

'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional

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



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






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


def 						__lowerCamelCase	(       )	->							Dict:






						"""simple docstring"""





						A__					:       int     =HfArgumentParser((ModelArguments,)	)
						((A__)   ,						)					:       List[str]     =parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

						# Use explicit specified encoder config
						if model_args.encoder_config_name:
												A__					:       List[Any]     =AutoConfig.from_pretrained(model_args.encoder_config_name	)
						# Use pretrained encoder model's config
						else:
												A__					:       Optional[int]     =AutoConfig.from_pretrained(model_args.encoder_model_name_or_path	)

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

						# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
						A__					:       Tuple     =True
						A__					:       int     =True

						A__					:       List[str]     =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__					:       Optional[Any]     =decoder_config.decoder_start_token_id
						A__					:       Tuple     =decoder_config.pad_token_id
						if decoder_start_token_id is None:
												A__					:       List[Any]     =decoder_config.bos_token_id
						if pad_token_id is None:
												A__					:       Union[str, Any]     =decoder_config.eos_token_id

						# This is necessary to make Flax's generate() work
						A__					:       str     =decoder_config.eos_token_id
						A__					:       Dict     =decoder_start_token_id
						A__					:       str     =pad_token_id

						A__					:       Union[str, Any]     =AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path	)

						A__					:       Dict     =AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path	)
						A__					:       str     =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 maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 						__lowerCamelCase	(       __snake_case					:		int	)	->							int:






						"""simple docstring"""





						A__					:       List[Any]     =prime_factors(__snake_case	)
						if is_square_free(__snake_case	):
												return -1 if len(__snake_case	) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

