

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


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

def       __snake_case						( __UpperCamelCase	: str    ):



      """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       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      if args.student_type == "roberta":
            A_						  =				False
      elif args.student_type == "gpt2":
            A_						  =				False

def       __snake_case						( __UpperCamelCase	: int				,__UpperCamelCase	: Any    ):



      """simple docstring"""

      if args.student_type == "roberta":
            A_						  =				False

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				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=__UpperCamelCase				,required=__UpperCamelCase				,help="The output directory (log, checkpoints, parameters, etc.)"    )
      parser.add_argument(
          "--data_file"				,type=__UpperCamelCase				,required=__UpperCamelCase				,help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."				,)

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

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

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

      parser.add_argument("--log_interval"				,type=__UpperCamelCase				,default=500				,help="Tensorboard logging interval."    )
      parser.add_argument("--checkpoint_interval"				,type=__UpperCamelCase				,default=4000				,help="Checkpoint interval."    )
      A_						  =				parser.parse_args()
      sanity_checks(__UpperCamelCase    )

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

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

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

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

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

      A_						  =				LmSeqsDataset(params=__UpperCamelCase				,data=__UpperCamelCase    )
      logger.info("Data loader created."    )

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

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

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

      # TEACHER #
      A_						  =				teacher_model_class.from_pretrained(args.teacher_name				,output_hidden_states=__UpperCamelCase    )
      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(__UpperCamelCase				,__UpperCamelCase    )
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(__UpperCamelCase				,__UpperCamelCase    )

      # 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()
      A_						  =				Distiller(
          params=__UpperCamelCase				,dataset=__UpperCamelCase				,token_probs=__UpperCamelCase				,student=__UpperCamelCase				,teacher=__UpperCamelCase    )
      distiller.train()
      logger.info("Let's go get some drinks."    )


if __name__ == "__main__":
      main()






"""simple docstring"""




class 			__UpperCamelCase							:  # Public class to implement a graph






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a :						int      =							row
								a :						Tuple      =							col
								a :						Optional[int]      =							graph






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					bool:
								return (
								    0 <= i < self.ROW
								    and 0 <= j < self.COL
								    and not visited[i][j]
								    and self.graph[i][j]
								)






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								# Checking all 8 elements surrounding nth element
								a :						Optional[Any]      =							[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
								a :						str      =							[-1, 0, 1, -1, 1, -1, 0, 1]
								a :						str      =							True  # Make those cells visited
								for k in range(8 ):
									if self.is_safe(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ ):
										self.diffs(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					int:  # And finally, count all islands.
								a :						Union[str, Any]      =							[[False for j in range(self.COL )] for i in range(self.ROW )]
								a :						Dict      =							0
								for i in range(self.ROW ):
									for j in range(self.COL ):
										if visited[i][j] is False and self.graph[i][j] == 1:
											self.diffs(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
											count += 1
								return count


