





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





def lowerCAmelCase__(__snake_case							)				->      List[Any]:







	'''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__(__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__()				->      Optional[int]:







	'''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=__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=0.1_5       ,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=0.0_5       ,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=0.0_2       ,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.'''							)
	lowerCamelCase__								=		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							)

	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		MODEL_CLASSES[args.student_type]
	lowerCamelCase__				,	lowerCamelCase__				,	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(__snake_case							)
		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(__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:
			lowerCamelCase__								=		pickle.load(__snake_case							)

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

	lowerCamelCase__								=		LmSeqsDataset(params=__snake_case       ,data=__snake_case							)
	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=__snake_case							)
	else:
		lowerCamelCase__								=		student_model_class(__snake_case							)

	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=__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()
	lowerCamelCase__								=		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()










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""yolos"""





			def __init__(    self       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=[5_1_2, 8_6_4]       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=1_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.1       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)

				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		qkv_bias
				lowerCamelCase__								=		num_detection_tokens
				lowerCamelCase__								=		use_mid_position_embeddings
				lowerCamelCase__								=		auxiliary_loss
				# Hungarian matcher
				lowerCamelCase__								=		class_cost
				lowerCamelCase__								=		bbox_cost
				lowerCamelCase__								=		giou_cost
				# Loss coefficients
				lowerCamelCase__								=		bbox_loss_coefficient
				lowerCamelCase__								=		giou_loss_coefficient
				lowerCamelCase__								=		eos_coefficient






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						version.parse("""1.11""" )





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-4





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_2



