






"""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			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if gpta_config_file == "":
            UpperCAmelCase          =	GPTaConfig()
      else:
            UpperCAmelCase          =	GPTaConfig.from_json_file(SCREAMING_SNAKE_CASE_							)
      UpperCAmelCase          =	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
      UpperCAmelCase          =	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      UpperCAmelCase          =	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__":
     _UpperCamelCase					      =  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."""
         ),
     )
     _UpperCamelCase					      =  parser.parse_args()
     convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)











"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

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



