"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class        _snake_case   :
						snake_case__	     =	field(
						    metadata={"help": "The output directory where the model will be written."} ,       )
						snake_case__	     =	field(
						    metadata={
						        "help": (
						            "The encoder model checkpoint for weights initialization."
						            "Don't set if you want to train an encoder model from scratch."
						        )
						    } ,       )
						snake_case__	     =	field(
						    metadata={
						        "help": (
						            "The decoder model checkpoint for weights initialization."
						            "Don't set if you want to train a decoder model from scratch."
						        )
						    } ,       )
						snake_case__	     =	field(
						    default=_UpperCamelCase ,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
						snake_case__	     =	field(
						    default=_UpperCamelCase ,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)


def       lowercase_      (      )       ->					Optional[int]:



							'''simple docstring'''

							__lowerCamelCase    :						Optional[Any]						      =   HfArgumentParser((ModelArguments,)					)
							((__lowerCamelCase		)      ,     		)    :						Optional[Any]						      =   parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							__lowerCamelCase    :						str						      =   True
							__lowerCamelCase    :						Optional[Any]						      =   True

							__lowerCamelCase    :						int						      =   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
							__lowerCamelCase    :						Tuple						      =   decoder_config.decoder_start_token_id
							__lowerCamelCase    :						Union[str, Any]						      =   decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__lowerCamelCase    :						Dict						      =   decoder_config.bos_token_id
							if pad_token_id is None:
														__lowerCamelCase    :						List[Any]						      =   decoder_config.eos_token_id

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

							__lowerCamelCase    :						List[Any]						      =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

							__lowerCamelCase    :						Dict						      =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
							__lowerCamelCase    :						str						      =   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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






