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






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							if args.student_type == "roberta":
														_snake_case  =    False
							elif args.student_type == "gpt2":
														_snake_case  =    False






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							if args.student_type == "roberta":
														_snake_case  =    False






def    __SCREAMING_SNAKE_CASE (    ):
							_snake_case  =    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.""" )
							_snake_case  =    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 )

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

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

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

							_snake_case  =    LmSeqsDataset(params=__snake_case				,   data=__snake_case )
							logger.info("""Data loader created.""" )

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

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

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

							# TEACHER #
							_snake_case  =    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()
							_snake_case  =    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()







import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






