

from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







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

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       HfArgumentParser((ModelArguments,))
	((SCREAMING_SNAKE_CASE)     ,)   :      str				     =       parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

	# Use explicit specified encoder config
	if model_args.encoder_config_name:
		SCREAMING_SNAKE_CASE   :      Dict				     =       AutoConfig.from_pretrained(model_args.encoder_config_name)
	# Use pretrained encoder model's config
	else:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

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

	# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
	SCREAMING_SNAKE_CASE   :      int				     =       True
	SCREAMING_SNAKE_CASE   :      str				     =       True

	SCREAMING_SNAKE_CASE   :      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=_a							,			decoder_config=_a							,			)

	# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       decoder_config.decoder_start_token_id
	SCREAMING_SNAKE_CASE   :      Dict				     =       decoder_config.pad_token_id
	if decoder_start_token_id is None:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       decoder_config.bos_token_id
	if pad_token_id is None:
		SCREAMING_SNAKE_CASE   :      int				     =       decoder_config.eos_token_id

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

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

	SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
	SCREAMING_SNAKE_CASE   :      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()


class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return index | (index + 1)
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return (index & (index + 1)) - 1
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       value
				while index < self.size:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_prev(a	) + 1
					if current_left_border == index:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       max(a      ,      a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_next(a	)






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int      ,      a							: int	)							->       int:

				"""simple docstring"""

				right -= 1  # Because of right is exclusive
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0
				while left <= right:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_prev(a	)
					if left <= current_left:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      self.tree[right]	)
						SCREAMING_SNAKE_CASE   :      str				     =       current_left
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       max(a      ,      self.arr[right]	)
						right -= 1
				return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()