






from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			int =					"distilbert"
 _SCREAMING_SNAKE_CASE							:			int =					{
     "hidden_size": "dim",
     "num_attention_heads": "n_heads",
     "num_hidden_layers": "n_layers",
 }


 def __init__(	self							,  _A=30522							,  _A=512							,  _A=False							,  _A=6							,  _A=12							,  _A=768							,  _A=4 * 768							,  _A=0.1							,  _A=0.1							,  _A="gelu"							,  _A=0.02							,  _A=0.1							,  _A=0.2							,  _A=0							,  **_A							,  ) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				vocab_size
     _UpperCAmelCase				:      Optional[int]			  =				max_position_embeddings
     _UpperCAmelCase				:      List[str]			  =				sinusoidal_pos_embds
     _UpperCAmelCase				:      Any			  =				n_layers
     _UpperCAmelCase				:      str			  =				n_heads
     _UpperCAmelCase				:      Optional[Any]			  =				dim
     _UpperCAmelCase				:      str			  =				hidden_dim
     _UpperCAmelCase				:      Optional[Any]			  =				dropout
     _UpperCAmelCase				:      str			  =				attention_dropout
     _UpperCAmelCase				:      str			  =				activation
     _UpperCAmelCase				:      List[str]			  =				initializer_range
     _UpperCAmelCase				:      Any			  =				qa_dropout
     _UpperCAmelCase				:      Optional[int]			  =				seq_classif_dropout
     super().__init__(**_A							,  pad_token_id=_A)



class 							A_    (						__lowercase			):






 '''simple docstring'''




 @property
 def  snake_case__				(	self) ->		Mapping[str, Mapping[int, str]]:

     """simple docstring"""



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












import argparse

import torch

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


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

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

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


if __name__ == "__main__":
  SCREAMING_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(
      '--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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




