


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
   parser.add_argument("--model_name", default="roberta-large", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   lowercase__				     =		parser.parse_args()

   if args.model_type == "roberta":
      lowercase__				     =		RobertaForMaskedLM.from_pretrained(args.model_name)
      lowercase__				     =		"roberta"
   elif args.model_type == "gpt2":
      lowercase__				     =		GPTaLMHeadModel.from_pretrained(args.model_name)
      lowercase__				     =		"transformer"

   lowercase__				     =		model.state_dict()
   lowercase__				     =		{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         lowercase__				     =		state_dict[f'''{prefix}.{param_name}''']
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         lowercase__				     =		f'''{prefix}.embeddings.{w}.weight'''
         lowercase__				     =		state_dict[param_name]
      for w in ["weight", "bias"]:
         lowercase__				     =		f'''{prefix}.embeddings.LayerNorm.{w}'''
         lowercase__				     =		state_dict[param_name]

    # Transformer Blocks #
   lowercase__				     =		0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               lowercase__				     =		state_dict[
                   f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
               ]
         lowercase__				     =		state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               lowercase__				     =		state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         lowercase__				     =		state_dict[f'''{layer}''']
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            lowercase__				     =		state_dict[f'''lm_head.dense.{w}''']
            lowercase__				     =		state_dict[f'''lm_head.layer_norm.{w}''']
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         lowercase__				     =		state_dict[f'''{prefix}.ln_f.{w}''']
      lowercase__				     =		state_dict["lm_head.weight"]

   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)





'''simple docstring'''

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__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _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						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          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),
                  ("token_type_ids", dynamic_axis),
              ]		)

