






"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_   : Tuple   			= {
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Any   			= ["""MobileNetV2FeatureExtractor"""]
  UpperCAmelCase_   : str   			= ["""MobileNetV2ImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Tuple   			= [
      """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MobileNetV2ForImageClassification""",
      """MobileNetV2ForSemanticSegmentation""",
      """MobileNetV2Model""",
      """MobileNetV2PreTrainedModel""",
      """load_tf_weights_in_mobilenet_v2""",
  ]


if TYPE_CHECKING:
  from .configuration_mobilenet_va import (
      MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileNetVaConfig,
      MobileNetVaOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
    from .image_processing_mobilenet_va import MobileNetVaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilenet_va import (
        MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileNetVaForImageClassification,
        MobileNetVaForSemanticSegmentation,
        MobileNetVaModel,
        MobileNetVaPreTrainedModel,
        load_tf_weights_in_mobilenet_va,
    )


else:
  import sys

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





"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

		# Load weights from numpy
		load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		# Save pytorch-model
		__lowerCAmelCase:       str          =			pytorch_dump_folder_path + "/" + WEIGHTS_NAME
		__lowerCAmelCase:       List[Any]          =			pytorch_dump_folder_path + "/" + CONFIG_NAME
		print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
		torch.save(model.state_dict()					, __SCREAMING_SNAKE_CASE )
		print(F"Save configuration file to {pytorch_config_dump_path}" )
		with open(__SCREAMING_SNAKE_CASE					, "w"					, encoding="utf-8" ) as f:
				f.write(config.to_json_string() )


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--gpt2_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
			        "This specifies the model architecture."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


