



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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""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__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		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=_SCREAMING_SNAKE_CASE				,     required=_SCREAMING_SNAKE_CASE				,     help="""The output directory (log, checkpoints, parameters, etc.)"""    )
				parser.add_argument(
				    """--data_file"""				,     type=_SCREAMING_SNAKE_CASE				,     required=_SCREAMING_SNAKE_CASE				,     help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""				,     )

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

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

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

				parser.add_argument("""--log_interval"""				,     type=_SCREAMING_SNAKE_CASE				,     default=500				,     help="""Tensorboard logging interval."""    )
				parser.add_argument("""--checkpoint_interval"""				,     type=_SCREAMING_SNAKE_CASE				,     default=4000				,     help="""Checkpoint interval."""    )
				__a       =		parser.parse_args()
				sanity_checks(_SCREAMING_SNAKE_CASE    )

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

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

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

				if args.mlm:
								logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)"    )
								with open(args.token_counts				,     """rb"""    ) as fp:
												__a       =		pickle.load(_SCREAMING_SNAKE_CASE    )

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

				__a       =		LmSeqsDataset(params=_SCREAMING_SNAKE_CASE				,     data=_SCREAMING_SNAKE_CASE    )
				logger.info("""Data loader created."""    )

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

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

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

				# TEACHER #
				__a       =		teacher_model_class.from_pretrained(args.teacher_name				,     output_hidden_states=_SCREAMING_SNAKE_CASE    )
				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(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				if args.freeze_token_type_embds:
								freeze_token_type_embeddings(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# 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()
				__a       =		Distiller(
				    params=_SCREAMING_SNAKE_CASE				,     dataset=_SCREAMING_SNAKE_CASE				,     token_probs=_SCREAMING_SNAKE_CASE				,     student=_SCREAMING_SNAKE_CASE				,     teacher=_SCREAMING_SNAKE_CASE    )
				distiller.train()
				logger.info("""Let's go get some drinks."""    )


if __name__ == "__main__":
						main()





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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Dict    ='mgp-str'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Dict=[32, 128]      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		str=27      ,				__lowercase		:		Any=38      ,				__lowercase		:		str=50257      ,				__lowercase		:		List[str]=30522      ,				__lowercase		:		Tuple=768      ,				__lowercase		:		str=12      ,				__lowercase		:		Optional[Any]=12      ,				__lowercase		:		str=4.0      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Union[str, Any]=1E-5      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		Union[str, Any]=0.0      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		List[Any]=0.02      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		max_token_length
											__a       =		num_character_labels
											__a       =		num_bpe_labels
											__a       =		num_wordpiece_labels
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		mlp_ratio
											__a       =		distilled
											__a       =		layer_norm_eps
											__a       =		drop_rate
											__a       =		qkv_bias
											__a       =		attn_drop_rate
											__a       =		drop_path_rate
											__a       =		output_aa_attentions
											__a       =		initializer_range
