




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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				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_   (	lowerCamelCase	,     lowerCamelCase   ):
				if args.student_type == "roberta":
								__magic_name__	:	Optional[Any]								=False
				elif args.student_type == "gpt2":
								__magic_name__	:	str								=False




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				if args.student_type == "roberta":
								__magic_name__	:	Dict								=False




def 		lowerCAmelCase_   (	):
				__magic_name__	:	int								=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=0.1_5	,     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=0.0_5	,     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=0.0_2	,     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."""   )
				__magic_name__	:	List[Any]								=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   )

				__magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=MODEL_CLASSES[args.student_type]
				__magic_name__				, __magic_name__				, __magic_name__	:	Optional[int]								=MODEL_CLASSES[args.teacher_type]

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

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

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

				# STUDENT #
				logger.info(F"Loading student config from {args.student_config}"   )
				__magic_name__	:	Tuple								=student_config_class.from_pretrained(args.student_config   )
				__magic_name__	:	Tuple								=True

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

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

				# TEACHER #
				__magic_name__	:	Dict								=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()
				__magic_name__	:	Union[str, Any]								=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()










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




