




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__       (						A__       :					str	):





       '''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__       (						A__       :					str  ,			A__       :					Any	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Any	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						):





       '''simple docstring'''



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

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

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

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

       parser.add_argument("""--log_interval"""  ,			type=_lowercase  ,			default=500  ,			help="""Tensorboard logging interval."""	)
       parser.add_argument("""--checkpoint_interval"""  ,			type=_lowercase  ,			default=4000  ,			help="""Checkpoint interval."""	)
       __lowerCamelCase   =						parser.parse_args()
       sanity_checks(_lowercase	)

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

       __lowerCamelCase   =						MODEL_CLASSES[args.student_type]
       __lowerCamelCase   =						MODEL_CLASSES[args.teacher_type]

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

       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   =						pickle.load(_lowercase	)

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

       __lowerCamelCase   =						LmSeqsDataset(params=_lowercase  ,			data=_lowercase	)
       logger.info("""Data loader created."""	)

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

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

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

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

       # 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   =						Distiller(
           params=_lowercase  ,			dataset=_lowercase  ,			token_probs=_lowercase  ,			student=_lowercase  ,			teacher=_lowercase	)
       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, is_vision_available


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure)
