

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


lowercase_   							=  {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def 	a (     A__      :  List[str]	)       -> 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__      :  int ,  A__      :  str	)       -> Optional[int]:





      """simple docstring"""



      if args.student_type == "roberta":
            _lowercase      			=False
      elif args.student_type == "gpt2":
            _lowercase      			=False
def 	a (     A__      :  Tuple ,  A__      :  Tuple	)       -> Tuple:





      """simple docstring"""



      if args.student_type == "roberta":
            _lowercase      			=False
def 	a (     )       -> str:





      """simple docstring"""



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

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

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

      _lowercase      			=LmSeqsDataset(params=A__ ,  data=A__	)
      logger.info('Data loader created.'	)

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

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

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

      # TEACHER #
      _lowercase      			=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      			=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()






from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

