




'''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


lowerCamelCase_			=						{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			str:
   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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Dict  )			->			List[Any]:
   if args.student_type == "roberta":
      _SCREAMING_SNAKE_CASE	=				False
   elif args.student_type == "gpt2":
      _SCREAMING_SNAKE_CASE	=				False
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			List[str]  )			->			Any:
   if args.student_type == "roberta":
      _SCREAMING_SNAKE_CASE	=				False
def 			SCREAMING_SNAKE_CASE_			(							)			->			Any:
   _SCREAMING_SNAKE_CASE	=				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=5_00	,					help="Tensorboard logging interval."  )
   parser.add_argument("--checkpoint_interval"	,					type=__A	,					default=40_00	,					help="Checkpoint interval."  )
   _SCREAMING_SNAKE_CASE	=				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  )

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

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

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

   _SCREAMING_SNAKE_CASE	=				LmSeqsDataset(params=__A	,					data=__A  )
   logger.info("Data loader created."  )

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

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

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

   # TEACHER #
   _SCREAMING_SNAKE_CASE	=				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()
   _SCREAMING_SNAKE_CASE	=				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'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''linear'''
   lowerCamelCase_      =						'''cosine'''
   lowerCamelCase_      =						'''cosine_with_restarts'''
   lowerCamelCase_      =						'''polynomial'''
   lowerCamelCase_      =						'''constant'''
   lowerCamelCase_      =						'''constant_with_warmup'''
   lowerCamelCase_      =						'''piecewise_constant'''


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int = -1  )			->			Optional[int]:
   return LambdaLR(__A	,					lambda __A  : 1	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int = -1  )			->			Dict:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1.0	,					__A  )  )
      return 1.0

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			str	,					__A    :			int = -1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				{}
   _SCREAMING_SNAKE_CASE	=				step_rules.split(","  )
   for rule_str in rule_list[:-1]:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				rule_str.split(":"  )
      _SCREAMING_SNAKE_CASE	=				int(__A  )
      _SCREAMING_SNAKE_CASE	=				float(__A  )
      _SCREAMING_SNAKE_CASE	=				value
   _SCREAMING_SNAKE_CASE	=				float(rule_list[-1]  )

   def create_rules_function(__A    :			Tuple	,					__A    :			List[Any]  ):
      def rule_func(__A    :			int  ) -> float:
         _SCREAMING_SNAKE_CASE	=				sorted(rules_dict.keys()  )
         for i, sorted_step in enumerate(__A  ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   _SCREAMING_SNAKE_CASE	=				create_rules_function(__A	,					__A  )

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Union[str, Any]=-1  )			->			str:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      return max(
          0.0	,					float(num_training_steps - current_step  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			float = 0.5	,					__A    :			int = -1  )			->			Any:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				float(current_step - num_warmup_steps  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )
      return max(0.0	,					0.5 * (1.0 + math.cos(math.pi * float(__A  ) * 2.0 * progress  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			int = 1	,					__A    :			int = -1  )			->			str:

   def lr_lambda(__A    :			Optional[int]  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				float(current_step - num_warmup_steps  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )
      if progress >= 1.0:
         return 0.0
      return max(0.0	,					0.5 * (1.0 + math.cos(math.pi * ((float(__A  ) * progress) % 1.0)  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Any=1e-7	,					__A    :			Optional[Any]=1.0	,					__A    :			List[Any]=-1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				optimizer.defaults["lr"]
   if not (lr_init > lr_end):
      raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""  )

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         _SCREAMING_SNAKE_CASE	=				lr_init - lr_end
         _SCREAMING_SNAKE_CASE	=				num_training_steps - num_warmup_steps
         _SCREAMING_SNAKE_CASE	=				1 - (current_step - num_warmup_steps) / decay_steps
         _SCREAMING_SNAKE_CASE	=				lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__A	,					__A	,					__A  )


lowerCamelCase_			=						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, SchedulerType]	,					__A    :			Optimizer	,					__A    :			Optional[str] = None	,					__A    :			Optional[int] = None	,					__A    :			Optional[int] = None	,					__A    :			int = 1	,					__A    :			float = 1.0	,					__A    :			int = -1	,					)			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				SchedulerType(__A  )
   _SCREAMING_SNAKE_CASE	=				TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__A	,					last_epoch=__A  )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(__A	,					step_rules=__A	,					last_epoch=__A  )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""  )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(__A	,					num_warmup_steps=__A	,					last_epoch=__A  )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""  )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					num_cycles=__A	,					last_epoch=__A	,					)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					power=__A	,					last_epoch=__A	,					)

   return schedule_func(
       __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					last_epoch=__A  )

