"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class   UpperCAmelCase_			:
      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        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			HfArgumentParser((ModelArguments,)  )
       (lowercase__   )    :	List[str]     					=			parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

       # Use explicit specified encoder config
       if model_args.encoder_config_name:
              lowercase__    :	Dict     					=			AutoConfig.from_pretrained(model_args.encoder_config_name  )
       # Use pretrained encoder model's config
       else:
              lowercase__    :	Any     					=			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

       # Use explicit specified decoder config
       if model_args.decoder_config_name:
              lowercase__    :	str     					=			AutoConfig.from_pretrained(model_args.decoder_config_name  )
       # Use pretrained decoder model's config
       else:
              lowercase__    :	Tuple     					=			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

       # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
       lowercase__    :	Optional[int]     					=			True
       lowercase__    :	Optional[int]     					=			True

       lowercase__    :	Tuple     					=			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
       lowercase__    :	Optional[int]     					=			decoder_config.decoder_start_token_id
       lowercase__    :	str     					=			decoder_config.pad_token_id
       if decoder_start_token_id is None:
              lowercase__    :	Dict     					=			decoder_config.bos_token_id
       if pad_token_id is None:
              lowercase__    :	Tuple     					=			decoder_config.eos_token_id

       # This is necessary to make Flax's generate() work
       lowercase__    :	int     					=			decoder_config.eos_token_id
       lowercase__    :	List[str]     					=			decoder_start_token_id
       lowercase__    :	Any     					=			pad_token_id

       lowercase__    :	List[str]     					=			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

       lowercase__    :	Tuple     					=			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
       lowercase__    :	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()


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

       _UpperCamelCase     :		List[str]											=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

