





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






def     lowerCamelCase__			(__lowerCamelCase	):
     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__			(__lowerCamelCase,       __lowerCamelCase	):
     if args.student_type == "roberta":
          _SCREAMING_SNAKE_CASE       :	int						    =							False
     elif args.student_type == "gpt2":
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							False






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     if args.student_type == "roberta":
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							False






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	str						    =							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=500,       help="Tensorboard logging interval."	)
     parser.add_argument("--checkpoint_interval",       type=_snake_case,       default=4000,       help="Checkpoint interval."	)
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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	)

     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							MODEL_CLASSES[args.student_type]
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							MODEL_CLASSES[args.teacher_type]

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

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

     _SCREAMING_SNAKE_CASE       :	List[str]						    =							LmSeqsDataset(params=_snake_case,       data=_snake_case	)
     logger.info("Data loader created."	)

     # STUDENT #
     logger.info(f"""Loading student config from {args.student_config}"""	)
     _SCREAMING_SNAKE_CASE       :	int						    =							student_config_class.from_pretrained(args.student_config	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							True

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

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

     # TEACHER #
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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()
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							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()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)