






'''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


UpperCamelCase     :   int	      =		{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def     A__      (					__lowerCAmelCase		:      str    ):
						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     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[str]    ):
						if args.student_type == "roberta":
												lowerCamelCase__								=	False
						elif args.student_type == "gpt2":
												lowerCamelCase__								=	False
def     A__      (					__lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Tuple    ):
						if args.student_type == "roberta":
												lowerCamelCase__								=	False
def     A__      (					):
						lowerCamelCase__								=	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."""    )
						lowerCamelCase__								=	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    )

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

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

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

						lowerCamelCase__								=	LmSeqsDataset(params=__lowerCAmelCase ,     data=__lowerCAmelCase    )
						logger.info("""Data loader created."""    )

						# STUDENT #
						logger.info(F'''Loading student config from {args.student_config}'''    )
						lowerCamelCase__								=	student_config_class.from_pretrained(args.student_config    )
						lowerCamelCase__								=	True

						if args.student_pretrained_weights is not None:
												logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''    )
												lowerCamelCase__								=	student_model_class.from_pretrained(args.student_pretrained_weights ,     config=__lowerCAmelCase    )
						else:
												lowerCamelCase__								=	student_model_class(__lowerCAmelCase    )

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

						# TEACHER #
						lowerCamelCase__								=	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()
						lowerCamelCase__								=	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()



'''simple docstring'''



from manim import *




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					Rectangle(height=0.5       ,  width=0.5 )
												_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0 )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""CPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												cpu.move_to([-2.5, -0.5, 0] )
												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(1 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""GPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												gpu.align_to(_lowercase       ,  _lowercase )
												gpu.set_x(gpu.get_x() - 1 )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )

												_lowerCAmelCase    		=					Text("""Model"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												model.move_to([3, -1.0, 0] )

												self.play(
												    Create(_lowercase       ,  run_time=1 )       ,  Create(_lowercase       ,  run_time=1 )       ,  Create(_lowercase       ,  run_time=1 )       ,  )

												_lowerCAmelCase    		=					MarkupText(
												    F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'       ,  font_size=24       ,  )

												_lowerCAmelCase    		=					Square(side_length=2.2 )
												key.move_to([-5, 2, 0] )

												_lowerCAmelCase    		=					MarkupText(
												    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'       ,  font_size=18       ,  )

												key_text.move_to([-5, 2.4, 0] )

												step_a.move_to([2, 2, 0] )
												self.play(Write(_lowercase       ,  run_time=2.5 )       ,  Write(_lowercase )       ,  Write(_lowercase ) )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												for i, rect in enumerate(_lowercase ):
																		_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase       ,  opacity=0.7 )
																		cpu_target.move_to(_lowercase )
																		cpu_target.generate_target()
																		_lowerCAmelCase    		=					0.46 / 4
																		_lowerCAmelCase    		=					0.46 / 3

																		if i == 0:
																								cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT )       ,  buff=0.02       ,  direction=_lowercase )
																								cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
																		elif i == 3:
																								cpu_target.target.next_to(cpu_targs[0].target       ,  direction=_lowercase       ,  buff=0.0 )
																		else:
																								cpu_target.target.next_to(cpu_targs[i - 1].target       ,  direction=_lowercase       ,  buff=0.0 )
																		cpu_targs.append(_lowercase )

																		first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
																		second_animations.append(MoveToTarget(_lowercase       ,  run_time=1.5 ) )

												self.play(*_lowercase )
												self.play(*_lowercase )

												self.wait()
