






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

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     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 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Union[str, Any]  )    ->     Dict:
							if args.student_type == "roberta":
														lowerCamelCase_	:							Union[str, Any]									=False
							elif args.student_type == "gpt2":
														lowerCamelCase_	:							int									=False

def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Optional[Any]  )    ->     Optional[Any]:
							if args.student_type == "roberta":
														lowerCamelCase_	:							Dict									=False

def 			_snake_case    (					)    ->     Optional[int]:
							lowerCamelCase_	:							Any									=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=4_000    ,						help="Checkpoint interval."  )
							lowerCamelCase_	:							Union[str, Any]									=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  )

							lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=MODEL_CLASSES[args.student_type]
							lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							str									=MODEL_CLASSES[args.teacher_type]

							# TOKENIZER #
							lowerCamelCase_	:							Optional[Any]									=teacher_tokenizer_class.from_pretrained(args.teacher_name  )
							lowerCamelCase_	:							Optional[int]									={}
							for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
														lowerCamelCase_	:							List[Any]									=tokenizer.all_special_tokens.index(lowerCamelCase__  )
														lowerCamelCase_	:							Union[str, Any]									=tokenizer.all_special_ids[idx]
							logger.info(F"""Special tokens {special_tok_ids}"""  )
							lowerCamelCase_	:							str									=special_tok_ids
							lowerCamelCase_	:							List[str]									=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_	:							Union[str, Any]									=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:
																					lowerCamelCase_	:							Optional[Any]									=pickle.load(lowerCamelCase__  )

														lowerCamelCase_	:							Optional[Any]									=np.maximum(lowerCamelCase__    ,						1  ) ** -args.mlm_smoothing
														for idx in special_tok_ids.values():
																					lowerCamelCase_	:							Dict									=0.0  # do not predict special tokens
														lowerCamelCase_	:							Dict									=torch.from_numpy(lowerCamelCase__  )
							else:
														lowerCamelCase_	:							List[str]									=None

							lowerCamelCase_	:							int									=LmSeqsDataset(params=lowerCamelCase__    ,						data=lowerCamelCase__  )
							logger.info("Data loader created."  )

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

							if args.student_pretrained_weights is not None:
														logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
														lowerCamelCase_	:							Any									=student_model_class.from_pretrained(args.student_pretrained_weights    ,						config=lowerCamelCase__  )
							else:
														lowerCamelCase_	:							Optional[int]									=student_model_class(lowerCamelCase__  )

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

							# TEACHER #
							lowerCamelCase_	:							List[Any]									=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()
							lowerCamelCase_	:							Dict									=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 ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


A__	:    str			 = logging.get_logger(__name__)







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Optional[Any]       ,	*snake_case__					:  int       ,	**snake_case__					:  Any ):
										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."       ,	snake_case__       ,	)
										super().__init__(*snake_case__       ,	**snake_case__ )



