






"""simple docstring"""







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       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			RemBertConfig.from_json_file(lowerCAmelCase		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase		)		)		)
       UpperCAmelCase	    =			RemBertModel(lowerCAmelCase		)

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

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


if __name__ == "__main__":
 lowerCAmelCase_	:    int            =      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.'''
 )
 lowerCAmelCase_	:    List[Any]            =      parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Any            =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'canine'



  def __init__(      self ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=1_63_84 ,		snake_case__=16 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=0 ,		snake_case__=0xE_0_0_0 ,		snake_case__=0xE_0_0_1 ,		snake_case__=4 ,		snake_case__=4 ,		snake_case__=8 ,		snake_case__=1_63_84 ,		snake_case__=1_28 ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





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

         UpperCAmelCase	    =			max_position_embeddings
         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	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps

         # Character config:
         UpperCAmelCase	    =			downsampling_rate
         UpperCAmelCase	    =			upsampling_kernel_size
         UpperCAmelCase	    =			num_hash_functions
         UpperCAmelCase	    =			num_hash_buckets
         UpperCAmelCase	    =			local_transformer_stride
