






"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional

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



@dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






def       __UpperCamelCase						(		):
	A_		:    Any						     = HfArgumentParser((ModelArguments,)       )
	((A_)     ,  )		:    List[Any]						     = parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

	# Use explicit specified encoder config
	if model_args.encoder_config_name:
		A_		:    int						     = AutoConfig.from_pretrained(model_args.encoder_config_name       )
	# Use pretrained encoder model's config
	else:
		A_		:    Union[str, Any]						     = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path       )

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

	# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
	A_		:    List[str]						     = True
	A_		:    Union[str, Any]						     = True

	A_		:    List[Any]						     = 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_		:    str						     = decoder_config.decoder_start_token_id
	A_		:    List[str]						     = decoder_config.pad_token_id
	if decoder_start_token_id is None:
		A_		:    List[str]						     = decoder_config.bos_token_id
	if pad_token_id is None:
		A_		:    List[Any]						     = decoder_config.eos_token_id

	# This is necessary to make Flax's generate() work
	A_		:    int						     = decoder_config.eos_token_id
	A_		:    Optional[Any]						     = decoder_start_token_id
	A_		:    List[str]						     = pad_token_id

	A_		:    List[Any]						     = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

	A_		:    List[Any]						     = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
	A_		:    Optional[Any]						     = 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"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

