






'''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


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






def 	_UpperCAmelCase    (  a		:	Optional[int]							)			->			str:





						"""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 	_UpperCAmelCase    (  a		:	List[str]						,			a		:	List[Any]							)			->			int:





						"""simple docstring"""
						if args.student_type == "roberta":
												lowercase_      :   int        =			False
						elif args.student_type == "gpt2":
												lowercase_      :   List[Any]        =			False






def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	str							)			->			Tuple:





						"""simple docstring"""
						if args.student_type == "roberta":
												lowercase_      :   Optional[Any]        =			False






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





						"""simple docstring"""
						lowercase_      :   Any        =			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=5_0						,			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=5_6						,			help='Random seed'							)

						parser.add_argument('--log_interval'						,			type=a						,			default=5_0_0						,			help='Tensorboard logging interval.'							)
						parser.add_argument('--checkpoint_interval'						,			type=a						,			default=4_0_0_0						,			help='Checkpoint interval.'							)
						lowercase_      :   str        =			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							)

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

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

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

						lowercase_      :   Optional[int]        =			LmSeqsDataset(params=a						,			data=a							)
						logger.info('Data loader created.'							)

						# STUDENT #
						logger.info(f"Loading student config from {args.student_config}"							)
						lowercase_      :   str        =			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_      :   Any        =			student_model_class.from_pretrained(args.student_pretrained_weights						,			config=a							)
						else:
												lowercase_      :   List[Any]        =			student_model_class(a							)

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

						# TEACHER #
						lowercase_      :   Tuple        =			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()
						lowercase_      :   Optional[Any]        =			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'''

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


A:      int  				=				logging.get_logger(__name__)

A:      int  				=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  'gpt_bigcode'
	SCREAMING_SNAKE_CASE_					:      int        =  ['past_key_values']
	SCREAMING_SNAKE_CASE_					:      Any        =  {
	    'hidden_size': 'n_embd',
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(							self  ,      _lowercase=5_0257  ,      _lowercase=1024  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=None  ,      _lowercase="gelu_pytorch_tanh"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=1E-5  ,      _lowercase=0.02  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=5_0256  ,      _lowercase=5_0256  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      **_lowercase  ,      )						->  Any:
							lowercase_      :   Tuple        =			vocab_size
							lowercase_      :   str        =			n_positions
							lowercase_      :   List[str]        =			n_embd
							lowercase_      :   str        =			n_layer
							lowercase_      :   Optional[Any]        =			n_head
							lowercase_      :   Optional[int]        =			n_inner
							lowercase_      :   Union[str, Any]        =			activation_function
							lowercase_      :   Dict        =			resid_pdrop
							lowercase_      :   str        =			embd_pdrop
							lowercase_      :   Optional[Any]        =			attn_pdrop
							lowercase_      :   List[Any]        =			layer_norm_epsilon
							lowercase_      :   Optional[int]        =			initializer_range
							lowercase_      :   List[Any]        =			scale_attn_weights
							lowercase_      :   Any        =			use_cache
							lowercase_      :   List[str]        =			attention_softmax_in_fpaa
							lowercase_      :   Any        =			scale_attention_softmax_in_fpaa
							lowercase_      :   Optional[Any]        =			multi_query

							lowercase_      :   Optional[Any]        =			bos_token_id
							lowercase_      :   Optional[Any]        =			eos_token_id

							super().__init__(bos_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      **_lowercase       )
