





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





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict       ):
    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 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Any       ):
    if args.student_type == "roberta":
        A__				=			False
    elif args.student_type == "gpt2":
        A__				=			False





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Optional[int]       ):
    if args.student_type == "roberta":
        A__				=			False





def 						UpperCamelCase				(							):
    A__				=			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=0.1_5						,       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=0.0_5						,       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=0.0_2						,       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=5_00						,       help="Tensorboard logging interval."       )
    parser.add_argument("--checkpoint_interval"						,       type=_lowerCamelCase						,       default=40_00						,       help="Checkpoint interval."       )
    A__				=			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       )

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

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

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

    A__				=			LmSeqsDataset(params=_lowerCamelCase						,       data=_lowerCamelCase       )
    logger.info("Data loader created."       )

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

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

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

    # TEACHER #
    A__				=			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()
    A__				=			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()










'''simple docstring'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



