
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


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


def 				a__ (				snake_case				):





  """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 				a__ (				snake_case    ,       snake_case				):





  """simple docstring"""







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


def 				a__ (				snake_case    ,       snake_case				):





  """simple docstring"""







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


def 				a__ (				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						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=500    ,       help='''Tensorboard logging interval.'''				)
  parser.add_argument('''--checkpoint_interval'''    ,       type=snake_case    ,       default=4_000    ,       help='''Checkpoint interval.'''				)
  __SCREAMING_SNAKE_CASE	:     Tuple =						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				)

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

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

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

  __SCREAMING_SNAKE_CASE	:     List[str] =						LmSeqsDataset(params=snake_case    ,       data=snake_case				)
  logger.info('''Data loader created.'''				)

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

  if args.student_pretrained_weights is not None:
    logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''				)
    __SCREAMING_SNAKE_CASE	:     Optional[int] =						student_model_class.from_pretrained(args.student_pretrained_weights    ,       config=snake_case				)
  else:
    __SCREAMING_SNAKE_CASE	:     List[str] =						student_model_class(snake_case				)

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

  # TEACHER #
  __SCREAMING_SNAKE_CASE	:     List[Any] =						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()
  __SCREAMING_SNAKE_CASE	:     int =						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()

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    ArgumentParser("Accelerate CLI tool"   , usage="accelerate <command> [<args>]"   , allow_abbrev=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    parser.add_subparsers(help="accelerate command helpers"	)

		# Register commands
		get_config_parser(subparsers=lowerCAmelCase	)
		env_command_parser(subparsers=lowerCAmelCase	)
		launch_command_parser(subparsers=lowerCAmelCase	)
		tpu_command_parser(subparsers=lowerCAmelCase	)
		test_command_parser(subparsers=lowerCAmelCase	)

		# Let's go
		SCREAMING_SNAKE_CASE_  :		Dict				      =    parser.parse_args()

		if not hasattr(lowerCAmelCase   , "func"	):
				parser.print_help()
				exit(1	)

		# Run
		args.func(lowerCAmelCase	)


if __name__ == "__main__":
		main()
