






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 : Union[str, Any]						   =       {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def       snake_case_		(     lowerCAmelCase_       :						Optional[Any]      ):
							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_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Dict      ):
							if args.student_type == "roberta":
														__lowercase      :							Tuple				=     False
							elif args.student_type == "gpt2":
														__lowercase      :							int				=     False
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Tuple      ):
							if args.student_type == "roberta":
														__lowercase      :							Union[str, Any]				=     False
def       snake_case_		(     ):
							__lowercase      :							Tuple				=     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=lowerCAmelCase_       ,       required=lowerCAmelCase_       ,       help="""The output directory (log, checkpoints, parameters, etc.)"""      )
							parser.add_argument(
							    """--data_file"""       ,       type=lowerCAmelCase_       ,       required=lowerCAmelCase_       ,       help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""       ,       )

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

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

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

							parser.add_argument("""--log_interval"""       ,       type=lowerCAmelCase_       ,       default=500       ,       help="""Tensorboard logging interval."""      )
							parser.add_argument("""--checkpoint_interval"""       ,       type=lowerCAmelCase_       ,       default=4000       ,       help="""Checkpoint interval."""      )
							__lowercase      :							Optional[int]				=     parser.parse_args()
							sanity_checks(lowerCAmelCase_      )

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

							__lowercase			,			__lowercase			,			__lowercase      :							List[str]				=     MODEL_CLASSES[args.student_type]
							__lowercase			,			__lowercase			,			__lowercase      :							Dict				=     MODEL_CLASSES[args.teacher_type]

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

							if args.mlm:
														logger.info(F"Loading token counts from {args.token_counts} (already pre-computed)"      )
														with open(args.token_counts       ,       """rb"""      ) as fp:
																					__lowercase      :							Optional[int]				=     pickle.load(lowerCAmelCase_      )

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

							__lowercase      :							Optional[int]				=     LmSeqsDataset(params=lowerCAmelCase_       ,       data=lowerCAmelCase_      )
							logger.info("""Data loader created."""      )

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

							if args.student_pretrained_weights is not None:
														logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"      )
														__lowercase      :							str				=     student_model_class.from_pretrained(args.student_pretrained_weights       ,       config=lowerCAmelCase_      )
							else:
														__lowercase      :							int				=     student_model_class(lowerCAmelCase_      )

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

							# TEACHER #
							__lowercase      :							Optional[int]				=     teacher_model_class.from_pretrained(args.teacher_name       ,       output_hidden_states=lowerCAmelCase_      )
							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(lowerCAmelCase_       ,       lowerCAmelCase_      )
							if args.freeze_token_type_embds:
														freeze_token_type_embeddings(lowerCAmelCase_       ,       lowerCAmelCase_      )

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


if __name__ == "__main__":
	main()







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



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


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps