


'''simple docstring'''






from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class        a   :
 _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=snake_case_     , metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}				)
 _lowerCAmelCase           =		field(
     default=snake_case_     , metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}				)






def 		_A ()      ->     List[Any]:




     '''simple docstring'''



     _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=lowerCamelCase__ ,  decoder_config=lowerCamelCase__ ,  )

     # 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()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


