


"""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


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







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							if args.student_type == "roberta":
														snake_case_           =		False
							elif args.student_type == "gpt2":
														snake_case_           =		False







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							if args.student_type == "roberta":
														snake_case_           =		False







def 						_a				(      )    ->				Union[str, Any]:
							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=__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=50				, 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=56				, help="""Random seed"""				)

							parser.add_argument("""--log_interval"""				, type=__A				, default=500				, help="""Tensorboard logging interval."""				)
							parser.add_argument("""--checkpoint_interval"""				, type=__A				, default=4_000				, help="""Checkpoint interval."""				)
							snake_case_           =		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				)

							snake_case_           =		MODEL_CLASSES[args.student_type]
							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(__A				)
														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(__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:
																					snake_case_           =		pickle.load(__A				)

														snake_case_           =		np.maximum(__A				, 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(__A				)
							else:
														snake_case_           =		None

							snake_case_           =		LmSeqsDataset(params=__A				, data=__A				)
							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=__A				)
							else:
														snake_case_           =		student_model_class(__A				)

							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=__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()
							snake_case_           =		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 __future__ import annotations







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				list[int]:
							snake_case_           =		0
							snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1

							while i < j:
														if nums[i] + nums[j] == target:
																					return [i, j]
														elif nums[i] + nums[j] < target:
																					snake_case_           =		i + 1
														else:
																					snake_case_           =		j - 1

							return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")



