





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase				:	Any      						=  logging.get_logger(__name__)

lowercase				:	Tuple      						=  {
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}





class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  '''dinat'''

   __A       :    Optional[Any]	  =  {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }

   def __init__( self ,  lowercase=4 ,  lowercase=3 ,  lowercase=64 ,  lowercase=[3, 4, 6, 5] ,  lowercase=[2, 4, 8, 16] ,  lowercase=7 ,  lowercase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,  lowercase=3.0 ,  lowercase=True ,  lowercase=0.0 ,  lowercase=0.0 ,  lowercase=0.1 ,  lowercase="gelu" ,  lowercase=0.02 ,  lowercase=1e-5 ,  lowercase=0.0 ,  lowercase=None ,  lowercase=None ,  **lowercase ,  )      ->						str:







          '''simple docstring'''

          super().__init__(**lowercase)

          a__						:     Any			=    patch_size
          a__						:     List[Any]			=    num_channels
          a__						:     Tuple			=    embed_dim
          a__						:     int			=    depths
          a__						:     List[str]			=    len(lowercase)
          a__						:     List[Any]			=    num_heads
          a__						:     Optional[Any]			=    kernel_size
          a__						:     str			=    dilations
          a__						:     Optional[int]			=    mlp_ratio
          a__						:     Any			=    qkv_bias
          a__						:     Tuple			=    hidden_dropout_prob
          a__						:     Union[str, Any]			=    attention_probs_dropout_prob
          a__						:     Optional[int]			=    drop_path_rate
          a__						:     List[str]			=    hidden_act
          a__						:     Optional[Any]			=    layer_norm_eps
          a__						:     Optional[int]			=    initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          a__						:     List[Any]			=    int(embed_dim * 2 ** (len(lowercase) - 1))
          a__						:     Union[str, Any]			=    layer_scale_init_value
          a__						:     str			=    ['stem'] + [F'stage{idx}' for idx in range(1 ,  len(lowercase) + 1)]
          a__			,							a__						:     str			=    get_aligned_output_features_output_indices(
              out_features=lowercase ,  out_indices=lowercase ,  stage_names=self.stage_names)









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

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

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


if __name__ == "__main__":
    lowercase				:	Dict      						=  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."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


