'''simple docstring'''




import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    List[str]:
    # Initialise PyTorch model
    __lowerCamelCase		=  FunnelConfig.from_json_file(UpperCamelCase__				)
    print(f"""Building PyTorch model from configuration: {config}"""				)
    __lowerCamelCase		=  FunnelBaseModel(UpperCamelCase__				) if base_model else FunnelModel(UpperCamelCase__				)

    # Load weights from tf checkpoint
    load_tf_weights_in_funnel(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
    torch.save(model.state_dict()				,  UpperCamelCase__				)


if __name__ == "__main__":
  __UpperCAmelCase     							=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
  )
  __UpperCAmelCase     							=parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
  )





'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__a          =		logging.get_logger(__name__)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     List[str] ,     *snake_case_					:     str ,     **snake_case_					:     List[str]						):
          warnings.warn(
              """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
              """ use BeitImageProcessor instead.""" ,     snake_case_ ,     )
          super().__init__(*snake_case_ ,     **snake_case_						)
