

"""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


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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	if args.student_type == "roberta":
		lowerCAmelCase__	:						Optional[Any]  				=	False
	elif args.student_type == "gpt2":
		lowerCAmelCase__	:						List[str]  				=	False

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	if args.student_type == "roberta":
		lowerCAmelCase__	:						Optional[int]  				=	False

def   __SCREAMING_SNAKE_CASE  (      ):
	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=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=5_00						,     help='''Tensorboard logging interval.''' )
	parser.add_argument('''--checkpoint_interval'''						,     type=A_						,     default=40_00						,     help='''Checkpoint interval.''' )
	lowerCAmelCase__	:						Optional[Any]  				=	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 )

	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	MODEL_CLASSES[args.student_type]
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	MODEL_CLASSES[args.teacher_type]

	# TOKENIZER #
	lowerCAmelCase__	:						Tuple  				=	teacher_tokenizer_class.from_pretrained(args.teacher_name )
	lowerCAmelCase__	:						Dict  				=	{}
	for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
		lowerCAmelCase__	:						Tuple  				=	tokenizer.all_special_tokens.index(A_ )
		lowerCAmelCase__	:						Tuple  				=	tokenizer.all_special_ids[idx]
	logger.info(f'Special tokens {special_tok_ids}' )
	lowerCAmelCase__	:						Optional[int]  				=	special_tok_ids
	lowerCAmelCase__	:						int  				=	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__	:						Optional[int]  				=	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:
			lowerCAmelCase__	:						Any  				=	pickle.load(A_ )

		lowerCAmelCase__	:						Any  				=	np.maximum(A_						,     1 ) ** -args.mlm_smoothing
		for idx in special_tok_ids.values():
			lowerCAmelCase__	:						Tuple  				=	0.0  # do not predict special tokens
		lowerCAmelCase__	:						List[Any]  				=	torch.from_numpy(A_ )
	else:
		lowerCAmelCase__	:						int  				=	None

	lowerCAmelCase__	:						List[str]  				=	LmSeqsDataset(params=A_						,     data=A_ )
	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__	:						Union[str, Any]  				=	student_model_class.from_pretrained(args.student_pretrained_weights						,     config=A_ )
	else:
		lowerCAmelCase__	:						int  				=	student_model_class(A_ )

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

	# TEACHER #
	lowerCAmelCase__	:						List[str]  				=	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()
	lowerCAmelCase__	:						Optional[Any]  				=	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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




