args.encoder_config_name:
		A     = AutoConfig.from_pretrained(model_args.encoder_config_name  )
	# Use pretrained encoder model's config
	else:
		A     = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

	# Use explicit specified decoder config
	if model_args.decoder_config_name:
		A     = AutoConfig.from_pretrained(model_args.decoder_config_name  )
	# Use pretrained decoder model's config
	else:
		A     = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

	# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
	A     = True
	A     = True

	A     = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
	    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,   decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,   encoder_config=snake_case__						,   decoder_config=snake_case__						,   )

	# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
	A     = decoder_config.decoder_start_token_id
	A     = decoder_config.pad_token_id
	if decoder_start_token_id is None:
		A     = decoder_config.bos_token_id
	if pad_token_id is None:
		A     = decoder_config.eos_token_id

	# This is necessary to make Flax's generate() work
	A     = decoder_config.eos_token_id
	A     = decoder_start_token_id
	A     = pad_token_id

	A     = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

	A     = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
	A     = tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

	model.save_pretrained(model_args.output_dir  )
	image_processor.save_pretrained(model_args.output_dir  )
	tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
		main()

"""simple docstring"""


from __future__ import annotations






def    _snake_case					(		snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):
	A ,   A     = position
	A     = [
	    (y + 1, x + 2),
	    (y - 1, x + 2),
	    (y + 1, x - 2),
	    (y - 1, x - 2),
	    (y + 2, x + 1),
	    (y + 2, x - 1),
	    (y - 2, x + 1),
	    (y - 2, x - 1),
	]
	A     = []

	for position in positions:
		A ,   A     = position
		if 0 <= y_test < n and 0 <= x_test < n:
			permissible_positions.append(snake_case__  )

	return permissible_positions






def    _snake_case					(		snake_case__ :  list[list[int]]  ):
	return not any(elem == 0 for row in board for elem in row  )






def    _snake_case					(		snake_case__ :  list[list[int]]						,   snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):

	if is_complete(snake_case__  ):
		return True

	for position in get_valid_pos(snake_case__						,   len(snake_case__  )  ):
		A ,   A     = position

		if board[y][x] == 0:
			A     = curr + 1
			if open_knight_tour_helper(snake_case__						,   snake_case__						,   curr + 1  ):
				return True
			A     = 0

	return False






def    _snake_case					(		snake_case__ :  int  ):
	A     = [[0 for i in range(snake_case__  )] for j in range(snake_case__  )]

	for i in range(snake_case__  ):
		for j in range(snake_case__  ):
			A     = 1
			if open_knight_tour_helper(snake_case__						,   (i, j)						,   1  ):
				return board
			A     = 0

	A     = F'Open Kight Tour cannot be performed on a board of size {n}'
	raise ValueError(snake_case__  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()