





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

lowerCamelCase_	:		Tuple         =   {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  """rembert"""

      def __init__(	self   , snake_case_=2_5_0_3_0_0   , snake_case_=1_1_5_2   , snake_case_=3_2   , snake_case_=1_8   , snake_case_=2_5_6   , snake_case_=1_6_6_4   , snake_case_=4_6_0_8   , snake_case_="gelu"   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=0.1   , snake_case_=5_1_2   , snake_case_=2   , snake_case_=0.02   , snake_case_=1E-12   , snake_case_=True   , snake_case_=0   , snake_case_=3_1_2   , snake_case_=3_1_3   , **snake_case_   , ):

        """simple docstring"""

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

        A_    :			List[str]									=    vocab_size
        A_    :			List[Any]									=    input_embedding_size
        A_    :			List[str]									=    output_embedding_size
        A_    :			Optional[Any]									=    max_position_embeddings
        A_    :			str									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			List[Any]									=    num_attention_heads
        A_    :			Dict									=    intermediate_size
        A_    :			Any									=    hidden_act
        A_    :			Any									=    hidden_dropout_prob
        A_    :			Optional[int]									=    attention_probs_dropout_prob
        A_    :			List[Any]									=    classifier_dropout_prob
        A_    :			List[str]									=    initializer_range
        A_    :			str									=    type_vocab_size
        A_    :			Union[str, Any]									=    layer_norm_eps
        A_    :			Union[str, Any]									=    use_cache
        A_    :			List[Any]									=    False


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_    :			Dict									=    {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),
            ]    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1E-4



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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(
       '''--bert_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.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)