





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      lowerCAmelCase_ (       __a		)     ->		Tuple:







  """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      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if args.student_type == "roberta":
    lowerCamelCase__:      int			  =False
  elif args.student_type == "gpt2":
    lowerCamelCase__:      Optional[int]			  =False





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





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





def      lowerCAmelCase_ (       )     ->		int:







  """simple docstring"""





  lowerCamelCase__:      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=__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=0.1_5	,    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=0.0_5	,    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=0.0_2	,    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=500	,    help="Tensorboard logging interval."		)
  parser.add_argument("--checkpoint_interval"	,    type=__a	,    default=4000	,    help="Checkpoint interval."		)
  lowerCamelCase__:      int			  =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		)

  lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      int			  =MODEL_CLASSES[args.student_type]
  lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =MODEL_CLASSES[args.teacher_type]

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

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

  lowerCamelCase__:      Tuple			  =LmSeqsDataset(params=__a	,    data=__a		)
  logger.info("Data loader created."		)

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

  if args.student_pretrained_weights is not None:
    logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""		)
    lowerCamelCase__:      Optional[Any]			  =student_model_class.from_pretrained(args.student_pretrained_weights	,    config=__a		)
  else:
    lowerCamelCase__:      int			  =student_model_class(__a		)

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

  # TEACHER #
  lowerCamelCase__:      Tuple			  =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()
  lowerCamelCase__:      List[Any]			  =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()








from typing import Any





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		list:







  """simple docstring"""





  _validation(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  # Creates data structures and fill initial step
  lowerCamelCase__:      dict			  ={}
  lowerCamelCase__:      dict			  ={}
  for state in states_space:
    lowerCamelCase__:      Optional[Any]			  =observations_space[0]
    lowerCamelCase__:      List[Any]			  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    lowerCamelCase__:      int			  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,    len(__a		)		):
    lowerCamelCase__:      Tuple			  =observations_space[o]
    lowerCamelCase__:      Optional[Any]			  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      lowerCamelCase__:      Tuple			  =""
      lowerCamelCase__:      Optional[Any]			  =-1
      for k_state in states_space:
        lowerCamelCase__:      int			  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          lowerCamelCase__:      List[str]			  =probability
          lowerCamelCase__:      int			  =k_state

            # Update probabilities and pointers dicts
      lowerCamelCase__:      Any			  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      lowerCamelCase__:      int			  =arg_max

    # The final observation
  lowerCamelCase__:      Any			  =observations_space[len(__a		) - 1]

  # argmax for given final observation
  lowerCamelCase__:      Optional[Any]			  =""
  lowerCamelCase__:      int			  =-1
  for k_state in states_space:
    lowerCamelCase__:      Tuple			  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      lowerCamelCase__:      List[Any]			  =probability
      lowerCamelCase__:      Dict			  =k_state
  lowerCamelCase__:      str			  =arg_max

  # Process pointers backwards
  lowerCamelCase__:      Union[str, Any]			  =last_state
  lowerCamelCase__:      List[str]			  =[]
  for o in range(len(__a		) - 1	,    -1	,    -1		):
    result.append(__a		)
    lowerCamelCase__:      Union[str, Any]			  =pointers[previous, observations_space[o]]
  result.reverse()

  return result





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_not_empty(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  _validate_lists(__a	,    __a		)
  _validate_dicts(
      __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]		):
    raise ValueError("There's an empty parameter"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_list(__a	,    "observations_space"		)
  _validate_list(__a	,    "states_space"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} must be a list"""
    raise ValueError(__a		)
  else:
    for x in _object:
      if not isinstance(__a	,    __a		):
        lowerCamelCase__:      str			  =F"""{var_name} must be a list of strings"""
        raise ValueError(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_dict(__a	,    "initial_probabilities"	,    __a		)
  _validate_nested_dict(__a	,    "transition_probabilities"		)
  _validate_nested_dict(__a	,    "emission_probabilities"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_dict(_object	,    __a	,    __a		)
  for x in _object.values():
    _validate_dict(__a	,    __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Optional[int]			  =F"""{var_name} must be a dict"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} all keys must be strings"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object.values()		):
    lowerCamelCase__:      Dict			  ="nested dictionary " if nested else ""
    lowerCamelCase__:      List[str]			  =F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(__a		)


if __name__ == "__main__":
     from doctest import testmod

     testmod()

