




"""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 		_snake_case	(   _snake_case				:   Dict				)       ->   Dict:






      '''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 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   int				)       ->   List[Any]:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   List[str]				)       ->   int:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   List[Any]:






      '''simple docstring'''


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

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

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

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

      parser.add_argument('--log_interval'							,    type=_snake_case							,    default=5_00							,    help='Tensorboard logging interval.'				)
      parser.add_argument('--checkpoint_interval'							,    type=_snake_case							,    default=40_00							,    help='Checkpoint interval.'				)
      _A     =  parser.parse_args()
      sanity_checks(_snake_case				)

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

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

      # TOKENIZER #
      _A     =  teacher_tokenizer_class.from_pretrained(args.teacher_name				)
      _A     =  {}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
            _A     =  tokenizer.all_special_tokens.index(_snake_case				)
            _A     =  tokenizer.all_special_ids[idx]
      logger.info(F'''Special tokens {special_tok_ids}'''				)
      _A     =  special_tok_ids
      _A     =  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:
            _A     =  pickle.load(_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:
                  _A     =  pickle.load(_snake_case				)

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

      _A     =  LmSeqsDataset(params=_snake_case							,    data=_snake_case				)
      logger.info('Data loader created.'				)

      # STUDENT #
      logger.info(F'''Loading student config from {args.student_config}'''				)
      _A     =  student_config_class.from_pretrained(args.student_config				)
      _A     =  True

      if args.student_pretrained_weights is not None:
            logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''				)
            _A     =  student_model_class.from_pretrained(args.student_pretrained_weights							,    config=_snake_case				)
      else:
            _A     =  student_model_class(_snake_case				)

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

      # TEACHER #
      _A     =  teacher_model_class.from_pretrained(args.teacher_name							,    output_hidden_states=_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(_snake_case							,    _snake_case				)
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(_snake_case							,    _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()
      _A     =  Distiller(
          params=_snake_case							,    dataset=_snake_case							,    token_probs=_snake_case							,    student=_snake_case							,    teacher=_snake_case				)
      distiller.train()
      logger.info('Let\'s go get some drinks.'				)


if __name__ == "__main__":
       main()







"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

