


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






def       lowerCamelCase					(a_						:Dict)     ->							Any:
       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_						:Union[str, Any]				,				a_						:Union[str, Any])     ->							List[str]:
       if args.student_type == "roberta":
              lowercase		:Optional[int]       							=				False
       elif args.student_type == "gpt2":
              lowercase		:List[Any]       							=				False






def       lowerCamelCase					(a_						:int				,				a_						:List[str])     ->							Dict:
       if args.student_type == "roberta":
              lowercase		:Any       							=				False






def       lowerCamelCase					()     ->							Union[str, Any]:
       lowercase		: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.''')
       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		:List[Any]       							=				MODEL_CLASSES[args.student_type]
       lowercase							,  lowercase							,  lowercase		:int       							=				MODEL_CLASSES[args.teacher_type]

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

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

       lowercase		:List[str]       							=				LmSeqsDataset(params=a_				,				data=a_)
       logger.info('''Data loader created.''')

       # STUDENT #
       logger.info(F"""Loading student config from {args.student_config}""")
       lowercase		:Optional[Any]       							=				student_config_class.from_pretrained(args.student_config)
       lowercase		:Tuple       							=				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		:Dict       							=				student_model_class(a_)

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

       # TEACHER #
       lowercase		:Optional[Any]       							=				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		:Union[str, 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 __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def       lowerCamelCase					()     ->							tuple[list[int], int]:
       lowercase		:Any       							=				[randint(-1000				,				1000) for i in range(10)]
       lowercase		:Any       							=				randint(-5000				,				5000)
       return (arr, r)


UpperCAmelCase      		=			make_dataset()






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, ...]:
       for triplet in permutations(a_				,				3):
              if sum(a_) == target:
                     return tuple(sorted(a_))
       return (0, 0, 0)






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		:Union[str, Any]       							=				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase      		=			solution_times()
 print(F"""The time for naive implementation is {times[0]}.""")
 print(F"""The time for optimized implementation is {times[1]}.""")

