


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






def 			lowercase				(_A					):







       """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 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       if args.student_type == "roberta":
              _lowerCAmelCase    :							Tuple   			=       False
       elif args.student_type == "gpt2":
              _lowerCAmelCase    :							Tuple   			=       False






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       if args.student_type == "roberta":
              _lowerCAmelCase    :							Tuple   			=       False






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       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=5_0   ,						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=5_6   ,						help='Random seed'					)

       parser.add_argument('--log_interval'   ,						type=_A   ,						default=5_0_0   ,						help='Tensorboard logging interval.'					)
       parser.add_argument('--checkpoint_interval'   ,						type=_A   ,						default=4_0_0_0   ,						help='Checkpoint interval.'					)
       _lowerCAmelCase    :							Tuple   			=       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					)

       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       MODEL_CLASSES[args.student_type]
       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       MODEL_CLASSES[args.teacher_type]

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

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

       _lowerCAmelCase    :							List[str]   			=       LmSeqsDataset(params=_A   ,						data=_A					)
       logger.info('Data loader created.'					)

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

       if args.student_pretrained_weights is not None:
              logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'					)
              _lowerCAmelCase    :							Optional[int]   			=       student_model_class.from_pretrained(args.student_pretrained_weights   ,						config=_A					)
       else:
              _lowerCAmelCase    :							Optional[Any]   			=       student_model_class(_A					)

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

       # TEACHER #
       _lowerCAmelCase    :							int   			=       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()
       _lowerCAmelCase    :							int   			=       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'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase    :     Any					=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XCLIPModel""",
           """XCLIPPreTrainedModel""",
           """XCLIPTextModel""",
           """XCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       lowerCAmelCase    :     Dict					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

