


'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'dpt'


     def __init__(   self			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=384			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=True			,     lowerCamelCase=[2, 5, 8, 11]			,     lowerCamelCase="project"			,     lowerCamelCase=[4, 2, 1, 0.5]			,     lowerCamelCase=[96, 192, 384, 768]			,     lowerCamelCase=256			,     lowerCamelCase=-1			,     lowerCamelCase=False			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=255			,     lowerCamelCase=0.1			,     lowerCamelCase=[1, 1024, 24, 24]			,     lowerCamelCase=[0, 1]			,     lowerCamelCase=None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       Optional[Any]      =							hidden_size
          __A	:       Dict      =							is_hybrid

          if self.is_hybrid:
               if backbone_config is None:
                    logger.info("Initializing the config with a `BiT` backbone."		)
                    __A	:       str      =							{
                        "global_padding": "same",
                        "layer_type": "bottleneck",
                        "depths": [3, 4, 9],
                        "out_features": ["stage1", "stage2", "stage3"],
                        "embedding_dynamic_padding": True,
                    }
                    __A	:       List[str]      =							BitConfig(**lowerCamelCase		)
               elif isinstance(lowerCamelCase			,     lowerCamelCase		):
                    logger.info("Initializing the config with a `BiT` backbone."		)
                    __A	:       int      =							BitConfig(**lowerCamelCase		)
               elif isinstance(lowerCamelCase			,     lowerCamelCase		):
                    __A	:       List[Any]      =							backbone_config
               else:
                    raise ValueError(
                        f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."		)

               __A	:       Tuple      =							backbone_featmap_shape
               __A	:       Any      =							neck_ignore_stages

               if readout_type != "project":
                    raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."		)
          else:
               __A	:       str      =							None
               __A	:       List[str]      =							None
               __A	:       int      =							[]

          __A	:       List[Any]      =							num_hidden_layers
          __A	:       List[Any]      =							num_attention_heads
          __A	:       Optional[Any]      =							intermediate_size
          __A	:       str      =							hidden_act
          __A	:       List[Any]      =							hidden_dropout_prob
          __A	:       Any      =							attention_probs_dropout_prob
          __A	:       Optional[int]      =							initializer_range
          __A	:       List[Any]      =							layer_norm_eps
          __A	:       Optional[Any]      =							image_size
          __A	:       Union[str, Any]      =							patch_size
          __A	:       List[str]      =							num_channels
          __A	:       str      =							qkv_bias
          __A	:       Any      =							backbone_out_indices
          if readout_type not in ["ignore", "add", "project"]:
               raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"		)
          __A	:       Dict      =							readout_type
          __A	:       Optional[Any]      =							reassemble_factors
          __A	:       Tuple      =							neck_hidden_sizes
          __A	:       Tuple      =							fusion_hidden_size
          __A	:       Optional[Any]      =							head_in_index
          __A	:       Optional[int]      =							use_batch_norm_in_fusion_residual
          # auxiliary head attributes (semantic segmentation)
          __A	:       Tuple      =							use_auxiliary_head
          __A	:       str      =							auxiliary_loss_weight
          __A	:       List[Any]      =							semantic_loss_ignore_index
          __A	:       Tuple      =							semantic_classifier_dropout


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							copy.deepcopy(self.__dict__		)

          if output["backbone_config"] is not None:
               __A	:       Union[str, Any]      =							self.backbone_config.to_dict()

          __A	:       Tuple      =							self.__class__.model_type
          return output





'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					is_trivia_qa=SCREAMING_SNAKE_CASE							)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"							)
     model.save_pretrained(SCREAMING_SNAKE_CASE							)


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(
          """--big_bird_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."""
      )
      parser.add_argument(
          """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
      )
      _UpperCamelCase 							=  parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

