
"""simple docstring"""



from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(
           metadata={"help": "The output directory where the model will be written."}			,						)
       __A			:      Dict        =   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			:      List[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}     )
       __A			:      Tuple        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}     )







def       lowerCamelCase					()     ->							List[str]:
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments,))
       ((lowercase			)							,  			)		:Optional[Any]       							=				parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

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

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

       # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
       lowercase		:Tuple       							=				True
       lowercase		:List[Any]       							=				True

       lowercase		: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=__lowerCAmelCase				,				decoder_config=__lowerCAmelCase				,				)

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

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

       lowercase		:Dict       							=				AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

       lowercase		:Tuple       							=				AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
       lowercase		:List[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"""



import math






def       lowerCamelCase					(a_						:int)     ->							bool:
       assert isinstance(a_				,				a_) and (
           number >= 0
       ), "'number' must been an int and positive"

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or not number % 2:
              # Negatives, 0, 1 and all even numbers are not primes
              return False

       lowercase		:Any       							=				range(3				,				int(math.sqrt(a_) + 1)				,				2)
       return not any(not number % i for i in odd_numbers)






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:Optional[int]=1				,				**a_						:List[str])     ->							Any:
       lowercase		:str       							=				factor * value
       lowercase		:int       							=				value

       while not is_prime(a_):
              value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

       if value == first_value_val:
              return next_prime(value + 1				,				**a_)
       return value

