


"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional

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



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







def  __snake_case       (   )      ->		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=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()









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase						:				Any             =      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				int             =      ["LxmertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "LxmertEncoder",
	    "LxmertForPreTraining",
	    "LxmertForQuestionAnswering",
	    "LxmertModel",
	    "LxmertPreTrainedModel",
	    "LxmertVisualFeatureEncoder",
	    "LxmertXLayer",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFLxmertForPreTraining",
	    "TFLxmertMainLayer",
	    "TFLxmertModel",
	    "TFLxmertPreTrainedModel",
	    "TFLxmertVisualFeatureEncoder",
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





