'''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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if args.student_type == "roberta":
          lowercase_					:       List[str]     				=					False
     elif args.student_type == "gpt2":
          lowercase_					:       Any     				=					False

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if args.student_type == "roberta":
          lowercase_					:       Union[str, Any]     				=					False

def _UpperCamelCase   (       ):
     lowercase_					:       List[Any]     				=					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=SCREAMING_SNAKE_CASE_  ,       required=SCREAMING_SNAKE_CASE_  ,       help='The output directory (log, checkpoints, parameters, etc.)'							)
     parser.add_argument(
         '--data_file'  ,       type=SCREAMING_SNAKE_CASE_  ,       required=SCREAMING_SNAKE_CASE_  ,       help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'  ,       )

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

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

     parser.add_argument('--temperature'  ,       default=2.0  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Temperature for the softmax temperature.'							)
     parser.add_argument(
         '--alpha_ce'  ,       default=0.5  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Linear weight for the distillation loss. Must be >=0.'							)
     parser.add_argument(
         '--alpha_mlm'  ,       default=0.0  ,       type=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       help='Linear weight for the CLM loss. Must be >=0.'							)
     parser.add_argument('--alpha_mse'  ,       default=0.0  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Linear weight of the MSE loss. Must be >=0.'							)
     parser.add_argument(
         '--alpha_cos'  ,       default=0.0  ,       type=SCREAMING_SNAKE_CASE_  ,       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=0.15  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Proportion of tokens for which we need to make a prediction.'  ,       )
     parser.add_argument('--word_mask'  ,       default=0.8  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Proportion of tokens to mask out.'							)
     parser.add_argument('--word_keep'  ,       default=0.1  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Proportion of tokens to keep.'							)
     parser.add_argument('--word_rand'  ,       default=0.1  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Proportion of tokens to randomly replace.'							)
     parser.add_argument(
         '--mlm_smoothing'  ,       default=0.7  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'  ,       )
     parser.add_argument('--token_counts'  ,       type=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       default=3  ,       help='Number of pass on the whole dataset.'							)
     parser.add_argument('--batch_size'  ,       type=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       default=50  ,       help='Gradient accumulation for larger training batches.'  ,       )
     parser.add_argument('--warmup_prop'  ,       default=0.05  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Linear warmup proportion.'							)
     parser.add_argument('--weight_decay'  ,       default=0.0  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Weight decay if we apply some.'							)
     parser.add_argument('--learning_rate'  ,       default=5e-4  ,       type=SCREAMING_SNAKE_CASE_  ,       help='The initial learning rate for Adam.'							)
     parser.add_argument('--adam_epsilon'  ,       default=1e-6  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Epsilon for Adam optimizer.'							)
     parser.add_argument('--max_grad_norm'  ,       default=5.0  ,       type=SCREAMING_SNAKE_CASE_  ,       help='Max gradient norm.'							)
     parser.add_argument('--initializer_range'  ,       default=0.02  ,       type=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       default=1  ,       help='Number of GPUs in the node.'							)
     parser.add_argument('--local_rank'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=-1  ,       help='Distributed training - Local rank'							)
     parser.add_argument('--seed'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=56  ,       help='Random seed'							)

     parser.add_argument('--log_interval'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=500  ,       help='Tensorboard logging interval.'							)
     parser.add_argument('--checkpoint_interval'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=4_000  ,       help='Checkpoint interval.'							)
     lowercase_					:       Optional[Any]     				=					parser.parse_args()
     sanity_checks(SCREAMING_SNAKE_CASE_							)

     # ARGS #
     init_gpu_params(SCREAMING_SNAKE_CASE_							)
     set_seed(SCREAMING_SNAKE_CASE_							)
     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(SCREAMING_SNAKE_CASE_							)  ,       SCREAMING_SNAKE_CASE_  ,       indent=4							)
          git_log(args.dump_path							)

     lowercase_				,lowercase_				,lowercase_					:       int     				=					MODEL_CLASSES[args.student_type]
     lowercase_				,lowercase_				,lowercase_					:       Dict     				=					MODEL_CLASSES[args.teacher_type]

     # TOKENIZER #
     lowercase_					:       List[str]     				=					teacher_tokenizer_class.from_pretrained(args.teacher_name							)
     lowercase_					:       int     				=					{}
     for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
          lowercase_					:       Dict     				=					tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Union[str, Any]     				=					tokenizer.all_special_ids[idx]
     logger.info(f'''Special tokens {special_tok_ids}'''							)
     lowercase_					:       Any     				=					special_tok_ids
     lowercase_					:       List[str]     				=					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:
          lowercase_					:       Optional[int]     				=					pickle.load(SCREAMING_SNAKE_CASE_							)

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

          lowercase_					:       Any     				=					np.maximum(SCREAMING_SNAKE_CASE_  ,       1							) ** -args.mlm_smoothing
          for idx in special_tok_ids.values():
               lowercase_					:       Optional[Any]     				=					0.0  # do not predict special tokens
          lowercase_					:       Any     				=					torch.from_numpy(SCREAMING_SNAKE_CASE_							)
     else:
          lowercase_					:       Optional[int]     				=					None

     lowercase_					:       Tuple     				=					LmSeqsDataset(params=SCREAMING_SNAKE_CASE_  ,       data=SCREAMING_SNAKE_CASE_							)
     logger.info('Data loader created.'							)

     # STUDENT #
     logger.info(f'''Loading student config from {args.student_config}'''							)
     lowercase_					:       Optional[int]     				=					student_config_class.from_pretrained(args.student_config							)
     lowercase_					:       List[Any]     				=					True

     if args.student_pretrained_weights is not None:
          logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''							)
          lowercase_					:       Tuple     				=					student_model_class.from_pretrained(args.student_pretrained_weights  ,       config=SCREAMING_SNAKE_CASE_							)
     else:
          lowercase_					:       Union[str, Any]     				=					student_model_class(SCREAMING_SNAKE_CASE_							)

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

     # TEACHER #
     lowercase_					:       Optional[int]     				=					teacher_model_class.from_pretrained(args.teacher_name  ,       output_hidden_states=SCREAMING_SNAKE_CASE_							)
     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(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     if args.freeze_token_type_embds:
          freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # 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()
     lowercase_					:       Optional[Any]     				=					Distiller(
         params=SCREAMING_SNAKE_CASE_  ,       dataset=SCREAMING_SNAKE_CASE_  ,       token_probs=SCREAMING_SNAKE_CASE_  ,       student=SCREAMING_SNAKE_CASE_  ,       teacher=SCREAMING_SNAKE_CASE_							)
     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': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

_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(1_0):
 _A								=   ord(str(i))

def _UpperCamelCase   (       ):
     if os.name == "nt":
          import msvcrt

          lowercase_					:       Optional[Any]     				=					'mbcs'
          # Flush the keyboard buffer
          while msvcrt.kbhit():
               msvcrt.getch()
          if len(SCREAMING_SNAKE_CASE_							) == 0:
               # Read the keystroke
               lowercase_					:       int     				=					msvcrt.getch()

               # If it is a prefix char, get second part
               if ch in (b"\x00", b"\xe0"):
                    lowercase_					:       int     				=					ch + msvcrt.getch()
                    # Translate actual Win chars to bullet char types
                    try:
                         lowercase_					:       List[Any]     				=					chr(WIN_KEYMAP[cha]							)
                         WIN_CH_BUFFER.append(chr(KEYMAP['mod_int']							)							)
                         WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_							)
                         if ord(SCREAMING_SNAKE_CASE_							) 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							)							)
                         lowercase_					:       Optional[int]     				=					chr(KEYMAP['esc']							)
                    except KeyError:
                         lowercase_					:       Tuple     				=					cha[1]
               else:
                    lowercase_					:       Tuple     				=					ch.decode(SCREAMING_SNAKE_CASE_							)
          else:
               lowercase_					:       Dict     				=					WIN_CH_BUFFER.pop(0							)
     elif os.name == "posix":
          import termios
          import tty

          lowercase_					:       int     				=					sys.stdin.fileno()
          lowercase_					:       Union[str, Any]     				=					termios.tcgetattr(SCREAMING_SNAKE_CASE_							)
          try:
               tty.setraw(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Any     				=					sys.stdin.read(1							)
          finally:
               termios.tcsetattr(SCREAMING_SNAKE_CASE_  ,       termios.TCSADRAIN  ,       SCREAMING_SNAKE_CASE_							)
     return ch






def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					get_raw_chars()
     if ord(SCREAMING_SNAKE_CASE_							) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
          return char

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

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




