


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      :			List[Any]      				=   logging.get_logger(__name__)
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    RobertaPreLayerNormConfig.from_pretrained(
         _A							,architectures=['''RobertaPreLayerNormForMaskedLM''']    )

     # convert state_dict
     snake_case					=    torch.load(hf_hub_download(repo_id=_A							,filename='''pytorch_model.bin'''    )    )
     snake_case					=    {}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('''roberta.'''    ):
               snake_case					=    'roberta_prelayernorm.' + tensor_key[len('''roberta.'''    ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('''.self.LayerNorm.weight'''    ) or tensor_key.endswith('''.self.LayerNorm.bias'''    ):
               continue

          snake_case					=    tensor_value

     snake_case					=    RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=_A							,config=_A							,state_dict=_A    )
     model.save_pretrained(_A    )

     # convert tokenizer
     snake_case					=    AutoTokenizer.from_pretrained(_A    )
     tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Any      				=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint-repo",
     default=None,
     type=str,
     required=True,
     help="Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _SCREAMING_SNAKE_CASE      :			int      				=   parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)








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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




