



from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class 				_a						:





							'''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=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}			)






def     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							int      						= HfArgumentParser((ModelArguments,) )
				((a__)						,   )	:							List[str]      						= parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

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

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

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

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

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

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

				a__	:							Optional[Any]      						= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
				a__	:							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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		       =      {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""BlipImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlipModel""",
						    """BlipPreTrainedModel""",
						    """BlipForConditionalGeneration""",
						    """BlipForQuestionAnswering""",
						    """BlipVisionModel""",
						    """BlipTextModel""",
						    """BlipForImageTextRetrieval""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFBlipModel""",
						    """TFBlipPreTrainedModel""",
						    """TFBlipForConditionalGeneration""",
						    """TFBlipForQuestionAnswering""",
						    """TFBlipVisionModel""",
						    """TFBlipTextModel""",
						    """TFBlipForImageTextRetrieval""",
						]

if TYPE_CHECKING:
						from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
						from .processing_blip import BlipProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_blip import BlipImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blip import (
												    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlipForConditionalGeneration,
												    BlipForImageTextRetrieval,
												    BlipForQuestionAnswering,
												    BlipModel,
												    BlipPreTrainedModel,
												    BlipTextModel,
												    BlipVisionModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blip import (
												    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBlipForConditionalGeneration,
												    TFBlipForImageTextRetrieval,
												    TFBlipForQuestionAnswering,
												    TFBlipModel,
												    TFBlipPreTrainedModel,
												    TFBlipTextModel,
												    TFBlipVisionModel,
												)

else:
						import sys

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


