


"""simple docstring"""



import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


__snake_case   			=    re.compile(R"""\b(a|an|the)\b""", re.UNICODE)

__snake_case   			=    None







def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Any      					=    argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
    parser.add_argument("data_file"						,							metavar="data.json"						,							help="Input data JSON file." )
    parser.add_argument("pred_file"						,							metavar="pred.json"						,							help="Model predictions." )
    parser.add_argument(
        "--out-file"						,							"-o"						,							metavar="eval.json"						,							help="Write accuracy metrics to file (default is stdout)." )
    parser.add_argument(
        "--na-prob-file"						,							"-n"						,							metavar="na_prob.json"						,							help="Model estimates of probability of no answer." )
    parser.add_argument(
        "--na-prob-thresh"						,							"-t"						,							type=lowercase						,							default=1.0						,							help="Predict \"\" if no-answer probability exceeds this (default = 1.0)."						,							)
    parser.add_argument(
        "--out-image-dir"						,							"-p"						,							metavar="out_images"						,							default=lowercase						,							help="Save precision-recall curves to directory." )
    parser.add_argument("--verbose"						,							"-v"						,							action="store_true" )
    if len(sys.argv ) == 1:
        parser.print_help()
        sys.exit(1 )
    return parser.parse_args()







def   __lowerCAmelCase						(						lowercase  :  str )	->						Optional[Any]:


    """simple docstring"""





    snake_case    :      List[Any]      					=    {}
    for article in dataset:
        for p in article["paragraphs"]:
            for qa in p["qas"]:
                snake_case    :      str      					=    bool(qa["answers"]["text"] )
    return qid_to_has_ans







def   __lowerCAmelCase						(						lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





    def remove_articles(lowercase  :  Optional[int] ):
        return ARTICLES_REGEX.sub(" "						,							lowercase )

    def white_space_fix(lowercase  :  List[str] ):
        return " ".join(text.split() )

    def remove_punc(lowercase  :  Tuple ):
        snake_case    :      List[str]      					=    set(string.punctuation )
        return "".join(ch for ch in text if ch not in exclude )

    def lower(lowercase  :  Tuple ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )







def   __lowerCAmelCase						(						lowercase  :  Optional[Any] )	->						Any:


    """simple docstring"""





    if not s:
        return []
    return normalize_answer(lowercase ).split()







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  List[Any] )	->						str:


    """simple docstring"""





    return int(normalize_answer(lowercase ) == normalize_answer(lowercase ) )







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Optional[int] )	->						Optional[int]:


    """simple docstring"""





    snake_case    :      str      					=    get_tokens(lowercase )
    snake_case    :      Optional[int]      					=    get_tokens(lowercase )
    snake_case    :      str      					=    collections.Counter(lowercase ) & collections.Counter(lowercase )
    snake_case    :      str      					=    sum(common.values() )
    if len(lowercase ) == 0 or len(lowercase ) == 0:
        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
        return int(gold_toks == pred_toks )
    if num_same == 0:
        return 0
    snake_case    :      Dict      					=    1.0 * num_same / len(lowercase )
    snake_case    :      int      					=    1.0 * num_same / len(lowercase )
    snake_case    :      Union[str, Any]      					=    (2 * precision * recall) / (precision + recall)
    return fa







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Dict )	->						Dict:


    """simple docstring"""





    snake_case    :      Optional[int]      					=    {}
    snake_case    :      Dict      					=    {}
    for article in dataset:
        for p in article["paragraphs"]:
            for qa in p["qas"]:
                snake_case    :      str      					=    qa["id"]
                snake_case    :      List[Any]      					=    [t for t in qa["answers"]["text"] if normalize_answer(lowercase )]
                if not gold_answers:
                    # For unanswerable questions, only correct answer is empty string
                    snake_case    :      List[str]      					=    [""]
                if qid not in preds:
                    print(F'Missing prediction for {qid}' )
                    continue
                snake_case    :      Dict      					=    preds[qid]
                # Take max over all gold answers
                snake_case    :      List[str]      					=    max(compute_exact(lowercase						,							lowercase ) for a in gold_answers )
                snake_case    :      Union[str, Any]      					=    max(compute_fa(lowercase						,							lowercase ) for a in gold_answers )
    return exact_scores, fa_scores







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Optional[Any]						,							lowercase  :  int						,							lowercase  :  str )	->						int:


    """simple docstring"""





    snake_case    :      Optional[int]      					=    {}
    for qid, s in scores.items():
        snake_case    :      Tuple      					=    na_probs[qid] > na_prob_thresh
        if pred_na:
            snake_case    :      List[Any]      					=    float(not qid_to_has_ans[qid] )
        else:
            snake_case    :      int      					=    s
    return new_scores







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Any						,							lowercase  :  Optional[Any]=None )	->						Optional[int]:


    """simple docstring"""





    if not qid_list:
        snake_case    :      int      					=    len(lowercase )
        return collections.OrderedDict(
            [
                ("exact", 100.0 * sum(exact_scores.values() ) / total),
                ("f1", 100.0 * sum(fa_scores.values() ) / total),
                ("total", total),
            ] )
    else:
        snake_case    :      Union[str, Any]      					=    len(lowercase )
        return collections.OrderedDict(
            [
                ("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
                ("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
                ("total", total),
            ] )







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  int						,							lowercase  :  Optional[int] )	->						Optional[int]:


    """simple docstring"""





    for k in new_eval:
        snake_case    :      str      					=    new_eval[k]







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Dict						,							lowercase  :  Tuple						,							lowercase  :  Optional[Any] )	->						str:


    """simple docstring"""





    plt.step(lowercase						,							lowercase						,							color="b"						,							alpha=0.2						,							where="post" )
    plt.fill_between(lowercase						,							lowercase						,							step="post"						,							alpha=0.2						,							color="b" )
    plt.xlabel("Recall" )
    plt.ylabel("Precision" )
    plt.xlim([0.0, 1.05] )
    plt.ylim([0.0, 1.05] )
    plt.title(lowercase )
    plt.savefig(lowercase )
    plt.clf()







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  int						,							lowercase  :  str						,							lowercase  :  Dict						,							lowercase  :  List[str]=None						,							lowercase  :  List[Any]=None )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Optional[Any]      					=    sorted(lowercase						,							key=lambda lowercase : na_probs[k] )
    snake_case    :      List[Any]      					=    0.0
    snake_case    :      Tuple      					=    1.0
    snake_case    :      Dict      					=    0.0
    snake_case    :      Union[str, Any]      					=    [1.0]
    snake_case    :      Tuple      					=    [0.0]
    snake_case    :      Dict      					=    0.0
    for i, qid in enumerate(lowercase ):
        if qid_to_has_ans[qid]:
            true_pos += scores[qid]
        snake_case    :      Tuple      					=    true_pos / float(i + 1 )
        snake_case    :      Tuple      					=    true_pos / float(lowercase )
        if i == len(lowercase ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
            # i.e., if we can put a threshold after this point
            avg_prec += cur_p * (cur_r - recalls[-1])
            precisions.append(lowercase )
            recalls.append(lowercase )
    if out_image:
        plot_pr_curve(lowercase						,							lowercase						,							lowercase						,							lowercase )
    return {"ap": 100.0 * avg_prec}







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  List[Any]						,							lowercase  :  List[str]						,							lowercase  :  List[str]						,							lowercase  :  Dict						,							lowercase  :  Dict )	->						Any:


    """simple docstring"""





    if out_image_dir and not os.path.exists(lowercase ):
        os.makedirs(lowercase )
    snake_case    :      Dict      					=    sum(1 for v in qid_to_has_ans.values() if v )
    if num_true_pos == 0:
        return
    snake_case    :      Any      					=    make_precision_recall_eval(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							out_image=os.path.join(lowercase						,							"pr_exact.png" )						,							title="Precision-Recall curve for Exact Match score"						,							)
    snake_case    :      Union[str, Any]      					=    make_precision_recall_eval(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							out_image=os.path.join(lowercase						,							"pr_f1.png" )						,							title="Precision-Recall curve for F1 score"						,							)
    snake_case    :      Tuple      					=    {k: float(lowercase ) for k, v in qid_to_has_ans.items()}
    snake_case    :      Tuple      					=    make_precision_recall_eval(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							out_image=os.path.join(lowercase						,							"pr_oracle.png" )						,							title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"						,							)
    merge_eval(lowercase						,							lowercase						,							"pr_exact" )
    merge_eval(lowercase						,							lowercase						,							"pr_f1" )
    merge_eval(lowercase						,							lowercase						,							"pr_oracle" )







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  Tuple )	->						Union[str, Any]:


    """simple docstring"""





    if not qid_list:
        return
    snake_case    :      Dict      					=    [na_probs[k] for k in qid_list]
    snake_case    :      Any      					=    np.ones_like(lowercase ) / float(len(lowercase ) )
    plt.hist(lowercase						,							weights=lowercase						,							bins=20						,							range=(0.0, 1.0) )
    plt.xlabel("Model probability of no-answer" )
    plt.ylabel("Proportion of dataset" )
    plt.title(F'Histogram of no-answer probability: {name}' )
    plt.savefig(os.path.join(lowercase						,							F'na_prob_hist_{name}.png' ) )
    plt.clf()







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Dict						,							lowercase  :  str						,							lowercase  :  List[Any] )	->						Any:


    """simple docstring"""





    snake_case    :      Dict      					=    sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
    snake_case    :      List[Any]      					=    num_no_ans
    snake_case    :      Optional[int]      					=    cur_score
    snake_case    :      Optional[int]      					=    0.0
    snake_case    :      List[Any]      					=    sorted(lowercase						,							key=lambda lowercase : na_probs[k] )
    for i, qid in enumerate(lowercase ):
        if qid not in scores:
            continue
        if qid_to_has_ans[qid]:
            snake_case    :      Optional[Any]      					=    scores[qid]
        else:
            if preds[qid]:
                snake_case    :      List[Any]      					=    -1
            else:
                snake_case    :      Any      					=    0
        cur_score += diff
        if cur_score > best_score:
            snake_case    :      List[str]      					=    cur_score
            snake_case    :      Any      					=    na_probs[qid]
    return 100.0 * best_score / len(lowercase ), best_thresh







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  str						,							lowercase  :  Tuple						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    snake_case					,snake_case    :      int      					=    find_best_thresh(lowercase						,							lowercase						,							lowercase						,							lowercase )
    snake_case					,snake_case    :      str      					=    find_best_thresh(lowercase						,							lowercase						,							lowercase						,							lowercase )
    snake_case    :      List[str]      					=    best_exact
    snake_case    :      Optional[Any]      					=    exact_thresh
    snake_case    :      List[Any]      					=    best_fa
    snake_case    :      Dict      					=    fa_thresh







def   __lowerCAmelCase						(						)	->						Optional[int]:


    """simple docstring"""





    with open(OPTS.data_file ) as f:
        snake_case    :      Optional[int]      					=    json.load(lowercase )
        snake_case    :      Union[str, Any]      					=    dataset_json["data"]
    with open(OPTS.pred_file ) as f:
        snake_case    :      Any      					=    json.load(lowercase )
    if OPTS.na_prob_file:
        with open(OPTS.na_prob_file ) as f:
            snake_case    :      Dict      					=    json.load(lowercase )
    else:
        snake_case    :      str      					=    {k: 0.0 for k in preds}
    snake_case    :      Any      					=    make_qid_to_has_ans(lowercase )  # maps qid to True/False
    snake_case    :      Any      					=    [k for k, v in qid_to_has_ans.items() if v]
    snake_case    :      Any      					=    [k for k, v in qid_to_has_ans.items() if not v]
    snake_case					,snake_case    :      int      					=    get_raw_scores(lowercase						,							lowercase )
    snake_case    :      Tuple      					=    apply_no_ans_threshold(lowercase						,							lowercase						,							lowercase						,							OPTS.na_prob_thresh )
    snake_case    :      Optional[Any]      					=    apply_no_ans_threshold(lowercase						,							lowercase						,							lowercase						,							OPTS.na_prob_thresh )
    snake_case    :      List[str]      					=    make_eval_dict(lowercase						,							lowercase )
    if has_ans_qids:
        snake_case    :      Any      					=    make_eval_dict(lowercase						,							lowercase						,							qid_list=lowercase )
        merge_eval(lowercase						,							lowercase						,							"HasAns" )
    if no_ans_qids:
        snake_case    :      Optional[Any]      					=    make_eval_dict(lowercase						,							lowercase						,							qid_list=lowercase )
        merge_eval(lowercase						,							lowercase						,							"NoAns" )
    if OPTS.na_prob_file:
        find_all_best_thresh(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase )
    if OPTS.na_prob_file and OPTS.out_image_dir:
        run_precision_recall_analysis(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							OPTS.out_image_dir )
        histogram_na_prob(lowercase						,							lowercase						,							OPTS.out_image_dir						,							"hasAns" )
        histogram_na_prob(lowercase						,							lowercase						,							OPTS.out_image_dir						,							"noAns" )
    if OPTS.out_file:
        with open(OPTS.out_file						,							"w" ) as f:
            json.dump(lowercase						,							lowercase )
    else:
        print(json.dumps(lowercase						,							indent=2 ) )


if __name__ == "__main__":
  __snake_case   			=    parse_args()
  if OPTS.out_image_dir:
    import matplotlib

    matplotlib.use("""Agg""")
    import matplotlib.pyplot as plt
  main()









"""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


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







def   __lowerCAmelCase						(						lowercase  :  Any )	->						Union[str, Any]:


    """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						(						lowercase  :  int						,							lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      List[str]      					=    False
    elif args.student_type == "gpt2":
        snake_case    :      Optional[int]      					=    False







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[int] )	->						Optional[Any]:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      Optional[Any]      					=    False







def   __lowerCAmelCase						(						)	->						int:


    """simple docstring"""





    snake_case    :      Any      					=    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=lowercase						,							required=lowercase						,							help="The output directory (log, checkpoints, parameters, etc.)" )
    parser.add_argument(
        "--data_file"						,							type=lowercase						,							required=lowercase						,							help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."						,							)

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

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

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

    parser.add_argument("--log_interval"						,							type=lowercase						,							default=500						,							help="Tensorboard logging interval." )
    parser.add_argument("--checkpoint_interval"						,							type=lowercase						,							default=4000						,							help="Checkpoint interval." )
    snake_case    :      str      					=    parser.parse_args()
    sanity_checks(lowercase )

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

    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.student_type]
    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.teacher_type]

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

    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    :      Dict      					=    pickle.load(lowercase )

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

    snake_case    :      str      					=    LmSeqsDataset(params=lowercase						,							data=lowercase )
    logger.info("Data loader created." )

    # STUDENT #
    logger.info(F'Loading student config from {args.student_config}' )
    snake_case    :      str      					=    student_config_class.from_pretrained(args.student_config )
    snake_case    :      Optional[int]      					=    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=lowercase )
    else:
        snake_case    :      Union[str, Any]      					=    student_model_class(lowercase )

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

    # TEACHER #
    snake_case    :      List[Any]      					=    teacher_model_class.from_pretrained(args.teacher_name						,							output_hidden_states=lowercase )
    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(lowercase						,							lowercase )
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(lowercase						,							lowercase )

    # 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    :      Optional[Any]      					=    Distiller(
        params=lowercase						,							dataset=lowercase						,							token_probs=lowercase						,							student=lowercase						,							teacher=lowercase )
    distiller.train()
    logger.info("Let's go get some drinks." )


if __name__ == "__main__":
  main()





