


'''simple docstring'''



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   BigBirdConfig
				__magic_name__       :   jnp.dtype							= jnp.floataa
				__magic_name__       :   bool							= True




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

										"""simple docstring"""




										super().setup()
										UpperCAmelCase         =			nn.Dense(5   ,					dtype=self.dtype   )





				def __call__(	self    :				List[Any]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				int   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			super().__call__(*lowerCAmelCase   ,					**lowerCAmelCase   )
										UpperCAmelCase         =			self.cls(outputs[2]   )
										return outputs[:2] + (cls_out,)







class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Dict							= FlaxBigBirdForNaturalQuestionsModule







def    lowerCamelCase__   ( A     :    str   ,     A     :    Any   ,     A     :    int   ,     A     :    str   ,     A     :    List[str]   ,     A     :    List[Any]  ):




						'''simple docstring'''






						def cross_entropy(A     :    Optional[int]   ,     A     :    Tuple   ,     A     :    int=None  ):
												UpperCAmelCase         =			logits.shape[-1]
												UpperCAmelCase         =			(labels[..., None] == jnp.arange(A  )[None]).astype('''f4'''  )
												UpperCAmelCase         =			jax.nn.log_softmax(A   ,     axis=-1  )
												UpperCAmelCase         =			-jnp.sum(labels * logits   ,     axis=-1  )
												if reduction is not None:
																		UpperCAmelCase         =			reduction(A  )
												return loss

						UpperCAmelCase         =			partial(A   ,     reduction=jnp.mean  )
						UpperCAmelCase         =			cross_entropy(A   ,     A  )
						UpperCAmelCase         =			cross_entropy(A   ,     A  )
						UpperCAmelCase         =			cross_entropy(A   ,     A  )
						return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class  UpperCamelCase__:
				__magic_name__       :   str							= "google/bigbird-roberta-base"
				__magic_name__       :   int							= 3000
				__magic_name__       :   int							= 1_0500

				__magic_name__       :   int							= 128
				__magic_name__       :   int							= 3

				__magic_name__       :   int							= 1
				__magic_name__       :   int							= 5

				# tx_args
				__magic_name__       :   float							= 3e-5
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 2_0000
				__magic_name__       :   float							= 0.0_0_9_5

				__magic_name__       :   str							= "bigbird-roberta-natural-questions"
				__magic_name__       :   str							= "training-expt"
				__magic_name__       :   str							= "data/nq-training.jsonl"
				__magic_name__       :   str							= "data/nq-validation.jsonl"




				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




										os.makedirs(self.base_dir   ,					exist_ok=lowerCAmelCase   )
										UpperCAmelCase         =			os.path.join(self.base_dir   ,					self.save_dir   )
										UpperCAmelCase         =			self.batch_size_per_device * jax.device_count()







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   int
				__magic_name__       :   int							= 4096  # no dynamic padding on TPUs




				def __call__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.collate_fn(lowerCAmelCase   )
										UpperCAmelCase         =			jax.tree_util.tree_map(lowerCAmelCase   ,					lowerCAmelCase   )
										return batch




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.fetch_inputs(features['''input_ids''']   )
										UpperCAmelCase         =			{
										    '''input_ids''': jnp.array(lowerCAmelCase   ,					dtype=jnp.intaa   ),
										    '''attention_mask''': jnp.array(lowerCAmelCase   ,					dtype=jnp.intaa   ),
										    '''start_labels''': jnp.array(features['''start_token''']   ,					dtype=jnp.intaa   ),
										    '''end_labels''': jnp.array(features['''end_token''']   ,					dtype=jnp.intaa   ),
										    '''pooled_labels''': jnp.array(features['''category''']   ,					dtype=jnp.intaa   ),
										}
										return batch




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				list   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			[self._fetch_inputs(lowerCAmelCase   ) for ids in input_ids]
										return zip(*lowerCAmelCase   )





				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				list   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[1 for _ in range(len(lowerCAmelCase   )   )]
										while len(lowerCAmelCase   ) < self.max_length:
																input_ids.append(self.pad_id   )
																attention_mask.append(0   )
										return input_ids, attention_mask







def    lowerCamelCase__   ( A     :    str   ,     A     :    Optional[Any]   ,     A     :    List[str]=None  ):




						'''simple docstring'''






						if seed is not None:
												UpperCAmelCase         =			dataset.shuffle(seed=A  )
						for i in range(len(A  ) // batch_size  ):
												UpperCAmelCase         =			dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(A  )


@partial(jax.pmap   ,     axis_name='''batch'''  )
def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Optional[int]   ,     **A     :    int  ):




						'''simple docstring'''






						def loss_fn(A     :    Dict  ):
												UpperCAmelCase         =			model_inputs.pop('''start_labels'''  )
												UpperCAmelCase         =			model_inputs.pop('''end_labels'''  )
												UpperCAmelCase         =			model_inputs.pop('''pooled_labels'''  )

												UpperCAmelCase         =			state.apply_fn(**A   ,     params=A   ,     dropout_rng=A   ,     train=A  )
												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			outputs

												return state.loss_fn(
												    A   ,     A   ,     A   ,     A   ,     A   ,     A   ,     )

						UpperCAmelCase       ,       UpperCAmelCase         =			jax.random.split(A  )
						UpperCAmelCase         =			jax.value_and_grad(A  )
						UpperCAmelCase       ,       UpperCAmelCase         =			grad_fn(state.params  )
						UpperCAmelCase         =			jax.lax.pmean({'''loss''': loss}   ,     axis_name='''batch'''  )
						UpperCAmelCase         =			jax.lax.pmean(A   ,     '''batch'''  )

						UpperCAmelCase         =			state.apply_gradients(grads=A  )
						return state, metrics, new_drp_rng


@partial(jax.pmap   ,     axis_name='''batch'''  )
def    lowerCamelCase__   ( A     :    List[str]   ,     **A     :    List[str]  ):




						'''simple docstring'''






						UpperCAmelCase         =			model_inputs.pop('''start_labels'''  )
						UpperCAmelCase         =			model_inputs.pop('''end_labels'''  )
						UpperCAmelCase         =			model_inputs.pop('''pooled_labels'''  )

						UpperCAmelCase         =			state.apply_fn(**A   ,     params=state.params   ,     train=A  )
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			outputs

						UpperCAmelCase         =			state.loss_fn(A   ,     A   ,     A   ,     A   ,     A   ,     A  )
						UpperCAmelCase         =			jax.lax.pmean({'''loss''': loss}   ,     axis_name='''batch'''  )
						return metrics






class  UpperCamelCase__(     train_state.TrainState ):
				__magic_name__       :   Callable							= struct.field(pytree_node=lowerCAmelCase )







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   Args
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   wandb
				__magic_name__       :   Callable							= None




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]=None   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			model.params
										UpperCAmelCase         =			TrainState.create(
										    apply_fn=model.__call__   ,					params=lowerCAmelCase   ,					tx=lowerCAmelCase   ,					loss_fn=lowerCAmelCase   ,					)
										if ckpt_dir is not None:
																UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			restore_checkpoint(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			{
																    '''lr''': args.lr,
																    '''init_lr''': args.init_lr,
																    '''warmup_steps''': args.warmup_steps,
																    '''num_train_steps''': num_train_steps,
																    '''weight_decay''': args.weight_decay,
																}
																UpperCAmelCase       ,       UpperCAmelCase         =			build_tx(**lowerCAmelCase   )
																UpperCAmelCase         =			train_state.TrainState(
																    step=lowerCAmelCase   ,					apply_fn=model.__call__   ,					params=lowerCAmelCase   ,					tx=lowerCAmelCase   ,					opt_state=lowerCAmelCase   ,					)
																UpperCAmelCase         =			args
																UpperCAmelCase         =			data_collator
																UpperCAmelCase         =			lr
																UpperCAmelCase         =			params
										UpperCAmelCase         =			jax_utils.replicate(lowerCAmelCase   )
										return state




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.args
										UpperCAmelCase         =			len(lowerCAmelCase   ) // args.batch_size

										UpperCAmelCase         =			jax.random.PRNGKey(0   )
										UpperCAmelCase         =			jax.random.split(lowerCAmelCase   ,					jax.device_count()   )
										for epoch in range(args.max_epochs   ):
																UpperCAmelCase         =			jnp.array(0   ,					dtype=jnp.floataa   )
																UpperCAmelCase         =			get_batched_dataset(lowerCAmelCase   ,					args.batch_size   ,					seed=lowerCAmelCase   )
																UpperCAmelCase         =			0
																for batch in tqdm(lowerCAmelCase   ,					total=lowerCAmelCase   ,					desc=F"""Running EPOCH-{epoch}"""   ):
																						UpperCAmelCase         =			self.data_collator(lowerCAmelCase   )
																						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			self.train_step_fn(lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   )
																						running_loss += jax_utils.unreplicate(metrics['''loss''']   )
																						i += 1
																						if i % args.logging_steps == 0:
																												UpperCAmelCase         =			jax_utils.unreplicate(state.step   )
																												UpperCAmelCase         =			running_loss.item() / i
																												UpperCAmelCase         =			self.scheduler_fn(state_step - 1   )

																												UpperCAmelCase         =			self.evaluate(lowerCAmelCase   ,					lowerCAmelCase   )
																												UpperCAmelCase         =			{
																												    '''step''': state_step.item(),
																												    '''eval_loss''': eval_loss.item(),
																												    '''tr_loss''': tr_loss,
																												    '''lr''': lr.item(),
																												}
																												tqdm.write(str(lowerCAmelCase   )   )
																												self.logger.log(lowerCAmelCase   ,					commit=lowerCAmelCase   )

																						if i % args.save_steps == 0:
																												self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""   ,					state=lowerCAmelCase   )




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Tuple   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			get_batched_dataset(lowerCAmelCase   ,					self.args.batch_size   )
										UpperCAmelCase         =			len(lowerCAmelCase   ) // self.args.batch_size
										UpperCAmelCase         =			jnp.array(0   ,					dtype=jnp.floataa   )
										UpperCAmelCase         =			0
										for batch in tqdm(lowerCAmelCase   ,					total=lowerCAmelCase   ,					desc='''Evaluating ... '''   ):
																UpperCAmelCase         =			self.data_collator(lowerCAmelCase   )
																UpperCAmelCase         =			self.val_step_fn(lowerCAmelCase   ,					**lowerCAmelCase   )
																running_loss += jax_utils.unreplicate(metrics['''loss''']   )
																i += 1
										return running_loss / i





				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				str   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			jax_utils.unreplicate(lowerCAmelCase   )
										print(F"""SAVING CHECKPOINT IN {save_dir}"""   ,					end=''' ... '''   )
										self.model_save_fn(lowerCAmelCase   ,					params=state.params   )
										with open(os.path.join(lowerCAmelCase   ,					'''opt_state.msgpack'''   )   ,					'''wb'''   ) as f:
																f.write(to_bytes(state.opt_state   )   )
										joblib.dump(self.args   ,					os.path.join(lowerCAmelCase   ,					'''args.joblib'''   )   )
										joblib.dump(self.data_collator   ,					os.path.join(lowerCAmelCase   ,					'''data_collator.joblib'''   )   )
										with open(os.path.join(lowerCAmelCase   ,					'''training_state.json'''   )   ,					'''w'''   ) as f:
																json.dump({'''step''': state.step.item()}   ,					lowerCAmelCase   )
										print('''DONE'''   )







def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Dict  ):




						'''simple docstring'''






						print(f"""RESTORING CHECKPOINT FROM {save_dir}"""   ,     end=''' ... '''  )
						with open(os.path.join(A   ,     '''flax_model.msgpack'''  )   ,     '''rb'''  ) as f:
												UpperCAmelCase         =			from_bytes(state.params   ,     f.read()  )

						with open(os.path.join(A   ,     '''opt_state.msgpack'''  )   ,     '''rb'''  ) as f:
												UpperCAmelCase         =			from_bytes(state.opt_state   ,     f.read()  )

						UpperCAmelCase         =			joblib.load(os.path.join(A   ,     '''args.joblib'''  )  )
						UpperCAmelCase         =			joblib.load(os.path.join(A   ,     '''data_collator.joblib'''  )  )

						with open(os.path.join(A   ,     '''training_state.json'''  )   ,     '''r'''  ) as f:
												UpperCAmelCase         =			json.load(A  )
						UpperCAmelCase         =			training_state['''step''']

						print('''DONE'''  )
						return params, opt_state, step, args, data_collator


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Any   ,     A     :    Union[str, Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						UpperCAmelCase         =			num_train_steps - warmup_steps
						UpperCAmelCase         =			optax.linear_schedule(init_value=A   ,     end_value=A   ,     transition_steps=A  )
						UpperCAmelCase         =			optax.linear_schedule(init_value=A   ,     end_value=1E-7   ,     transition_steps=A  )
						UpperCAmelCase         =			optax.join_schedules(schedules=[warmup_fn, decay_fn]   ,     boundaries=[warmup_steps]  )
						return lr




def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    Optional[Any]   ,     A     :    str   ,     A     :    Optional[Any]   ,     A     :    Any  ):




						'''simple docstring'''






						def weight_decay_mask(A     :    List[str]  ):
												UpperCAmelCase         =			traverse_util.flatten_dict(A  )
												UpperCAmelCase         =			{k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
												return traverse_util.unflatten_dict(A  )

						UpperCAmelCase         =			scheduler_fn(A   ,     A   ,     A   ,     A  )

						UpperCAmelCase         =			optax.adamw(learning_rate=A   ,     weight_decay=A   ,     mask=A  )
						return tx, lr









'''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


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


def    lowerCamelCase__   ( A     :    Optional[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__   ( A     :    Any   ,     A     :    str  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False
						elif args.student_type == "gpt2":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    List[str]  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			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.'''  )
						UpperCAmelCase         =			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  )

						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.student_type]
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase         =			teacher_tokenizer_class.from_pretrained(args.teacher_name  )
						UpperCAmelCase         =			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase         =			tokenizer.all_special_tokens.index(A  )
												UpperCAmelCase         =			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""  )
						UpperCAmelCase         =			special_tok_ids
						UpperCAmelCase         =			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:
												UpperCAmelCase         =			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:
																		UpperCAmelCase         =			pickle.load(A  )

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

						UpperCAmelCase         =			LmSeqsDataset(params=A   ,     data=A  )
						logger.info('''Data loader created.'''  )

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""  )
						UpperCAmelCase         =			student_config_class.from_pretrained(args.student_config  )
						UpperCAmelCase         =			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
												UpperCAmelCase         =			student_model_class.from_pretrained(args.student_pretrained_weights   ,     config=A  )
						else:
												UpperCAmelCase         =			student_model_class(A  )

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

						# TEACHER #
						UpperCAmelCase         =			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()
						UpperCAmelCase         =			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()





