





'''simple docstring'''

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


a        =						{
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def 						__magic_name__       (							__UpperCAmelCase      )						->					List[str]:

   '''simple docstring'''







   assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
   assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
   if args.mlm:
      assert os.path.isfile(args.token_counts      )
      assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
   else:
      assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

   assert args.teacher_type == args.student_type or (
       args.student_type == "distilbert" and args.teacher_type == "bert"
   )
   assert os.path.isfile(args.student_config      )
   if args.student_pretrained_weights is not None:
      assert os.path.isfile(args.student_pretrained_weights      )

   if args.freeze_token_type_embds:
      assert args.student_type in ["roberta"]

   assert args.alpha_ce >= 0.0
   assert args.alpha_mlm >= 0.0
   assert args.alpha_clm >= 0.0
   assert args.alpha_mse >= 0.0
   assert args.alpha_cos >= 0.0
   assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   if args.student_type == "roberta":
      __SCREAMING_SNAKE_CASE      							=       False
   elif args.student_type == "gpt2":
      __SCREAMING_SNAKE_CASE      							=       False

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   if args.student_type == "roberta":
      __SCREAMING_SNAKE_CASE      							=       False

def 						__magic_name__       (							)						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       argparse.ArgumentParser(description="""Training"""      )
   parser.add_argument("""--force"""							,	action="""store_true"""							,	help="""Overwrite dump_path if it already exists."""      )

   parser.add_argument(
       """--dump_path"""							,	type=__UpperCAmelCase							,	required=__UpperCAmelCase							,	help="""The output directory (log, checkpoints, parameters, etc.)"""      )
   parser.add_argument(
       """--data_file"""							,	type=__UpperCAmelCase							,	required=__UpperCAmelCase							,	help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""							,	)

   parser.add_argument(
       """--student_type"""							,	type=__UpperCAmelCase							,	choices=["""distilbert""", """roberta""", """gpt2"""]							,	required=__UpperCAmelCase							,	help="""The student type (DistilBERT, RoBERTa)."""							,	)
   parser.add_argument("""--student_config"""							,	type=__UpperCAmelCase							,	required=__UpperCAmelCase							,	help="""Path to the student configuration."""      )
   parser.add_argument(
       """--student_pretrained_weights"""							,	default=__UpperCAmelCase							,	type=__UpperCAmelCase							,	help="""Load student initialization checkpoint."""      )

   parser.add_argument(
       """--teacher_type"""							,	choices=["""bert""", """roberta""", """gpt2"""]							,	required=__UpperCAmelCase							,	help="""Teacher type (BERT, RoBERTa)."""      )
   parser.add_argument("""--teacher_name"""							,	type=__UpperCAmelCase							,	required=__UpperCAmelCase							,	help="""The teacher model."""      )

   parser.add_argument("""--temperature"""							,	default=2.0							,	type=__UpperCAmelCase							,	help="""Temperature for the softmax temperature."""      )
   parser.add_argument(
       """--alpha_ce"""							,	default=0.5							,	type=__UpperCAmelCase							,	help="""Linear weight for the distillation loss. Must be >=0."""      )
   parser.add_argument(
       """--alpha_mlm"""							,	default=0.0							,	type=__UpperCAmelCase							,	help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""							,	)
   parser.add_argument("""--alpha_clm"""							,	default=0.5							,	type=__UpperCAmelCase							,	help="""Linear weight for the CLM loss. Must be >=0."""      )
   parser.add_argument("""--alpha_mse"""							,	default=0.0							,	type=__UpperCAmelCase							,	help="""Linear weight of the MSE loss. Must be >=0."""      )
   parser.add_argument(
       """--alpha_cos"""							,	default=0.0							,	type=__UpperCAmelCase							,	help="""Linear weight of the cosine embedding loss. Must be >=0."""      )

   parser.add_argument(
       """--mlm"""							,	action="""store_true"""							,	help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""      )
   parser.add_argument(
       """--mlm_mask_prop"""							,	default=0.1_5							,	type=__UpperCAmelCase							,	help="""Proportion of tokens for which we need to make a prediction."""							,	)
   parser.add_argument("""--word_mask"""							,	default=0.8							,	type=__UpperCAmelCase							,	help="""Proportion of tokens to mask out."""      )
   parser.add_argument("""--word_keep"""							,	default=0.1							,	type=__UpperCAmelCase							,	help="""Proportion of tokens to keep."""      )
   parser.add_argument("""--word_rand"""							,	default=0.1							,	type=__UpperCAmelCase							,	help="""Proportion of tokens to randomly replace."""      )
   parser.add_argument(
       """--mlm_smoothing"""							,	default=0.7							,	type=__UpperCAmelCase							,	help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""							,	)
   parser.add_argument("""--token_counts"""							,	type=__UpperCAmelCase							,	help="""The token counts in the data_file for MLM."""      )

   parser.add_argument(
       """--restrict_ce_to_mask"""							,	action="""store_true"""							,	help="""If true, compute the distillation loss only the [MLM] prediction distribution."""							,	)
   parser.add_argument(
       """--freeze_pos_embs"""							,	action="""store_true"""							,	help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""							,	)
   parser.add_argument(
       """--freeze_token_type_embds"""							,	action="""store_true"""							,	help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""							,	)

   parser.add_argument("""--n_epoch"""							,	type=__UpperCAmelCase							,	default=3							,	help="""Number of pass on the whole dataset."""      )
   parser.add_argument("""--batch_size"""							,	type=__UpperCAmelCase							,	default=5							,	help="""Batch size (for each process)."""      )
   parser.add_argument(
       """--group_by_size"""							,	action="""store_false"""							,	help="""If true, group sequences that have similar length into the same batch. Default is true."""							,	)

   parser.add_argument(
       """--gradient_accumulation_steps"""							,	type=__UpperCAmelCase							,	default=50							,	help="""Gradient accumulation for larger training batches."""							,	)
   parser.add_argument("""--warmup_prop"""							,	default=0.0_5							,	type=__UpperCAmelCase							,	help="""Linear warmup proportion."""      )
   parser.add_argument("""--weight_decay"""							,	default=0.0							,	type=__UpperCAmelCase							,	help="""Weight decay if we apply some."""      )
   parser.add_argument("""--learning_rate"""							,	default=5e-4							,	type=__UpperCAmelCase							,	help="""The initial learning rate for Adam."""      )
   parser.add_argument("""--adam_epsilon"""							,	default=1e-6							,	type=__UpperCAmelCase							,	help="""Epsilon for Adam optimizer."""      )
   parser.add_argument("""--max_grad_norm"""							,	default=5.0							,	type=__UpperCAmelCase							,	help="""Max gradient norm."""      )
   parser.add_argument("""--initializer_range"""							,	default=0.0_2							,	type=__UpperCAmelCase							,	help="""Random initialization range."""      )

   parser.add_argument(
       """--fp16"""							,	action="""store_true"""							,	help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""							,	)
   parser.add_argument(
       """--fp16_opt_level"""							,	type=__UpperCAmelCase							,	default="""O1"""							,	help=(
           """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
           """See details at https://nvidia.github.io/apex/amp.html"""
       )							,	)
   parser.add_argument("""--n_gpu"""							,	type=__UpperCAmelCase							,	default=1							,	help="""Number of GPUs in the node."""      )
   parser.add_argument("""--local_rank"""							,	type=__UpperCAmelCase							,	default=-1							,	help="""Distributed training - Local rank"""      )
   parser.add_argument("""--seed"""							,	type=__UpperCAmelCase							,	default=56							,	help="""Random seed"""      )

   parser.add_argument("""--log_interval"""							,	type=__UpperCAmelCase							,	default=500							,	help="""Tensorboard logging interval."""      )
   parser.add_argument("""--checkpoint_interval"""							,	type=__UpperCAmelCase							,	default=4000							,	help="""Checkpoint interval."""      )
   __SCREAMING_SNAKE_CASE      							=       parser.parse_args()
   sanity_checks(__UpperCAmelCase      )

   # ARGS #
   init_gpu_params(__UpperCAmelCase      )
   set_seed(__UpperCAmelCase      )
   if args.is_master:
      if os.path.exists(args.dump_path      ):
         if not args.force:
            raise ValueError(
                f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                """ itUse `--force` if you want to overwrite it"""      )
         else:
            shutil.rmtree(args.dump_path      )

      if not os.path.exists(args.dump_path      ):
         os.makedirs(args.dump_path      )
      logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""      )

      # SAVE PARAMS #
      logger.info(f"""Param: {args}"""      )
      with open(os.path.join(args.dump_path							,	"""parameters.json"""      )							,	"""w"""      ) as f:
         json.dump(vars(__UpperCAmelCase      )							,	__UpperCAmelCase							,	indent=4      )
      git_log(args.dump_path      )

   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       MODEL_CLASSES[args.student_type]
   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       MODEL_CLASSES[args.teacher_type]

   # TOKENIZER #
   __SCREAMING_SNAKE_CASE      							=       teacher_tokenizer_class.from_pretrained(args.teacher_name      )
   __SCREAMING_SNAKE_CASE      							=       {}
   for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
      __SCREAMING_SNAKE_CASE      							=       tokenizer.all_special_tokens.index(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       tokenizer.all_special_ids[idx]
   logger.info(f"""Special tokens {special_tok_ids}"""      )
   __SCREAMING_SNAKE_CASE      							=       special_tok_ids
   __SCREAMING_SNAKE_CASE      							=       tokenizer.max_model_input_sizes[args.teacher_name]

   # DATA LOADER #
   logger.info(f"""Loading data from {args.data_file}"""      )
   with open(args.data_file							,	"""rb"""      ) as fp:
      __SCREAMING_SNAKE_CASE      							=       pickle.load(__UpperCAmelCase      )

   if args.mlm:
      logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""      )
      with open(args.token_counts							,	"""rb"""      ) as fp:
         __SCREAMING_SNAKE_CASE      							=       pickle.load(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE      							=       np.maximum(__UpperCAmelCase							,	1      ) ** -args.mlm_smoothing
      for idx in special_tok_ids.values():
         __SCREAMING_SNAKE_CASE      							=       0.0  # do not predict special tokens
      __SCREAMING_SNAKE_CASE      							=       torch.from_numpy(__UpperCAmelCase      )
   else:
      __SCREAMING_SNAKE_CASE      							=       None

   __SCREAMING_SNAKE_CASE      							=       LmSeqsDataset(params=__UpperCAmelCase							,	data=__UpperCAmelCase      )
   logger.info("""Data loader created."""      )

   # STUDENT #
   logger.info(f"""Loading student config from {args.student_config}"""      )
   __SCREAMING_SNAKE_CASE      							=       student_config_class.from_pretrained(args.student_config      )
   __SCREAMING_SNAKE_CASE      							=       True

   if args.student_pretrained_weights is not None:
      logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""      )
      __SCREAMING_SNAKE_CASE      							=       student_model_class.from_pretrained(args.student_pretrained_weights							,	config=__UpperCAmelCase      )
   else:
      __SCREAMING_SNAKE_CASE      							=       student_model_class(__UpperCAmelCase      )

   if args.n_gpu > 0:
      student.to(f"""cuda:{args.local_rank}"""      )
   logger.info("""Student loaded."""      )

   # TEACHER #
   __SCREAMING_SNAKE_CASE      							=       teacher_model_class.from_pretrained(args.teacher_name							,	output_hidden_states=__UpperCAmelCase      )
   if args.n_gpu > 0:
      teacher.to(f"""cuda:{args.local_rank}"""      )
   logger.info(f"""Teacher loaded from {args.teacher_name}."""      )

   # FREEZING #
   if args.freeze_pos_embs:
      freeze_pos_embeddings(__UpperCAmelCase							,	__UpperCAmelCase      )
   if args.freeze_token_type_embds:
      freeze_token_type_embeddings(__UpperCAmelCase							,	__UpperCAmelCase      )

   # SANITY CHECKS #
   assert student.config.vocab_size == teacher.config.vocab_size
   assert student.config.hidden_size == teacher.config.hidden_size
   assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
   if args.mlm:
      assert token_probs.size(0      ) == stu_architecture_config.vocab_size

   # DISTILLER #
   torch.cuda.empty_cache()
   __SCREAMING_SNAKE_CASE      							=       Distiller(
       params=__UpperCAmelCase							,	dataset=__UpperCAmelCase							,	token_probs=__UpperCAmelCase							,	student=__UpperCAmelCase							,	teacher=__UpperCAmelCase      )
   distiller.train()
   logger.info("""Let's go get some drinks."""      )


if __name__ == "__main__":
    main()












'''simple docstring'''

import os
import string
import sys


a        =						1 << 8

a        =						{
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

a        =						KEYMAP["up"]
a        =						KEYMAP["left"]

if sys.platform == "win32":
    a        =						[]
    a        =						{
        b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
        b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
        b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
        b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
        b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
        b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
        b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
        b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
    }

for i in range(10):
    a        =						ord(str(i))

def 						__magic_name__       (							)						->					Union[str, Any]:

   '''simple docstring'''







   if os.name == "nt":
      import msvcrt

      __SCREAMING_SNAKE_CASE      							=       """mbcs"""
      # Flush the keyboard buffer
      while msvcrt.kbhit():
         msvcrt.getch()
      if len(__UpperCAmelCase      ) == 0:
         # Read the keystroke
         __SCREAMING_SNAKE_CASE      							=       msvcrt.getch()

         # If it is a prefix char, get second part
         if ch in (b"\x00", b"\xe0"):
            __SCREAMING_SNAKE_CASE      							=       ch + msvcrt.getch()
            # Translate actual Win chars to bullet char types
            try:
               __SCREAMING_SNAKE_CASE      							=       chr(WIN_KEYMAP[cha]      )
               WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]      )      )
               WIN_CH_BUFFER.append(__UpperCAmelCase      )
               if ord(__UpperCAmelCase      ) in (
                   KEYMAP["insert"] - 1 << 9,
                   KEYMAP["delete"] - 1 << 9,
                   KEYMAP["pg_up"] - 1 << 9,
                   KEYMAP["pg_down"] - 1 << 9,
               ):
                  WIN_CH_BUFFER.append(chr(126      )      )
               __SCREAMING_SNAKE_CASE      							=       chr(KEYMAP["""esc"""]      )
            except KeyError:
               __SCREAMING_SNAKE_CASE      							=       cha[1]
         else:
            __SCREAMING_SNAKE_CASE      							=       ch.decode(__UpperCAmelCase      )
      else:
         __SCREAMING_SNAKE_CASE      							=       WIN_CH_BUFFER.pop(0      )
   elif os.name == "posix":
      import termios
      import tty

      __SCREAMING_SNAKE_CASE      							=       sys.stdin.fileno()
      __SCREAMING_SNAKE_CASE      							=       termios.tcgetattr(__UpperCAmelCase      )
      try:
         tty.setraw(__UpperCAmelCase      )
         __SCREAMING_SNAKE_CASE      							=       sys.stdin.read(1      )
      finally:
         termios.tcsetattr(__UpperCAmelCase							,	termios.TCSADRAIN							,	__UpperCAmelCase      )
   return ch







def 						__magic_name__       (							)						->					List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       get_raw_chars()
   if ord(__UpperCAmelCase      ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
      return char

   elif ord(__UpperCAmelCase      ) == KEYMAP["esc"]:
      __SCREAMING_SNAKE_CASE      							=       get_raw_chars()
      if ord(__UpperCAmelCase      ) == KEYMAP["mod_int"]:
         __SCREAMING_SNAKE_CASE      							=       get_raw_chars()
         if ord(__UpperCAmelCase      ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCAmelCase      ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
            return chr(ord(__UpperCAmelCase      ) + ARROW_KEY_FLAG      )
         else:
            return KEYMAP["undefined"]
      else:
         return get_raw_chars()

   else:
      if char in string.printable:
         return char
      else:
         return KEYMAP["undefined"]





