



"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCAmelCase__      =    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None






def 	snake_case_  (				A_ :		str,    A_ :		int,    A_ :		List[Any]						):


       '''simple docstring'''





       return max(metric_fn(A_,    A_						) for gt in ground_truths						)






def 	snake_case_  (				A_ :		Optional[int],    A_ :		str,    A_ :		Optional[int]						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			[line.strip() for line in open(A_,    '''r'''						).readlines()]
       _lowerCamelCase						:    List[Any]         =			[]

       if args.gold_data_mode == "qa":
              _lowerCamelCase						:    Dict         =			pd.read_csv(A_,    sep='''\t''',    header=A_						)
              for answer_list in data[1]:
                     _lowerCamelCase						:    Tuple         =			ast.literal_eval(A_						)
                     answers.append(A_						)
       else:
              _lowerCamelCase						:    Optional[int]         =			[line.strip() for line in open(A_,    '''r'''						).readlines()]
              _lowerCamelCase						:    str         =			[[reference] for reference in references]

       _lowerCamelCase						:    Union[str, Any]         =			0
       for prediction, ground_truths in zip(A_,    A_						):
              total += 1
              em += metric_max_over_ground_truths(A_,    A_,    A_						)
              fa += metric_max_over_ground_truths(A_,    A_,    A_						)

       _lowerCamelCase						:    List[Any]         =			100.0 * em / total
       _lowerCamelCase						:    int         =			100.0 * fa / total

       logger.info(F'''F1: {fa:.2f}'''						)
       logger.info(F'''EM: {em:.2f}'''						)






def 	snake_case_  (				A_ :		List[Any],    A_ :		int,    A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			args.k
       _lowerCamelCase						:    Tuple         =			[line.strip() for line in open(A_,    '''r'''						).readlines()]
       _lowerCamelCase						:    int         =			[line.strip() for line in open(A_,    '''r'''						).readlines()]

       _lowerCamelCase						:    Tuple         =			0
       for hypo, reference in zip(A_,    A_						):
              _lowerCamelCase						:    List[Any]         =			set(hypo.split('''\t'''						)[:k]						)
              _lowerCamelCase						:    List[Any]         =			set(reference.split('''\t'''						)						)
              total += 1
              em += len(hypo_provenance & ref_provenance						) / k

       _lowerCamelCase						:    List[str]         =			100.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''						)






def 	snake_case_  (				A_ :		Optional[int],    A_ :		Dict,    A_ :		Optional[Any]						):


       '''simple docstring'''





       def strip_title(A_ :		List[Any]						):
              if title.startswith('''"'''						):
                     _lowerCamelCase						:    int         =			title[1:]
              if title.endswith('''"'''						):
                     _lowerCamelCase						:    Any         =			title[:-1]
              return title

       _lowerCamelCase						:    Any         =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           A_,    return_tensors='''pt''',    padding=A_,    truncation=A_,    )['''input_ids'''].to(args.device						)

       _lowerCamelCase						:    int         =			rag_model.rag.question_encoder(A_						)
       _lowerCamelCase						:    Union[str, Any]         =			question_enc_outputs[0]

       _lowerCamelCase						:    int         =			rag_model.retriever(
           A_,    question_enc_pool_output.cpu().detach().to(torch.floataa						).numpy(),    prefix=rag_model.rag.generator.config.prefix,    n_docs=rag_model.config.n_docs,    return_tensors='''pt''',    )
       _lowerCamelCase						:    Tuple         =			rag_model.retriever.index.get_doc_dicts(result.doc_ids						)
       _lowerCamelCase						:    List[Any]         =			[]
       for docs in all_docs:
              _lowerCamelCase						:    Optional[Any]         =			[strip_title(A_						) for title in docs['''title''']]
              provenance_strings.append('''\t'''.join(A_						)						)
       return provenance_strings






def 	snake_case_  (				A_ :		Dict,    A_ :		Union[str, Any],    A_ :		Optional[Any]						):


       '''simple docstring'''





       with torch.no_grad():
              _lowerCamelCase						:    List[Any]         =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  A_,    return_tensors='''pt''',    padding=A_,    truncation=A_						)

              _lowerCamelCase						:    int         =			inputs_dict.input_ids.to(args.device						)
              _lowerCamelCase						:    Optional[Any]         =			inputs_dict.attention_mask.to(args.device						)
              _lowerCamelCase						:    int         =			rag_model.generate(  # rag_model overwrites generate
                  A_,    attention_mask=A_,    num_beams=args.num_beams,    min_length=args.min_length,    max_length=args.max_length,    early_stopping=A_,    num_return_sequences=1,    bad_words_ids=[[0, 0]],    )
              _lowerCamelCase						:    Union[str, Any]         =			rag_model.retriever.generator_tokenizer.batch_decode(A_,    skip_special_tokens=A_						)

              if args.print_predictions:
                     for q, a in zip(A_,    A_						):
                            logger.info('''Q: {} - A: {}'''.format(A_,    A_						)						)

              return answers






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    int         =			argparse.ArgumentParser()
       parser.add_argument(
           '''--model_type''',    choices=['''rag_sequence''', '''rag_token''', '''bart'''],    type=A_,    help=(
               '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
               ''' model_name_or_path'''
           ),    )
       parser.add_argument(
           '''--index_name''',    default=A_,    choices=['''exact''', '''compressed''', '''legacy'''],    type=A_,    help='''RAG model retriever type''',    )
       parser.add_argument(
           '''--index_path''',    default=A_,    type=A_,    help='''Path to the retrieval index''',    )
       parser.add_argument('''--n_docs''',    default=5,    type=A_,    help='''Number of retrieved docs'''						)
       parser.add_argument(
           '''--model_name_or_path''',    default=A_,    type=A_,    required=A_,    help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''',    )
       parser.add_argument(
           '''--eval_mode''',    choices=['''e2e''', '''retrieval'''],    default='''e2e''',    type=A_,    help=(
               '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
               ''' precision@k.'''
           ),    )
       parser.add_argument('''--k''',    default=1,    type=A_,    help='''k for the precision@k calculation'''						)
       parser.add_argument(
           '''--evaluation_set''',    default=A_,    type=A_,    required=A_,    help='''Path to a file containing evaluation samples''',    )
       parser.add_argument(
           '''--gold_data_path''',    default=A_,    type=A_,    required=A_,    help='''Path to a tab-separated file with gold samples''',    )
       parser.add_argument(
           '''--gold_data_mode''',    default='''qa''',    type=A_,    choices=['''qa''', '''ans'''],    help=(
               '''Format of the gold data file'''
               '''qa - a single line in the following format: question [tab] answer_list'''
               '''ans - a single line of the gold file contains the expected answer string'''
           ),    )
       parser.add_argument(
           '''--predictions_path''',    type=A_,    default='''predictions.txt''',    help='''Name of the predictions file, to be stored in the checkpoints directory''',    )
       parser.add_argument(
           '''--eval_all_checkpoints''',    action='''store_true''',    help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''',    )
       parser.add_argument(
           '''--eval_batch_size''',    default=8,    type=A_,    help='''Batch size per GPU/CPU for evaluation.''',    )
       parser.add_argument(
           '''--recalculate''',    help='''Recalculate predictions even if the prediction file exists''',    action='''store_true''',    )
       parser.add_argument(
           '''--num_beams''',    default=4,    type=A_,    help='''Number of beams to be used when generating answers''',    )
       parser.add_argument('''--min_length''',    default=1,    type=A_,    help='''Min length of the generated answers'''						)
       parser.add_argument('''--max_length''',    default=50,    type=A_,    help='''Max length of the generated answers'''						)

       parser.add_argument(
           '''--print_predictions''',    action='''store_true''',    help='''If True, prints predictions while evaluating.''',    )
       parser.add_argument(
           '''--print_docs''',    action='''store_true''',    help='''If True, prints docs retried while generating.''',    )
       _lowerCamelCase						:    Tuple         =			parser.parse_args()
       _lowerCamelCase						:    Optional[int]         =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''						)
       return args






def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			{}
       if args.model_type is None:
              _lowerCamelCase						:    List[str]         =			infer_model_type(args.model_name_or_path						)
              assert args.model_type is not None
       if args.model_type.startswith('''rag'''						):
              _lowerCamelCase						:    Union[str, Any]         =			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
              _lowerCamelCase						:    Optional[Any]         =			args.n_docs
              if args.index_name is not None:
                     _lowerCamelCase						:    Dict         =			args.index_name
              if args.index_path is not None:
                     _lowerCamelCase						:    Any         =			args.index_path
       else:
              _lowerCamelCase						:    Tuple         =			BartForConditionalGeneration

       _lowerCamelCase						:    int         =			(
           [f.path for f in os.scandir(args.model_name_or_path						) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info('''Evaluate the following checkpoints: %s''',    A_						)

       _lowerCamelCase						:    Dict         =			get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
       _lowerCamelCase						:    int         =			evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path						) and (not args.recalculate):
                     logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path						)						)
                     score_fn(A_,    args.predictions_path,    args.gold_data_path						)
                     continue

              logger.info('''***** Running evaluation for {} *****'''.format(A_						)						)
              logger.info('''  Batch size = %d''',    args.eval_batch_size						)
              logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path						)						)

              if args.model_type.startswith('''rag'''						):
                     _lowerCamelCase						:    str         =			RagRetriever.from_pretrained(A_,    **A_						)
                     _lowerCamelCase						:    str         =			model_class.from_pretrained(A_,    retriever=A_,    **A_						)
                     model.retriever.init_retrieval()
              else:
                     _lowerCamelCase						:    Any         =			model_class.from_pretrained(A_,    **A_						)
              model.to(args.device						)

              with open(args.evaluation_set,    '''r'''						) as eval_file, open(args.predictions_path,    '''w'''						) as preds_file:
                     _lowerCamelCase						:    Optional[int]         =			[]
                     for line in tqdm(A_						):
                            questions.append(line.strip()						)
                            if len(A_						) == args.eval_batch_size:
                                   _lowerCamelCase						:    Dict         =			evaluate_batch_fn(A_,    A_,    A_						)
                                   preds_file.write('''\n'''.join(A_						) + '''\n'''						)
                                   preds_file.flush()
                                   _lowerCamelCase						:    str         =			[]
                     if len(A_						) > 0:
                            _lowerCamelCase						:    Optional[int]         =			evaluate_batch_fn(A_,    A_,    A_						)
                            preds_file.write('''\n'''.join(A_						)						)
                            preds_file.flush()

                     score_fn(A_,    args.predictions_path,    args.gold_data_path						)


if __name__ == "__main__":
   lowerCAmelCase__      =    get_args()
   main(args)










"""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


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






def 	snake_case_  (				A_ :		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 	snake_case_  (				A_ :		Dict,    A_ :		Any						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       if args.student_type == "roberta":
              _lowerCamelCase						:    Optional[int]         =			False






def 	snake_case_  (				):


       '''simple docstring'''





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

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

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

       parser.add_argument('''--temperature''',    default=2.0,    type=A_,    help='''Temperature for the softmax temperature.'''						)
       parser.add_argument(
           '''--alpha_ce''',    default=0.5,    type=A_,    help='''Linear weight for the distillation loss. Must be >=0.'''						)
       parser.add_argument(
           '''--alpha_mlm''',    default=0.0,    type=A_,    help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.''',    )
       parser.add_argument('''--alpha_clm''',    default=0.5,    type=A_,    help='''Linear weight for the CLM loss. Must be >=0.'''						)
       parser.add_argument('''--alpha_mse''',    default=0.0,    type=A_,    help='''Linear weight of the MSE loss. Must be >=0.'''						)
       parser.add_argument(
           '''--alpha_cos''',    default=0.0,    type=A_,    help='''Linear weight of the cosine embedding loss. Must be >=0.'''						)

       parser.add_argument(
           '''--mlm''',    action='''store_true''',    help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''						)
       parser.add_argument(
           '''--mlm_mask_prop''',    default=0.15,    type=A_,    help='''Proportion of tokens for which we need to make a prediction.''',    )
       parser.add_argument('''--word_mask''',    default=0.8,    type=A_,    help='''Proportion of tokens to mask out.'''						)
       parser.add_argument('''--word_keep''',    default=0.1,    type=A_,    help='''Proportion of tokens to keep.'''						)
       parser.add_argument('''--word_rand''',    default=0.1,    type=A_,    help='''Proportion of tokens to randomly replace.'''						)
       parser.add_argument(
           '''--mlm_smoothing''',    default=0.7,    type=A_,    help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).''',    )
       parser.add_argument('''--token_counts''',    type=A_,    help='''The token counts in the data_file for MLM.'''						)

       parser.add_argument(
           '''--restrict_ce_to_mask''',    action='''store_true''',    help='''If true, compute the distillation loss only the [MLM] prediction distribution.''',    )
       parser.add_argument(
           '''--freeze_pos_embs''',    action='''store_true''',    help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.''',    )
       parser.add_argument(
           '''--freeze_token_type_embds''',    action='''store_true''',    help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.''',    )

       parser.add_argument('''--n_epoch''',    type=A_,    default=3,    help='''Number of pass on the whole dataset.'''						)
       parser.add_argument('''--batch_size''',    type=A_,    default=5,    help='''Batch size (for each process).'''						)
       parser.add_argument(
           '''--group_by_size''',    action='''store_false''',    help='''If true, group sequences that have similar length into the same batch. Default is true.''',    )

       parser.add_argument(
           '''--gradient_accumulation_steps''',    type=A_,    default=50,    help='''Gradient accumulation for larger training batches.''',    )
       parser.add_argument('''--warmup_prop''',    default=0.05,    type=A_,    help='''Linear warmup proportion.'''						)
       parser.add_argument('''--weight_decay''',    default=0.0,    type=A_,    help='''Weight decay if we apply some.'''						)
       parser.add_argument('''--learning_rate''',    default=5E-4,    type=A_,    help='''The initial learning rate for Adam.'''						)
       parser.add_argument('''--adam_epsilon''',    default=1E-6,    type=A_,    help='''Epsilon for Adam optimizer.'''						)
       parser.add_argument('''--max_grad_norm''',    default=5.0,    type=A_,    help='''Max gradient norm.'''						)
       parser.add_argument('''--initializer_range''',    default=0.02,    type=A_,    help='''Random initialization range.'''						)

       parser.add_argument(
           '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
       parser.add_argument(
           '''--fp16_opt_level''',    type=A_,    default='''O1''',    help=(
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           ),    )
       parser.add_argument('''--n_gpu''',    type=A_,    default=1,    help='''Number of GPUs in the node.'''						)
       parser.add_argument('''--local_rank''',    type=A_,    default=-1,    help='''Distributed training - Local rank'''						)
       parser.add_argument('''--seed''',    type=A_,    default=56,    help='''Random seed'''						)

       parser.add_argument('''--log_interval''',    type=A_,    default=5_00,    help='''Tensorboard logging interval.'''						)
       parser.add_argument('''--checkpoint_interval''',    type=A_,    default=40_00,    help='''Checkpoint interval.'''						)
       _lowerCamelCase						:    List[Any]         =			parser.parse_args()
       sanity_checks(A_						)

       # ARGS #
       init_gpu_params(A_						)
       set_seed(A_						)
       if args.is_master:
              if os.path.exists(args.dump_path						):
                     if not args.force:
                            raise ValueError(
                                F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
                                ''' itUse `--force` if you want to overwrite it'''						)
                     else:
                            shutil.rmtree(args.dump_path						)

              if not os.path.exists(args.dump_path						):
                     os.makedirs(args.dump_path						)
              logger.info(F'''Experiment will be dumped and logged in {args.dump_path}'''						)

              # SAVE PARAMS #
              logger.info(F'''Param: {args}'''						)
              with open(os.path.join(args.dump_path,    '''parameters.json'''						),    '''w'''						) as f:
                     json.dump(vars(A_						),    A_,    indent=4						)
              git_log(args.dump_path						)

       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			MODEL_CLASSES[args.student_type]
       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			MODEL_CLASSES[args.teacher_type]

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

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

       _lowerCamelCase						:    Any         =			LmSeqsDataset(params=A_,    data=A_						)
       logger.info('''Data loader created.'''						)

       # STUDENT #
       logger.info(F'''Loading student config from {args.student_config}'''						)
       _lowerCamelCase						:    str         =			student_config_class.from_pretrained(args.student_config						)
       _lowerCamelCase						:    Union[str, Any]         =			True

       if args.student_pretrained_weights is not None:
              logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
              _lowerCamelCase						:    Dict         =			student_model_class.from_pretrained(args.student_pretrained_weights,    config=A_						)
       else:
              _lowerCamelCase						:    Optional[Any]         =			student_model_class(A_						)

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

       # TEACHER #
       _lowerCamelCase						:    int         =			teacher_model_class.from_pretrained(args.teacher_name,    output_hidden_states=A_						)
       if args.n_gpu > 0:
              teacher.to(F'''cuda:{args.local_rank}'''						)
       logger.info(F'''Teacher loaded from {args.teacher_name}.'''						)

       # FREEZING #
       if args.freeze_pos_embs:
              freeze_pos_embeddings(A_,    A_						)
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(A_,    A_						)

       # SANITY CHECKS #
       assert student.config.vocab_size == teacher.config.vocab_size
       assert student.config.hidden_size == teacher.config.hidden_size
       assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
       if args.mlm:
              assert token_probs.size(0						) == stu_architecture_config.vocab_size

       # DISTILLER #
       torch.cuda.empty_cache()
       _lowerCamelCase						:    Optional[int]         =			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()


