



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

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 				_UpperCAmelCase				:





     '''simple docstring'''



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





def 					lowerCamelCase_		():
    _UpperCAmelCase				:    str											=       HfArgumentParser((ModelArguments,)				)
    ((_UpperCAmelCase   )				,							   )				:    Union[str, Any]											=       parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

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

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

    # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
    _UpperCAmelCase				:    Tuple											=       True
    _UpperCAmelCase				:    Dict											=       True

    _UpperCAmelCase				:    Optional[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
    _UpperCAmelCase				:    Optional[int]											=       decoder_config.decoder_start_token_id
    _UpperCAmelCase				:    Dict											=       decoder_config.pad_token_id
    if decoder_start_token_id is None:
        _UpperCAmelCase				:    Dict											=       decoder_config.bos_token_id
    if pad_token_id is None:
        _UpperCAmelCase				:    Optional[int]											=       decoder_config.eos_token_id

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

    _UpperCAmelCase				:    Any											=       AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

    _UpperCAmelCase				:    str											=       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
    _UpperCAmelCase				:    List[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 typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset