



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="bert", choices=["bert"])
   parser.add_argument("--model_name", default="bert-base-uncased", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   _lowercase    	=		parser.parse_args()

   if args.model_type == "bert":
      _lowercase    	=		BertForMaskedLM.from_pretrained(args.model_name)
      _lowercase    	=		"bert"
   else:
      raise ValueError("args.model_type should be \"bert\".")

   _lowercase    	=		model.state_dict()
   _lowercase    	=		{}

   for w in ["word_embeddings", "position_embeddings"]:
      _lowercase    	=		state_dict[F"{prefix}.embeddings.{w}.weight"]
   for w in ["weight", "bias"]:
      _lowercase    	=		state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

   _lowercase    	=		0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      for w in ["weight", "bias"]:
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
         ]
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
         ]
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
         ]

         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
         ]
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
         ]

         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
         ]
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
         ]
         _lowercase    	=		state_dict[
             F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
         ]
      std_idx += 1

   _lowercase    	=		state_dict["cls.predictions.decoder.weight"]
   _lowercase    	=		state_dict["cls.predictions.bias"]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _lowercase    	=		state_dict[F"cls.predictions.transform.dense.{w}"]
         _lowercase    	=		state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

   print(F"N layers selected for distillation: {std_idx}")
   print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

   print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
   torch.save(compressed_sd, args.dump_checkpoint)





from collections import OrderedDict
from typing import Mapping

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         if self.task == "multiple-choice":
             A__					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ])
