


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[Any] 			=		'convbert'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case=768   ,    snake_case=2   ,    snake_case=9   ,    snake_case=1   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case   ,    )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							embedding_size
						UpperCamelCase__       =							head_ratio
						UpperCamelCase__       =							conv_kernel_size
						UpperCamelCase__       =							num_groups
						UpperCamelCase__       =							classifier_dropout







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									UpperCamelCase__       =							{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]  )






'''simple docstring'''






from typing import Union

import fire
import torch
from tqdm import tqdm







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  str = "cpu"				,       A__   :  Union[str, None] = None   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  torch.load(A__				,       map_location=A__   )
  for k, v in tqdm(state_dict.items()   ):
    if not isinstance(A__				,       torch.Tensor   ):
      raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""   )
    lowerCAmelCase_     : List[str]     =  v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_     : List[str]     =  src_path
  torch.save(A__				,       A__   )


if __name__ == "__main__":
  fire.Fire(convert)



