






"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase							:       Optional[int]       					=		logging.get_logger(__name__)

_UpperCamelCase							:       List[Any]       					=		{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    __magic_name__       	=	'''squeezebert'''





    def __init__(				self      :   Any							,						A      :   Optional[int]=3_0_5_2_2							,						A      :   Optional[Any]=7_6_8							,						A      :   Tuple=1_2							,						A      :   List[str]=1_2							,						A      :   int=3_0_7_2							,						A      :   Dict="gelu"							,						A      :   int=0.1							,						A      :   Any=0.1							,						A      :   str=5_1_2							,						A      :   List[Any]=2							,						A      :   int=0.02							,						A      :   Optional[Any]=1E-12							,						A      :   Any=0							,						A      :   Any=7_6_8							,						A      :   Any=4							,						A      :   Optional[int]=4							,						A      :   List[str]=4							,						A      :   List[Any]=1							,						A      :   Tuple=4							,						A      :   Optional[Any]=4							,						**A      :   Any							,						):


      '''simple docstring'''




      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_							,						**SCREAMING_SNAKE_CASE_     )

      a	:     Optional[Any]							=  vocab_size
      a	:     Any							=  hidden_size
      a	:     Dict							=  num_hidden_layers
      a	:     str							=  num_attention_heads
      a	:     Optional[Any]							=  hidden_act
      a	:     int							=  intermediate_size
      a	:     Optional[Any]							=  hidden_dropout_prob
      a	:     List[str]							=  attention_probs_dropout_prob
      a	:     Tuple							=  max_position_embeddings
      a	:     Tuple							=  type_vocab_size
      a	:     Optional[Any]							=  initializer_range
      a	:     Union[str, Any]							=  layer_norm_eps
      a	:     Tuple							=  embedding_size
      a	:     Any							=  q_groups
      a	:     int							=  k_groups
      a	:     str							=  v_groups
      a	:     Tuple							=  post_attention_groups
      a	:     Optional[int]							=  intermediate_groups
      a	:     Dict							=  output_groups






class 		snake_case    (    UpperCAmelCase			):





    @property
    def 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




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






import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT 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."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
