
"""simple docstring"""

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def 			lowercase__  (    lowerCAmelCase :    Any       )       ->					str:






    """simple docstring"""




    if "model" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('model.'       , ''       )
    if "norm1" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('norm1'       , 'attention.output.LayerNorm'       )
    if "norm2" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('norm2'       , 'output.LayerNorm'       )
    if "norm" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('norm'       , 'LayerNorm'       )
    if "transformer" in orig_key:
        UpperCAmelCase  	=    orig_key.split('.'       )[0].split('_'       )[-1]
        UpperCAmelCase  	=    orig_key.replace(F"transformer_{layer_num}"       , F"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('mha.attn'       , 'attention.self'       )
    if "mha" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('mha'       , 'attention'       )
    if "W_q" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('W_q'       , 'self.query'       )
    if "W_k" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('W_k'       , 'self.key'       )
    if "W_v" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('W_v'       , 'self.value'       )
    if "ff.0" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('ff.0'       , 'intermediate.dense'       )
    if "ff.2" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('ff.2'       , 'output.dense'       )
    if "ff" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('ff'       , 'output.dense'       )
    if "mlm_class" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('mlm.mlm_class'       , 'cls.predictions.decoder'       )
    if "mlm" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('mlm'       , 'cls.predictions.transform'       )
    if "backbone.backbone.encoders" in orig_key:
        UpperCAmelCase  	=    orig_key.replace('backbone.backbone.encoders'       , 'encoder.layer'       )
    if "cls" not in orig_key:
        UpperCAmelCase  	=    'mra.' + orig_key

    return orig_key




def 			lowercase__  (    lowerCAmelCase :    List[Any]       , lowerCAmelCase :    Union[str, Any]       )       ->					Any:






    """simple docstring"""




    for key in orig_state_dict.copy().keys():
        UpperCAmelCase  	=    orig_state_dict.pop(lowerCAmelCase       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            UpperCAmelCase  	=    val

    UpperCAmelCase  	=    orig_state_dict['cls.predictions.decoder.bias']
    UpperCAmelCase  	=    torch.arange(lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict




def 			lowercase__  (    lowerCAmelCase :    List[str]       , lowerCAmelCase :    List[str]       , lowerCAmelCase :    int       )       ->					List[str]:






    """simple docstring"""




    UpperCAmelCase  	=    torch.load(lowerCAmelCase       , map_location='cpu'       )['model_state_dict']
    UpperCAmelCase  	=    MraConfig.from_json_file(lowerCAmelCase       )
    UpperCAmelCase  	=    MraForMaskedLM(lowerCAmelCase       )

    UpperCAmelCase  	=    convert_checkpoint_helper(config.max_position_embeddings       , lowerCAmelCase       )

    print(model.load_state_dict(lowerCAmelCase       )       )
    model.eval()
    model.save_pretrained(lowerCAmelCase       )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_			    =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for Mra model config.''',
   )
   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_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





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

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





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




