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		:       Optional[Any]									=     {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def       A	(    _lowercase						):
						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	(    _lowercase		,  _lowercase						):
						if args.student_type == "roberta":
												SCREAMING_SNAKE_CASE							:    List[Any]							=							False
						elif args.student_type == "gpt2":
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							False
def       A	(    _lowercase		,  _lowercase						):
						if args.student_type == "roberta":
												SCREAMING_SNAKE_CASE							:    Tuple							=							False
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[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=_lowercase		,  required=_lowercase		,  help='''The output directory (log, checkpoints, parameters, etc.)'''						)
						parser.add_argument(
						    '''--data_file'''		,  type=_lowercase		,  required=_lowercase		,  help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''		,  )

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

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

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

						parser.add_argument('''--log_interval'''		,  type=_lowercase		,  default=500		,  help='''Tensorboard logging interval.'''						)
						parser.add_argument('''--checkpoint_interval'''		,  type=_lowercase		,  default=4_000		,  help='''Checkpoint interval.'''						)
						SCREAMING_SNAKE_CASE							:    int							=							parser.parse_args()
						sanity_checks(_lowercase						)

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

						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							MODEL_CLASSES[args.student_type]
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							MODEL_CLASSES[args.teacher_type]

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

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

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

						SCREAMING_SNAKE_CASE							:    Tuple							=							LmSeqsDataset(params=_lowercase		,  data=_lowercase						)
						logger.info('''Data loader created.'''						)

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

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							student_model_class.from_pretrained(args.student_pretrained_weights		,  config=_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    str							=							student_model_class(_lowercase						)

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

						# TEACHER #
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							teacher_model_class.from_pretrained(args.teacher_name		,  output_hidden_states=_lowercase						)
						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(_lowercase		,  _lowercase						)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(_lowercase		,  _lowercase						)

						# 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()
						SCREAMING_SNAKE_CASE							:    int							=							Distiller(
						    params=_lowercase		,  dataset=_lowercase		,  token_probs=_lowercase		,  student=_lowercase		,  teacher=_lowercase						)
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''						)


if __name__ == "__main__":
					main()






import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





