






'''simple docstring'''

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__						:List[Any]          =			logging.get_logger(__name__)

UpperCAmelCase__						:str          =			{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			'efficientformer'




       def __init__(					self      : str	,    A__      : List[int] = [3, 2, 6, 4]	,    A__      : List[int] = [48, 96, 224, 448]	,    A__      : List[bool] = [True, True, True, True]	,    A__      : int = 448	,    A__      : int = 32	,    A__      : int = 4	,    A__      : int = 7	,    A__      : int = 5	,    A__      : int = 8	,    A__      : int = 4	,    A__      : float = 0.0	,    A__      : int = 16	,    A__      : int = 3	,    A__      : int = 3	,    A__      : int = 3	,    A__      : int = 2	,    A__      : int = 1	,    A__      : float = 0.0	,    A__      : int = 1	,    A__      : bool = True	,    A__      : bool = True	,    A__      : float = 1e-5	,    A__      : str = "gelu"	,    A__      : float = 0.02	,    A__      : float = 1e-1_2	,    A__      : int = 224	,    A__      : float = 1e-0_5	,    **A__      : str	,    ):


            """simple docstring"""







            super().__init__(**A__					)

            __lowerCamelCase						:       Optional[int]				   =				hidden_act
            __lowerCamelCase						:       Dict				   =				hidden_dropout_prob
            __lowerCamelCase						:       str				   =				hidden_sizes
            __lowerCamelCase						:       List[Any]				   =				num_hidden_layers
            __lowerCamelCase						:       List[str]				   =				num_attention_heads
            __lowerCamelCase						:       Optional[Any]				   =				initializer_range
            __lowerCamelCase						:       Dict				   =				layer_norm_eps
            __lowerCamelCase						:       str				   =				patch_size
            __lowerCamelCase						:       Union[str, Any]				   =				num_channels
            __lowerCamelCase						:       List[str]				   =				depths
            __lowerCamelCase						:       int				   =				mlp_expansion_ratio
            __lowerCamelCase						:       Any				   =				downsamples
            __lowerCamelCase						:       Union[str, Any]				   =				dim
            __lowerCamelCase						:       Tuple				   =				key_dim
            __lowerCamelCase						:       int				   =				attention_ratio
            __lowerCamelCase						:       Dict				   =				resolution
            __lowerCamelCase						:       str				   =				pool_size
            __lowerCamelCase						:       str				   =				downsample_patch_size
            __lowerCamelCase						:       int				   =				downsample_stride
            __lowerCamelCase						:       Tuple				   =				downsample_pad
            __lowerCamelCase						:       Union[str, Any]				   =				drop_path_rate
            __lowerCamelCase						:       List[Any]				   =				num_metaad_blocks
            __lowerCamelCase						:       str				   =				distillation
            __lowerCamelCase						:       Union[str, Any]				   =				use_layer_scale
            __lowerCamelCase						:       Optional[Any]				   =				layer_scale_init_value
            __lowerCamelCase						:       List[str]				   =				image_size
            __lowerCamelCase						:       Any				   =				batch_norm_eps










'''simple docstring'''

import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

     # Load weights from tf checkpoint
     load_tf_weights_in_rembert(_lowercase,						_lowercase,						_lowercase     )

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



