


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


UpperCAmelCase_      : Optional[Any]  					=       {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}







def        SCREAMING_SNAKE_CASE_						(			__A :			int				)	->		int:

       """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        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			Dict				)	->		Optional[Any]:

       """simple docstring"""

       if args.student_type == "roberta":
              a_					:		List[Any]							=  False
       elif args.student_type == "gpt2":
              a_					:		str							=  False







def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Optional[Any]				)	->		Dict:

       """simple docstring"""

       if args.student_type == "roberta":
              a_					:		Union[str, Any]							=  False







def        SCREAMING_SNAKE_CASE_						(			)	->		List[str]:

       """simple docstring"""

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

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

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

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

       parser.add_argument('--log_interval'  ,    type=__A  ,    default=5_00  ,    help='Tensorboard logging interval.'				)
       parser.add_argument('--checkpoint_interval'  ,    type=__A  ,    default=40_00  ,    help='Checkpoint interval.'				)
       a_					:		Any							=  parser.parse_args()
       sanity_checks(__A				)

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

       a_							,		a_							,		a_					:		str							=  MODEL_CLASSES[args.student_type]
       a_							,		a_							,		a_					:		List[Any]							=  MODEL_CLASSES[args.teacher_type]

       # TOKENIZER #
       a_					:		Tuple							=  teacher_tokenizer_class.from_pretrained(args.teacher_name				)
       a_					:		List[str]							=  {}
       for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
              a_					:		Dict							=  tokenizer.all_special_tokens.index(__A				)
              a_					:		str							=  tokenizer.all_special_ids[idx]
       logger.info(F"""Special tokens {special_tok_ids}"""				)
       a_					:		List[str]							=  special_tok_ids
       a_					:		Optional[int]							=  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_					:		int							=  pickle.load(__A				)

       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_					:		Dict							=  pickle.load(__A				)

              a_					:		str							=  np.maximum(__A  ,    1				) ** -args.mlm_smoothing
              for idx in special_tok_ids.values():
                     a_					:		Tuple							=  0.0  # do not predict special tokens
              a_					:		str							=  torch.from_numpy(__A				)
       else:
              a_					:		List[str]							=  None

       a_					:		List[Any]							=  LmSeqsDataset(params=__A  ,    data=__A				)
       logger.info('Data loader created.'				)

       # STUDENT #
       logger.info(F"""Loading student config from {args.student_config}"""				)
       a_					:		int							=  student_config_class.from_pretrained(args.student_config				)
       a_					:		Any							=  True

       if args.student_pretrained_weights is not None:
              logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""				)
              a_					:		int							=  student_model_class.from_pretrained(args.student_pretrained_weights  ,    config=__A				)
       else:
              a_					:		Dict							=  student_model_class(__A				)

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

       # TEACHER #
       a_					:		int							=  teacher_model_class.from_pretrained(args.teacher_name  ,    output_hidden_states=__A				)
       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(__A  ,    __A				)
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(__A  ,    __A				)

       # 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_					:		List[str]							=  Distiller(
           params=__A  ,    dataset=__A  ,    token_probs=__A  ,    student=__A  ,    teacher=__A				)
       distiller.train()
       logger.info('Let\'s go get some drinks.'				)


if __name__ == "__main__":
   main()





def 		_lowercase (     lowercase__   ,	lowercase__						):

      if density <= 0:
            raise ValueError('''Impossible fluid density'''						)
      if bulk_modulus <= 0:
            raise ValueError('''Impossible bulk modulus'''						)

      return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()


