
import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def 				a__   (   a   :				Union[str, Any]       ,     a   :				List[Any]       ,     a   :				List[Any]		):

  """simple docstring"""







  _snake_case     :						Any						=   BertConfig.from_json_file(a		)
  print(f'Building PyTorch model from configuration: {config}'		)
  _snake_case     :						Optional[Any]						=   BertForPreTraining(a		)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(a       ,     a       ,     a		)

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


if __name__ == "__main__":
    _a      :			Union[str, Any]					=							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(
        """--bert_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    _a      :			Any					=							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



"""simple docstring"""
from typing import TYPE_CHECKING

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


_a      :			str					=							{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Optional[Any]					=							["""YolosFeatureExtractor"""]
    _a      :			List[Any]					=							["""YolosImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Union[str, Any]					=							[
        """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """YolosForObjectDetection""",
        """YolosModel""",
        """YolosPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_yolos import YolosFeatureExtractor
        from .image_processing_yolos import YolosImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yolos import (
            YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
            YolosForObjectDetection,
            YolosModel,
            YolosPreTrainedModel,
        )


else:
    import sys

    _a      :			Union[str, Any]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

