


'''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


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


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



					'''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       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					if args.student_type == "roberta":
										UpperCamelCase__       :Dict							 =			False
					elif args.student_type == "gpt2":
										UpperCamelCase__       :List[str]							 =			False


def       a				(      __a			, __a       )  ->     List[Any]:



					'''simple docstring'''






					if args.student_type == "roberta":
										UpperCamelCase__       :Dict							 =			False


def       a				(      )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, 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=0.1_5			, 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=0.0_5			, 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=0.0_2			, 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.'''       )
					UpperCamelCase__       :Dict							 =			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       )

					UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			MODEL_CLASSES[args.student_type]
					UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			MODEL_CLASSES[args.teacher_type]

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

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

					UpperCamelCase__       :str							 =			LmSeqsDataset(params=__a			, data=__a       )
					logger.info('''Data loader created.'''       )

					# STUDENT #
					logger.info(f'''Loading student config from {args.student_config}'''       )
					UpperCamelCase__       :Dict							 =			student_config_class.from_pretrained(args.student_config       )
					UpperCamelCase__       :int							 =			True

					if args.student_pretrained_weights is not None:
										logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''       )
										UpperCamelCase__       :str							 =			student_model_class.from_pretrained(args.student_pretrained_weights			, config=__a       )
					else:
										UpperCamelCase__       :Optional[int]							 =			student_model_class(__a       )

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

					# TEACHER #
					UpperCamelCase__       :List[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()
					UpperCamelCase__       :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'''
from __future__ import annotations

__snake_case											=					[True] * 1000001
__snake_case											=					2
while i * i <= 1000000:
		if seive[i]:
				for j in range(i * i, 1000001, i):
						__snake_case											=					False
		i += 1


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return seive[n]


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return any(digit in '''02468''' for digit in str(__a       )       )


def       a				(      __a = 1000000       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			[2]  # result already includes the number 2.
					for num in range(3			, limit + 1			, 2       ):
										if is_prime(__a       ) and not contains_an_even_digit(__a       ):
															UpperCamelCase__       :str							 =			str(__a       )
															UpperCamelCase__       :List[str]							 =			[int(str_num[j:] + str_num[:j]       ) for j in range(len(__a       )       )]
															if all(is_prime(__a       ) for i in list_nums       ):
																				result.append(__a       )
					return result


def       a				(      )  ->     int:



					'''simple docstring'''






					return len(find_circular_primes()       )


if __name__ == "__main__":
		print(F"""{len(find_circular_primes()) = }""")