





from dataclasses import dataclass, field
from typing import Optional

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



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






def 	lowerCamelCase_		(							)						->		List[str]:






				"""simple docstring"""


				snake_case_		:       int           =    HfArgumentParser((ModelArguments,)					)
				(snake_case_   )		:       List[str]           =    parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

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

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

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

				snake_case_		:       str           =    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
				snake_case_		:       Dict           =    decoder_config.decoder_start_token_id
				snake_case_		:       List[str]           =    decoder_config.pad_token_id
				if decoder_start_token_id is None:
								snake_case_		:       Optional[int]           =    decoder_config.bos_token_id
				if pad_token_id is None:
								snake_case_		:       Optional[Any]           =    decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				snake_case_		:       str           =    decoder_config.eos_token_id
				snake_case_		:       Union[str, Any]           =    decoder_start_token_id
				snake_case_		:       Dict           =    pad_token_id

				snake_case_		:       Optional[Any]           =    AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

				snake_case_		:       Optional[Any]           =    AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
				snake_case_		:       Optional[int]           =    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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


