


"""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


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_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=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=4000						,		help="Checkpoint interval."     )
					__SCREAMING_SNAKE_CASE		     =       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     )

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

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

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

					__SCREAMING_SNAKE_CASE		     =       LmSeqsDataset(params=lowerCAmelCase_						,		data=lowerCAmelCase_     )
					logger.info("Data loader created."     )

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

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

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

					# TEACHER #
					__SCREAMING_SNAKE_CASE		     =       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()
					__SCREAMING_SNAKE_CASE		     =       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 functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
