
'''simple docstring'''





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase    					=   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCAmelCase    					=   {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase    					=   {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase    					=   sorted(arg_to_scheduler.keys())
UpperCAmelCase    					=   "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 						a   (				pl.LightningModule    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				argparse.Namespace,    SCREAMING_SNAKE_CASE_	:				Tuple=None,    SCREAMING_SNAKE_CASE_	:				Any="base",    SCREAMING_SNAKE_CASE_	:				Tuple=None,    SCREAMING_SNAKE_CASE_	:				str=None,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    0
										snake_case       :     int =    Path(self.hparams.output_dir      )
										snake_case       :     Tuple =    self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																snake_case       :     int =    AutoConfig.from_pretrained(
																    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,    **({'''num_labels''': num_labels} if num_labels is not None else {}),    cache_dir=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     PretrainedConfig =    config

										snake_case       :     Any =    ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
										for p in extra_model_params:
																if getattr(self.hparams,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
																						assert hasattr(self.config,    SCREAMING_SNAKE_CASE_      ), F"""model config doesn't have a `{p}` attribute"""
																						setattr(self.config,    SCREAMING_SNAKE_CASE_,    getattr(self.hparams,    SCREAMING_SNAKE_CASE_      )      )

										if tokenizer is None:
																snake_case       :     str =    AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,    cache_dir=SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     PreTrainedTokenizer =    tokenizer
										snake_case       :     int =    MODEL_MODES[mode]
										if model is None:
																snake_case       :     List[str] =    self.model_type.from_pretrained(
																    self.hparams.model_name_or_path,    from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path      ),    config=self.config,    cache_dir=SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     int =    model




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     str =    self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     int =    arg_to_scheduler[self.hparams.lr_scheduler]
										snake_case       :     Optional[int] =    get_schedule_func(
										    self.opt,    num_warmup_steps=self.hparams.warmup_steps,    num_training_steps=self.total_steps()      )
										snake_case       :     int =    {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Dict =    self.model
										snake_case       :     Optional[int] =    ['''bias''', '''LayerNorm.weight''']
										snake_case       :     str =    [
										    {
										        '''params''': [
										            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
										        ],  # check this named paramters
										        '''weight_decay''': self.hparams.weight_decay,
										    },
										    {
										        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )],
										        '''weight_decay''': 0.0,
										    },
										]
										if self.hparams.adafactor:
																snake_case       :     Union[str, Any] =    Adafactor(
																    SCREAMING_SNAKE_CASE_,    lr=self.hparams.learning_rate,    scale_parameter=SCREAMING_SNAKE_CASE_,    relative_step=SCREAMING_SNAKE_CASE_      )

										else:
																snake_case       :     List[str] =    AdamW(
																    SCREAMING_SNAKE_CASE_,    lr=self.hparams.learning_rate,    eps=self.hparams.adam_epsilon      )
										snake_case       :     Optional[Any] =    optimizer

										snake_case       :     Dict =    self.get_lr_scheduler()

										return [optimizer], [scheduler]




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Any      ):
										return self.validation_step(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.validation_end(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     List[Any] =    max(1,    self.hparams.gpus      )  # TODO: consider num_tpu_cores
										snake_case       :     Union[str, Any] =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										if stage == "test":
																snake_case       :     Tuple =    len(self.test_dataloader().dataset      )
										else:
																snake_case       :     Optional[Any] =    self.get_dataloader('''train''',    self.hparams.train_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )
																snake_case       :     Tuple =    len(self.train_dataloader().dataset      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				bool = False      ):
										raise NotImplementedError('''You must implement this for your task'''      )




				def 	__snake_case							(							self	:				str      ):
										return self.train_loader




				def 	__snake_case							(							self	:				List[str]      ):
										return self.get_dataloader('''dev''',    self.hparams.eval_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										return self.get_dataloader('''test''',    self.hparams.eval_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return os.path.join(
										    self.hparams.data_dir,    '''cached_{}_{}_{}'''.format(
										        SCREAMING_SNAKE_CASE_,    list(filter(SCREAMING_SNAKE_CASE_,    self.hparams.model_name_or_path.split('''/'''      )      )      ).pop(),    str(self.hparams.max_seq_length      ),    ),    )




				@pl.utilities.rank_zero_only
				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict[str, Any]      ):
										snake_case       :     int =    self.output_dir.joinpath('''best_tfmr'''      )
										snake_case       :     int =    self.step_count
										self.model.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int      ):
										parser.add_argument(
										    '''--model_name_or_path''',    default=SCREAMING_SNAKE_CASE_,    type=SCREAMING_SNAKE_CASE_,    required=SCREAMING_SNAKE_CASE_,    help='''Path to pretrained model or model identifier from huggingface.co/models''',    )
										parser.add_argument(
										    '''--config_name''',    default='''''',    type=SCREAMING_SNAKE_CASE_,    help='''Pretrained config name or path if not the same as model_name'''      )
										parser.add_argument(
										    '''--tokenizer_name''',    default=SCREAMING_SNAKE_CASE_,    type=SCREAMING_SNAKE_CASE_,    help='''Pretrained tokenizer name or path if not the same as model_name''',    )
										parser.add_argument(
										    '''--cache_dir''',    default=str(Path(SCREAMING_SNAKE_CASE_      ).parent / '''test_run''' / '''cache'''      ),    type=SCREAMING_SNAKE_CASE_,    help='''Where do you want to store the pre-trained models downloaded from huggingface.co''',    )
										parser.add_argument(
										    '''--encoder_layerdrop''',    type=SCREAMING_SNAKE_CASE_,    help='''Encoder layer dropout probability (Optional). Goes into model.config''',    )
										parser.add_argument(
										    '''--decoder_layerdrop''',    type=SCREAMING_SNAKE_CASE_,    help='''Decoder layer dropout probability (Optional). Goes into model.config''',    )
										parser.add_argument(
										    '''--dropout''',    type=SCREAMING_SNAKE_CASE_,    help='''Dropout probability (Optional). Goes into model.config''',    )
										parser.add_argument(
										    '''--attention_dropout''',    type=SCREAMING_SNAKE_CASE_,    help='''Attention dropout probability (Optional). Goes into model.config''',    )
										parser.add_argument('''--learning_rate''',    default=5e-5,    type=SCREAMING_SNAKE_CASE_,    help='''The initial learning rate for Adam.'''      )
										parser.add_argument(
										    '''--lr_scheduler''',    default='''linear''',    choices=SCREAMING_SNAKE_CASE_,    metavar=SCREAMING_SNAKE_CASE_,    type=SCREAMING_SNAKE_CASE_,    help='''Learning rate scheduler''',    )
										parser.add_argument('''--weight_decay''',    default=0.0,    type=SCREAMING_SNAKE_CASE_,    help='''Weight decay if we apply some.'''      )
										parser.add_argument('''--adam_epsilon''',    default=1e-8,    type=SCREAMING_SNAKE_CASE_,    help='''Epsilon for Adam optimizer.'''      )
										parser.add_argument('''--warmup_steps''',    default=0,    type=SCREAMING_SNAKE_CASE_,    help='''Linear warmup over warmup_steps.'''      )
										parser.add_argument('''--num_workers''',    default=4,    type=SCREAMING_SNAKE_CASE_,    help='''kwarg passed to DataLoader'''      )
										parser.add_argument('''--num_train_epochs''',    dest='''max_epochs''',    default=3,    type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--train_batch_size''',    default=32,    type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--eval_batch_size''',    default=32,    type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--adafactor''',    action='''store_true'''      )




class 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str      ):
										if (
										    trainer.is_global_zero and trainer.global_rank == 0
										):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
																if param.grad is None:
																						print(SCREAMING_SNAKE_CASE_      )




class 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Union[str, Any] =    trainer.lr_schedulers[0]['''scheduler''']
										snake_case       :     Optional[int] =    {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				pl.Trainer,    SCREAMING_SNAKE_CASE_	:				pl.LightningModule      ):
										rank_zero_info('''***** Validation results *****'''      )
										snake_case       :     Dict =    trainer.callback_metrics
										# Log results
										for key in sorted(SCREAMING_SNAKE_CASE_      ):
																if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				pl.Trainer,    SCREAMING_SNAKE_CASE_	:				pl.LightningModule      ):
										rank_zero_info('''***** Test results *****'''      )
										snake_case       :     Optional[Any] =    trainer.callback_metrics
										# Log and save results to file
										snake_case       :     Dict =    os.path.join(pl_module.hparams.output_dir,    '''test_results.txt'''      )
										with open(SCREAMING_SNAKE_CASE_,    '''w'''      ) as writer:
																for key in sorted(SCREAMING_SNAKE_CASE_      ):
																						if key not in ["log", "progress_bar"]:
																												rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )
																												writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )




def      A (       A_							:					List[Any]    ,							A_							:					Union[str, Any]       ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir'''    ,							default=str(Path(A_       ).parent / '''test_run''' / '''model_checkpoints'''       )    ,							type=A_    ,							help='''The output directory where the model predictions and checkpoints will be written.'''    ,							)
						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='''O2'''    ,							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_tpu_cores'''    ,							dest='''tpu_cores'''    ,							type=A_       )
						parser.add_argument('''--max_grad_norm'''    ,							dest='''gradient_clip_val'''    ,							default=1.0    ,							type=A_    ,							help='''Max gradient norm'''       )
						parser.add_argument('''--do_train'''    ,							action='''store_true'''    ,							help='''Whether to run training.'''       )
						parser.add_argument('''--do_predict'''    ,							action='''store_true'''    ,							help='''Whether to run predictions on the test set.'''       )
						parser.add_argument(
						    '''--gradient_accumulation_steps'''    ,							dest='''accumulate_grad_batches'''    ,							type=A_    ,							default=1    ,							help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,							)
						parser.add_argument('''--seed'''    ,							type=A_    ,							default=42    ,							help='''random seed for initialization'''       )
						parser.add_argument(
						    '''--data_dir'''    ,							default=str(Path(A_       ).parent / '''test_run''' / '''dummy-train-data'''       )    ,							type=A_    ,							help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,							)





def      A (       A_							:					BaseTransformer    ,							A_							:					argparse.Namespace    ,							A_							:					Union[str, Any]=None    ,							A_							:					List[Any]=True    ,							A_							:					List[Any]=[]    ,							A_							:					Tuple=None    ,							A_							:					Union[str, Any]=None    ,							**A_							:					List[Any]    ,							):
						pl.seed_everything(args.seed       )

						# init model
						snake_case       :     int =    Path(model.hparams.output_dir       )
						odir.mkdir(exist_ok=A_       )

						# add custom checkpoints
						if checkpoint_callback is None:
												snake_case       :     Optional[Any] =    pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir    ,							prefix='''checkpoint'''    ,							monitor='''val_loss'''    ,							mode='''min'''    ,							save_top_k=1       )
						if early_stopping_callback:
												extra_callbacks.append(A_       )
						if logging_callback is None:
												snake_case       :     Any =    LoggingCallback()

						snake_case       :     List[str] =    {}

						if args.fpaa:
												snake_case       :     str =    16

						if args.gpus > 1:
												snake_case       :     Optional[int] =    '''auto'''
												snake_case       :     Optional[Any] =    '''ddp'''

						snake_case       :     Optional[Any] =    args.accumulate_grad_batches
						snake_case       :     List[str] =    None
						snake_case       :     Union[str, Any] =    '''auto'''

						snake_case       :     Union[str, Any] =    pl.Trainer.from_argparse_args(
						    A_    ,							weights_summary=A_    ,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,							logger=A_    ,							val_check_interval=1    ,							num_sanity_val_steps=2    ,							**A_    ,							)

						if args.do_train:
												trainer.fit(A_       )

						else:
												print('''RAG modeling tests with new set functions successfuly executed!'''       )
						return trainer



'''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


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

def      A (       A_							:					List[str]       ):
						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 (       A_							:					Union[str, Any]    ,							A_							:					Any       ):
						if args.student_type == "roberta":
												snake_case       :     Union[str, Any] =    False
						elif args.student_type == "gpt2":
												snake_case       :     Union[str, Any] =    False

def      A (       A_							:					Dict    ,							A_							:					int       ):
						if args.student_type == "roberta":
												snake_case       :     str =    False

def      A (       ):
						snake_case       :     Optional[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=500    ,							help='''Tensorboard logging interval.'''       )
						parser.add_argument('''--checkpoint_interval'''    ,							type=A_    ,							default=4000    ,							help='''Checkpoint interval.'''       )
						snake_case       :     Tuple =    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       )

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

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

						# DATA LOADER #
						logger.info(F"""Loading data from {args.data_file}"""       )
						with open(args.data_file    ,							'''rb'''       ) as fp:
												snake_case       :     Optional[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:
																		snake_case       :     int =    pickle.load(A_       )

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

						snake_case       :     Optional[Any] =    LmSeqsDataset(params=A_    ,							data=A_       )
						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       :     str =    True

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

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

						# TEACHER #
						snake_case       :     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()
						snake_case       :     Tuple =    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()

