






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 							=						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}
class 							A_    (						snake_case_			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[int] =					"""camembert"""


 def __init__(	self							,  _A=30522							,  _A=768							,  _A=12							,  _A=12							,  _A=3072							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=512							,  _A=2							,  _A=0.02							,  _A=1e-12							,  _A=1							,  _A=0							,  _A=2							,  _A="absolute"							,  _A=True							,  _A=None							,  **_A							,  ) ->		Tuple:

     """simple docstring"""



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

     _UpperCAmelCase				:      Union[str, Any]			  =				vocab_size
     _UpperCAmelCase				:      Optional[int]			  =				hidden_size
     _UpperCAmelCase				:      int			  =				num_hidden_layers
     _UpperCAmelCase				:      str			  =				num_attention_heads
     _UpperCAmelCase				:      Any			  =				hidden_act
     _UpperCAmelCase				:      Tuple			  =				intermediate_size
     _UpperCAmelCase				:      Dict			  =				hidden_dropout_prob
     _UpperCAmelCase				:      str			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      List[str]			  =				max_position_embeddings
     _UpperCAmelCase				:      str			  =				type_vocab_size
     _UpperCAmelCase				:      Dict			  =				initializer_range
     _UpperCAmelCase				:      Dict			  =				layer_norm_eps
     _UpperCAmelCase				:      Tuple			  =				position_embedding_type
     _UpperCAmelCase				:      str			  =				use_cache
     _UpperCAmelCase				:      List[Any]			  =				classifier_dropout



class 							A_    (						snake_case_			):






 '''simple docstring'''




 @property
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     if self.task == "multiple-choice":
         _UpperCAmelCase				:      Optional[Any]			  =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         _UpperCAmelCase				:      Any			  =				{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)




