

"""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


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





def       lowercase__						(       snake_case_   :Union[str, 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       lowercase__						(       snake_case_   :int			,		snake_case_   :Dict    ):
	if args.student_type == "roberta":
		__UpperCAmelCase      		= False
	elif args.student_type == "gpt2":
		__UpperCAmelCase      		= False





def       lowercase__						(       snake_case_   :Optional[int]			,		snake_case_   :Union[str, Any]    ):
	if args.student_type == "roberta":
		__UpperCAmelCase      		= False





def       lowercase__						(       ):
	__UpperCAmelCase      		= 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=4_000			,		help='''Checkpoint interval.'''    )
	__UpperCAmelCase      		= 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    )

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

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

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

	__UpperCAmelCase      		= LmSeqsDataset(params=snake_case_			,		data=snake_case_    )
	logger.info('''Data loader created.'''    )

	# STUDENT #
	logger.info(F'''Loading student config from {args.student_config}'''    )
	__UpperCAmelCase      		= student_config_class.from_pretrained(args.student_config    )
	__UpperCAmelCase      		= True

	if args.student_pretrained_weights is not None:
		logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''    )
		__UpperCAmelCase      		= student_model_class.from_pretrained(args.student_pretrained_weights			,		config=snake_case_    )
	else:
		__UpperCAmelCase      		= student_model_class(snake_case_    )

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

	# TEACHER #
	__UpperCAmelCase      		= 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()
	__UpperCAmelCase      		= 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()





"""simple docstring"""







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=    logging.get_logger(__name__)

a_							=    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			List[Any]												=			"""xlm-prophetnet"""
			a_       :			Any												=			["""past_key_values"""]
			a_       :			Optional[int]												=			{
			    """num_attention_heads""": """num_encoder_attention_heads""",
			}
			def __init__(	self				,   A = 0.1				,   A = "gelu"				,   A = 3_0522				,   A = 1024				,   A = 4096				,   A = 12				,   A = 16				,   A = 4096				,   A = 12				,   A = 16				,   A = 0.1				,   A = 0.1				,   A = 512				,   A = 0.0_2				,   A = True				,   A = True				,   A = 0				,   A = 2				,   A = 32				,   A = 128				,   A = False				,   A = 0.0				,   A = True				,   A = 0				,   A = 1				,   A = 2				,   **A				,   ):
						_lowerCamelCase					:					Dict	       =    vocab_size
						_lowerCamelCase					:					List[Any]	       =    hidden_size
						_lowerCamelCase					:					List[Any]	       =    encoder_ffn_dim
						_lowerCamelCase					:					Any	       =    num_encoder_layers
						_lowerCamelCase					:					Any	       =    num_encoder_attention_heads
						_lowerCamelCase					:					List[str]	       =    decoder_ffn_dim
						_lowerCamelCase					:					Optional[int]	       =    num_decoder_layers
						_lowerCamelCase					:					List[str]	       =    num_decoder_attention_heads
						_lowerCamelCase					:					Optional[Any]	       =    max_position_embeddings
						_lowerCamelCase					:					Optional[Any]	       =    init_std  # Normal(0, this parameter)
						_lowerCamelCase					:					Optional[int]	       =    activation_function

						# parameters for xlmprophetnet
						_lowerCamelCase					:					Any	       =    ngram
						_lowerCamelCase					:					Dict	       =    num_buckets
						_lowerCamelCase					:					Dict	       =    relative_max_distance
						_lowerCamelCase					:					Optional[Any]	       =    disable_ngram_loss
						_lowerCamelCase					:					Union[str, Any]	       =    eps

						# 3 Types of Dropout
						_lowerCamelCase					:					int	       =    attention_dropout
						_lowerCamelCase					:					str	       =    activation_dropout
						_lowerCamelCase					:					Optional[int]	       =    dropout

						_lowerCamelCase					:					List[Any]	       =    use_cache

						super().__init__(
						    pad_token_id=A				,   bos_token_id=A				,   eos_token_id=A				,   is_encoder_decoder=A				,   add_cross_attention=A				,   decoder_start_token_id=A				,   **A				,   )
			@property
			def    _lowerCAmelCase		(	self							):
						return self.num_encoder_layers + self.num_decoder_layers

			@num_hidden_layers.setter
			def    _lowerCAmelCase		(	self				,   A							):
						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'							)





