



from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class 					__lowerCAmelCase :
				__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=lowerCamelCase__       ,						metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}       )
				__lowerCamelCase =				field(
				    default=lowerCamelCase__       ,						metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}       )


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    HfArgumentParser((ModelArguments,)			)
					((_lowerCAmelCase)	,	)						=    parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

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

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

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

					_lowerCAmelCase						=    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
					_lowerCAmelCase						=    decoder_config.decoder_start_token_id
					_lowerCAmelCase						=    decoder_config.pad_token_id
					if decoder_start_token_id is None:
										_lowerCAmelCase						=    decoder_config.bos_token_id
					if pad_token_id is None:
										_lowerCAmelCase						=    decoder_config.eos_token_id

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

					_lowerCAmelCase						=    AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path			)

					_lowerCAmelCase						=    AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path			)
					_lowerCAmelCase						=    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()





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



