



from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class   __lowerCAmelCase  :
					"""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=UpperCAmelCase_				,  metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}   )
					A__					:		Optional[str]      							=     field(
					    default=UpperCAmelCase_				,  metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}   )







def A    (							)    ->   Dict:
			A__	  =     HfArgumentParser((ModelArguments,)       )
			((A__)  ,			)	  =     parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

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

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

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

			A__	  =     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=__UpperCamelCase			,						decoder_config=__UpperCamelCase			,						)

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			A__	  =     decoder_config.decoder_start_token_id
			A__	  =     decoder_config.pad_token_id
			if decoder_start_token_id is None:
						A__	  =     decoder_config.bos_token_id
			if pad_token_id is None:
						A__	  =     decoder_config.eos_token_id

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

			A__	  =     AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

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









import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       *_snake_case       :							int					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								warnings.warn(
								    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use CLIPImageProcessor instead.'					,       _snake_case					,       )
								super().__init__(*_snake_case					,       **_snake_case       )




