






"""simple docstring"""


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 __future__ import annotations

from typing import Any



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not postfix_notation:
    return 0

  __A									= {'''+''', '''-''', '''*''', '''/'''}
  __A									= []

  for token in postfix_notation:
    if token in operations:
      __A			,    __A									= stack.pop(), stack.pop()
      if token == "+":
        stack.append(a + b )
      elif token == "-":
        stack.append(a - b )
      elif token == "*":
        stack.append(a * b )
      else:
        if a * b < 0 and a % b != 0:
          stack.append(a // b + 1 )
        else:
          stack.append(a // b )
    else:
      stack.append(int(__UpperCamelCase ) )

  return stack.pop()


if __name__ == "__main__":
       import doctest

       doctest.testmod()




