

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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]						)				->  List[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 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Optional[int]						)				->  Tuple:
						if args.student_type == "roberta":
												lowercase :							List[Any]    		=False
						elif args.student_type == "gpt2":
												lowercase :							Dict    		=False






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]   , __magic_name__						:     int						)				->  str:
						if args.student_type == "roberta":
												lowercase :							str    		=False






def 				_lowerCAmelCase			(   )				->  List[Any]:
						lowercase :							int    		=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=__magic_name__   , required=__magic_name__   , help='''The output directory (log, checkpoints, parameters, etc.)'''						)
						parser.add_argument(
						    '''--data_file'''   , type=__magic_name__   , required=__magic_name__   , help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   , )

						parser.add_argument(
						    '''--student_type'''   , type=__magic_name__   , choices=['''distilbert''', '''roberta''', '''gpt2''']   , required=__magic_name__   , help='''The student type (DistilBERT, RoBERTa).'''   , )
						parser.add_argument('''--student_config'''   , type=__magic_name__   , required=__magic_name__   , help='''Path to the student configuration.'''						)
						parser.add_argument(
						    '''--student_pretrained_weights'''   , default=__magic_name__   , type=__magic_name__   , help='''Load student initialization checkpoint.'''						)

						parser.add_argument(
						    '''--teacher_type'''   , choices=['''bert''', '''roberta''', '''gpt2''']   , required=__magic_name__   , help='''Teacher type (BERT, RoBERTa).'''						)
						parser.add_argument('''--teacher_name'''   , type=__magic_name__   , required=__magic_name__   , help='''The teacher model.'''						)

						parser.add_argument('''--temperature'''   , default=2.0   , type=__magic_name__   , help='''Temperature for the softmax temperature.'''						)
						parser.add_argument(
						    '''--alpha_ce'''   , default=0.5   , type=__magic_name__   , help='''Linear weight for the distillation loss. Must be >=0.'''						)
						parser.add_argument(
						    '''--alpha_mlm'''   , default=0.0   , type=__magic_name__   , 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=__magic_name__   , help='''Linear weight for the CLM loss. Must be >=0.'''						)
						parser.add_argument('''--alpha_mse'''   , default=0.0   , type=__magic_name__   , help='''Linear weight of the MSE loss. Must be >=0.'''						)
						parser.add_argument(
						    '''--alpha_cos'''   , default=0.0   , type=__magic_name__   , 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=__magic_name__   , help='''Proportion of tokens for which we need to make a prediction.'''   , )
						parser.add_argument('''--word_mask'''   , default=0.8   , type=__magic_name__   , help='''Proportion of tokens to mask out.'''						)
						parser.add_argument('''--word_keep'''   , default=0.1   , type=__magic_name__   , help='''Proportion of tokens to keep.'''						)
						parser.add_argument('''--word_rand'''   , default=0.1   , type=__magic_name__   , help='''Proportion of tokens to randomly replace.'''						)
						parser.add_argument(
						    '''--mlm_smoothing'''   , default=0.7   , type=__magic_name__   , help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   , )
						parser.add_argument('''--token_counts'''   , type=__magic_name__   , 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=__magic_name__   , default=3   , help='''Number of pass on the whole dataset.'''						)
						parser.add_argument('''--batch_size'''   , type=__magic_name__   , 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=__magic_name__   , default=50   , help='''Gradient accumulation for larger training batches.'''   , )
						parser.add_argument('''--warmup_prop'''   , default=0.0_5   , type=__magic_name__   , help='''Linear warmup proportion.'''						)
						parser.add_argument('''--weight_decay'''   , default=0.0   , type=__magic_name__   , help='''Weight decay if we apply some.'''						)
						parser.add_argument('''--learning_rate'''   , default=5E-4   , type=__magic_name__   , help='''The initial learning rate for Adam.'''						)
						parser.add_argument('''--adam_epsilon'''   , default=1E-6   , type=__magic_name__   , help='''Epsilon for Adam optimizer.'''						)
						parser.add_argument('''--max_grad_norm'''   , default=5.0   , type=__magic_name__   , help='''Max gradient norm.'''						)
						parser.add_argument('''--initializer_range'''   , default=0.0_2   , type=__magic_name__   , 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=__magic_name__   , 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=__magic_name__   , default=1   , help='''Number of GPUs in the node.'''						)
						parser.add_argument('''--local_rank'''   , type=__magic_name__   , default=-1   , help='''Distributed training - Local rank'''						)
						parser.add_argument('''--seed'''   , type=__magic_name__   , default=56   , help='''Random seed'''						)

						parser.add_argument('''--log_interval'''   , type=__magic_name__   , default=500   , help='''Tensorboard logging interval.'''						)
						parser.add_argument('''--checkpoint_interval'''   , type=__magic_name__   , default=4000   , help='''Checkpoint interval.'''						)
						lowercase :							Dict    		=parser.parse_args()
						sanity_checks(__magic_name__						)

						# ARGS #
						init_gpu_params(__magic_name__						)
						set_seed(__magic_name__						)
						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(__magic_name__						)   , __magic_name__   , indent=4						)
												git_log(args.dump_path						)

						lowercase		,	lowercase		,	lowercase :							Optional[int]    		=MODEL_CLASSES[args.student_type]
						lowercase		,	lowercase		,	lowercase :							str    		=MODEL_CLASSES[args.teacher_type]

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

						if args.mlm:
												logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''						)
												with open(args.token_counts   , '''rb'''						) as fp:
																		lowercase :							Optional[Any]    		=pickle.load(__magic_name__						)

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

						lowercase :							int    		=LmSeqsDataset(params=__magic_name__   , data=__magic_name__						)
						logger.info('''Data loader created.'''						)

						# STUDENT #
						logger.info(f'''Loading student config from {args.student_config}'''						)
						lowercase :							Optional[int]    		=student_config_class.from_pretrained(args.student_config						)
						lowercase :							List[Any]    		=True

						if args.student_pretrained_weights is not None:
												logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
												lowercase :							Optional[int]    		=student_model_class.from_pretrained(args.student_pretrained_weights   , config=__magic_name__						)
						else:
												lowercase :							int    		=student_model_class(__magic_name__						)

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

						# TEACHER #
						lowercase :							List[Any]    		=teacher_model_class.from_pretrained(args.teacher_name   , output_hidden_states=__magic_name__						)
						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(__magic_name__   , __magic_name__						)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(__magic_name__   , __magic_name__						)

						# 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()
						lowercase :							Optional[Any]    		=Distiller(
						    params=__magic_name__   , dataset=__magic_name__   , token_probs=__magic_name__   , student=__magic_name__   , teacher=__magic_name__						)
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''						)


if __name__ == "__main__":
							main()








'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


UpperCamelCase_    						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_    						=       """cuda""" if torch.cuda.is_available() else """cpu"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  dict:
						lowercase		,	lowercase :							int    		=[], []
						for title, text in zip(documents['''title''']   , documents['''text''']						):
												if text is not None:
																		for passage in split_text(__magic_name__						):
																								titles.append(title if title is not None else ''''''						)
																								texts.append(__magic_name__						)
						return {"title": titles, "text": texts}






def 				_lowerCAmelCase			(   __magic_name__						:     dict   , __magic_name__						:     DPRContextEncoder   , __magic_name__						:     DPRContextEncoderTokenizerFast						)				->  dict:
						lowercase :							Dict    		=ctx_tokenizer(
						    documents['''title''']   , documents['''text''']   , truncation=__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)['''input_ids''']
						lowercase :							Optional[int]    		=ctx_encoder(input_ids.to(device=__magic_name__						)   , return_dict=__magic_name__						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						logger.info('''Step 1 - Create the dataset'''						)
						######################################

						# The dataset needed for RAG must have three columns:
						# - title (string): title of the document
						# - text (string): text of a passage of the document
						# - embeddings (array of dimension d): DPR representation of the passage
						# Let's say you have documents in tab-separated csv files with columns "title" and "text"
						assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

						# You can load a Dataset object this way
						lowercase :							Tuple    		=load_dataset(
						    '''csv'''   , data_files=[rag_example_args.csv_path]   , split='''train'''   , delimiter='''\t'''   , column_names=['''title''', '''text''']						)

						# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

						# Then split the documents into passages of 100 words
						lowercase :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

						# And compute the embeddings
						lowercase :							Any    		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__magic_name__						)
						lowercase :							Any    		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						lowercase :							Optional[int]    		=Features(
						    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
						lowercase :							Optional[Any]    		=dataset.map(
						    partial(__magic_name__   , ctx_encoder=__magic_name__   , ctx_tokenizer=__magic_name__						)   , batched=__magic_name__   , batch_size=processing_args.batch_size   , features=__magic_name__   , )

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# from datasets import load_from_disk
						# dataset = load_from_disk(passages_path)  # to reload the dataset

						######################################
						logger.info('''Step 2 - Index the dataset'''						)
						######################################

						# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
						lowercase :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

						# And save the index
						lowercase :							Dict    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset_hnsw_index.faiss'''						)
						dataset.get_index('''embeddings'''						).save(__magic_name__						)
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      )						,							metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/rag-sequence-nq'						,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/dpr-ctx_encoder-multiset-base'						,							metadata={
				        'help': (
				            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
				            ' \'facebook/dpr-ctx_encoder-multiset-base\''
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb'      )						,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=16						,							metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The number of bi-directional links created for every new element during the HNSW index construction.'
				        )
				    }						,							)


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							UpperCamelCase_    						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														UpperCamelCase_    						=       rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





