





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__UpperCAmelCase	:       Dict    	=							"bert-base-cased"
__UpperCAmelCase	:       Any    	=							"google/pegasus-xsum"
__UpperCAmelCase	:       Dict    	=							[" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCAmelCase	:       Optional[Any]    	=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCAmelCase	:       Union[str, Any]    	=							"patrickvonplaten/t5-tiny-random"
__UpperCAmelCase	:       List[Any]    	=							"sshleifer/bart-tiny-random"
__UpperCAmelCase	:       List[Any]    	=							"sshleifer/tiny-mbart"
__UpperCAmelCase	:       int    	=							"sshleifer/tiny-marian-en-de"







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Tuple:
  __snake_case:						Tuple			  =						"""\n""".join(SCREAMING_SNAKE_CASE__)
  Path(SCREAMING_SNAKE_CASE__).open("""w""").writelines(SCREAMING_SNAKE_CASE__)







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Tuple:
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__ , F'''{split}.source''') , SCREAMING_SNAKE_CASE__)
    _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__ , F'''{split}.target''') , SCREAMING_SNAKE_CASE__)
  return tmp_dir







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]							,    )
       @slow
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   Union[str, Any]		):
         __snake_case:						Dict			  =						AutoTokenizer.from_pretrained(A		)
         __snake_case:						Optional[int]			  =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
         __snake_case:						Any			  =						max(len(tokenizer.encode(A		)		) for a in ARTICLES		)
         __snake_case:						List[str]			  =						max(len(tokenizer.encode(A		)		) for a in SUMMARIES		)
         __snake_case:						Tuple			  =						4
         __snake_case:						Any			  =						8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         __snake_case  ,       __snake_case:						List[Any]			  =						"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
         __snake_case:						Any			  =						SeqaSeqDataset(
             A							,    data_dir=A							,    type_path="""train"""							,    max_source_length=A							,    max_target_length=A							,    src_lang=A							,    tgt_lang=A							,    )
         __snake_case:						Optional[int]			  =						DataLoader(A							,    batch_size=2							,    collate_fn=train_dataset.collate_fn		)
         for batch in dataloader:
           assert isinstance(A							,    A		)
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_src_len
           # show that targets are the same len
           assert batch["labels"].shape[1] == max_tgt_len
           if tok_name != MBART_TINY:
             continue
           # check language codes in correct place
           __snake_case:						Dict			  =						shift_tokens_right(batch["""labels"""]							,    tokenizer.pad_token_id		)
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch




       @parameterized.expand([BART_TINY, BERT_BASE_CASED]		)
       def 							UpperCAmelCase__			(  self			:   str							,    A			:   int		):
         __snake_case:						str			  =						AutoTokenizer.from_pretrained(A		)
         __snake_case:						Optional[Any]			  =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
         __snake_case:						Tuple			  =						max(len(tokenizer.encode(A		)		) for a in ARTICLES		)
         __snake_case:						Union[str, Any]			  =						max(len(tokenizer.encode(A		)		) for a in SUMMARIES		)
         __snake_case:						Tuple			  =						4
         __snake_case:						Optional[Any]			  =						LegacySeqaSeqDataset(
             A							,    data_dir=A							,    type_path="""train"""							,    max_source_length=20							,    max_target_length=A							,    )
         __snake_case:						List[str]			  =						DataLoader(A							,    batch_size=2							,    collate_fn=train_dataset.collate_fn		)
         for batch in dataloader:
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_len_source
           assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
           # show that targets were truncated
           assert batch["labels"].shape[1] == trunc_target  # Truncated
           assert max_len_target > trunc_target  # Truncated
           break  # No need to test every batch




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						int			  =						AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""		)

         __snake_case:						List[Any]			  =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
         __snake_case:						Tuple			  =						tmp_dir.joinpath("""train.source"""		).open().readlines()
         __snake_case:						Optional[Any]			  =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
         pack_data_dir(A							,    A							,    128							,    A		)
         __snake_case:						Union[str, Any]			  =						{x.name for x in tmp_dir.iterdir()}
         __snake_case:						Dict			  =						{x.name for x in save_dir.iterdir()}
         __snake_case:						Tuple			  =						save_dir.joinpath("""train.source"""		).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(A		) < len(A		)
         assert len(A		) == 1
         assert len(packed_examples[0]		) == sum(len(A		) for x in orig_examples		)
         assert orig_paths == new_paths




       @pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,    reason="""This test requires fairseq"""		)
       def 							UpperCAmelCase__			(  self			:   List[str]		):
         if not FAIRSEQ_AVAILABLE:
           return
         __snake_case  ,       __snake_case  ,       __snake_case:						int			  =						self._get_dataset(max_len=64		)
         __snake_case:						Tuple			  =						64
         __snake_case:						str			  =						ds.make_dynamic_sampler(A							,    required_batch_size_multiple=A		)
         __snake_case:						Dict			  =						[len(A		) for x in batch_sampler]
         assert len(set(A		)		) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(A		) == len(A		)  # no dropped or added examples
         __snake_case:						str			  =						DataLoader(A							,    batch_sampler=A							,    collate_fn=ds.collate_fn							,    num_workers=2		)
         __snake_case:						Tuple			  =						[]
         __snake_case:						Dict			  =						[]
         for batch in data_loader:
           __snake_case:						Dict			  =						batch["""input_ids"""].shape
           __snake_case:						Union[str, Any]			  =						src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           __snake_case:						Optional[int]			  =						np.product(batch["""input_ids"""].shape		)
           num_src_per_batch.append(A		)
           if num_src_tokens > (max_tokens * 1.1):
             failures.append(A		)
         assert num_src_per_batch[0] == max(A		)
         if failures:
           raise AssertionError(f'''too many tokens in {len(A		)} batches'''		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case  ,       __snake_case  ,       __snake_case:						List[str]			  =						self._get_dataset(max_len=512		)
         __snake_case:						str			  =						2
         __snake_case:						Any			  =						ds.make_sortish_sampler(A							,    shuffle=A		)

         __snake_case:						Optional[int]			  =						DataLoader(A							,    batch_size=A							,    collate_fn=ds.collate_fn							,    num_workers=2		)
         __snake_case:						Any			  =						DataLoader(A							,    batch_size=A							,    collate_fn=ds.collate_fn							,    num_workers=2							,    sampler=A		)

         __snake_case:						int			  =						tokenizer.pad_token_id

         def count_pad_tokens(A			:   Optional[Any]							,    A			:   Tuple="input_ids"		):
           return [batch[k].eq(A		).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(A							,    k="""labels"""		)		) < sum(count_pad_tokens(A							,    k="""labels"""		)		)
         assert sum(count_pad_tokens(A		)		) < sum(count_pad_tokens(A		)		)
         assert len(A		) == len(A		)




       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   Optional[int]=1_000							,    A			:   Any=128		):
         if os.getenv("""USE_REAL_DATA"""							,    A		):
           __snake_case:						int			  =						"""examples/seq2seq/wmt_en_ro"""
           __snake_case:						Union[str, Any]			  =						max_len * 2 * 64
           if not Path(A		).joinpath("""train.len"""		).exists():
             save_len_file(A							,    A		)
         else:
           __snake_case:						Union[str, Any]			  =						"""examples/seq2seq/test_data/wmt_en_ro"""
           __snake_case:						Dict			  =						max_len * 4
           save_len_file(A							,    A		)

         __snake_case:						Tuple			  =						AutoTokenizer.from_pretrained(A		)
         __snake_case:						List[str]			  =						SeqaSeqDataset(
             A							,    data_dir=A							,    type_path="""train"""							,    max_source_length=A							,    max_target_length=A							,    n_obs=A							,    )
         return ds, max_tokens, tokenizer




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case  ,       __snake_case  ,       __snake_case:						List[Any]			  =						self._get_dataset()
         __snake_case:						Optional[int]			  =						set(DistributedSortishSampler(A							,    256							,    num_replicas=2							,    rank=0							,    add_extra_examples=A		)		)
         __snake_case:						str			  =						set(DistributedSortishSampler(A							,    256							,    num_replicas=2							,    rank=1							,    add_extra_examples=A		)		)
         assert idsa.intersection(A		) == set()






       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]							,    )
       def 							UpperCAmelCase__			(  self			:   str							,    A			:   Tuple		):
         __snake_case:						Dict			  =						AutoTokenizer.from_pretrained(A							,    use_fast=A		)
         if tok_name == MBART_TINY:
           __snake_case:						Tuple			  =						SeqaSeqDataset(
               A							,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)							,    type_path="""train"""							,    max_source_length=4							,    max_target_length=8							,    src_lang="""EN"""							,    tgt_lang="""FR"""							,    )
           __snake_case:						Dict			  =						train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
           __snake_case:						Union[str, Any]			  =						SeqaSeqDataset(
               A							,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)							,    type_path="""train"""							,    max_source_length=4							,    max_target_length=8							,    )
           __snake_case:						str			  =						train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(A		) == 1 if tok_name == BART_TINY else len(A		) == 0













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







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  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__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   str:
  if args.student_type == "roberta":
    __snake_case:						Optional[Any]			  =						False
  elif args.student_type == "gpt2":
    __snake_case:						str			  =						False







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   List[str]:
  if args.student_type == "roberta":
    __snake_case:						Optional[int]			  =						False







def       A__							(   )       ->   Tuple:
  __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=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=4000 , help="""Checkpoint interval.""")
  __snake_case:						List[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)

  __snake_case  ,       __snake_case  ,       __snake_case:						str			  =						MODEL_CLASSES[args.student_type]
  __snake_case  ,       __snake_case  ,       __snake_case:						Union[str, Any]			  =						MODEL_CLASSES[args.teacher_type]

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

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

  __snake_case:						Union[str, Any]			  =						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}''')
  __snake_case:						Tuple			  =						student_config_class.from_pretrained(args.student_config)
  __snake_case:						List[str]			  =						True

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






