'''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


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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Tuple:


     '''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      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			List[str]      )   ->				Tuple:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




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

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

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

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

     parser.add_argument("--log_interval" ,   type=snake_case_ ,   default=5_00 ,   help="Tensorboard logging interval."      )
     parser.add_argument("--checkpoint_interval" ,   type=snake_case_ ,   default=40_00 ,   help="Checkpoint interval."      )
     UpperCAmelCase_	   =       parser.parse_args()
     sanity_checks(snake_case_      )

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

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

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

     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_	   =       pickle.load(snake_case_      )

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

     UpperCAmelCase_	   =       LmSeqsDataset(params=snake_case_ ,   data=snake_case_      )
     logger.info("Data loader created."      )

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

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

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

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

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


if __name__ == "__main__":
    main()





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




