

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


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

def        lowerCamelCase__       (    _a):
	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							,	_a):
	if args.student_type == "roberta":
		SCREAMING_SNAKE_CASE      :					Any    =				False
	elif args.student_type == "gpt2":
		SCREAMING_SNAKE_CASE      :					Dict    =				False

def        lowerCamelCase__       (    _a							,	_a):
	if args.student_type == "roberta":
		SCREAMING_SNAKE_CASE      :					int    =				False

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					str    =				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.")
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				parser.parse_args()
	sanity_checks(_a)

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

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

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

	SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[Any]    =				MODEL_CLASSES[args.student_type]
	SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				MODEL_CLASSES[args.teacher_type]

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

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

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

	# STUDENT #
	logger.info(f"Loading student config from {args.student_config}")
	SCREAMING_SNAKE_CASE      :					List[str]    =				student_config_class.from_pretrained(args.student_config)
	SCREAMING_SNAKE_CASE      :					List[str]    =				True

	if args.student_pretrained_weights is not None:
		logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
		SCREAMING_SNAKE_CASE      :					str    =				student_model_class.from_pretrained(args.student_pretrained_weights							,	config=_a)
	else:
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				student_model_class(_a)

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

	# TEACHER #
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				teacher_model_class.from_pretrained(args.teacher_name							,	output_hidden_states=_a)
	if args.n_gpu > 0:
		teacher.to(f"cuda:{args.local_rank}")
	logger.info(f"Teacher loaded from {args.teacher_name}.")

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

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

	# DISTILLER #
	torch.cuda.empty_cache()
	SCREAMING_SNAKE_CASE      :					List[str]    =				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()







"""simple docstring"""




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

