
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 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''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 						__magic_name__			(   __a      :	Union[str, Any]	,		__a      :	Optional[Any]						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	List[str]	,		__a      :	str						):



     '''simple docstring'''




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






def 						__magic_name__			(   ):



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

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

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

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

     parser.add_argument("""--log_interval"""	,		type=lowerCAmelCase__	,		default=500	,		help="""Tensorboard logging interval."""						)
     parser.add_argument("""--checkpoint_interval"""	,		type=lowerCAmelCase__	,		default=4_000	,		help="""Checkpoint interval."""						)
     UpperCamelCase__				=    parser.parse_args()
     sanity_checks(lowerCAmelCase__						)

     # ARGS #
     init_gpu_params(lowerCAmelCase__						)
     set_seed(lowerCAmelCase__						)
     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(lowerCAmelCase__						)	,		lowerCAmelCase__	,		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(lowerCAmelCase__						)
          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(lowerCAmelCase__						)

     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(lowerCAmelCase__						)

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

     UpperCamelCase__				=    LmSeqsDataset(params=lowerCAmelCase__	,		data=lowerCAmelCase__						)
     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=lowerCAmelCase__						)
     else:
          UpperCamelCase__				=    student_model_class(lowerCAmelCase__						)

     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=lowerCAmelCase__						)
     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(lowerCAmelCase__	,		lowerCAmelCase__						)
     if args.freeze_token_type_embds:
          freeze_token_type_embeddings(lowerCAmelCase__	,		lowerCAmelCase__						)

     # 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=lowerCAmelCase__	,		dataset=lowerCAmelCase__	,		token_probs=lowerCAmelCase__	,		student=lowerCAmelCase__	,		teacher=lowerCAmelCase__						)
     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


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

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





