

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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_    =					logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_    =					'''cuda''' if torch.cuda.is_available() else '''cpu'''



def    UpperCamelCase(					lowercase_			,					lowercase_=100			,					lowercase_=" "							)	->				List[str]:





  '''simple docstring'''





  snake_case_       						=      text.split(lowercase_							)
  return [character.join(text[i : i + n]							).strip() for i in range(0			,					len(lowercase_							)			,					lowercase_							)]



def    UpperCamelCase(					lowercase_							)	->				dict:





  '''simple docstring'''





  snake_case_						,				snake_case_       						=      [], []
  for title, text in zip(documents["""title"""]			,					documents["""text"""]							):
    if text is not None:
      for passage in split_text(lowercase_							):
        titles.append(title if title is not None else """"""							)
        texts.append(lowercase_							)
  return {"title": titles, "text": texts}



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_							)	->				dict:





  '''simple docstring'''





  snake_case_       						=      ctx_tokenizer(
      documents["""title"""]			,					documents["""text"""]			,					truncation=lowercase_			,					padding="""longest"""			,					return_tensors="""pt"""							)["""input_ids"""]
  snake_case_       						=      ctx_encoder(input_ids.to(device=lowercase_							)			,					return_dict=lowercase_							).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					)	->				List[str]:





  '''simple docstring'''





  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
  snake_case_       						=      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
  snake_case_       						=      dataset.map(lowercase_			,					batched=lowercase_			,					num_proc=processing_args.num_proc							)

  # And compute the embeddings
  snake_case_       						=      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							).to(device=lowercase_							)
  snake_case_       						=      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							)
  snake_case_       						=      Features(
      {"""text""": Value("""string"""							), """title""": Value("""string"""							), """embeddings""": Sequence(Value("""float32"""							)							)}							)  # optional, save as float32 instead of float64 to save space
  snake_case_       						=      dataset.map(
      partial(lowercase_			,					ctx_encoder=lowercase_			,					ctx_tokenizer=lowercase_							)			,					batched=lowercase_			,					batch_size=processing_args.batch_size			,					features=lowercase_			,					)

  # And finally save your dataset
  snake_case_       						=      os.path.join(rag_example_args.output_dir			,					"""my_knowledge_dataset"""							)
  dataset.save_to_disk(lowercase_							)
  # 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
  snake_case_       						=      faiss.IndexHNSWFlat(index_hnsw_args.d			,					index_hnsw_args.m			,					faiss.METRIC_INNER_PRODUCT							)
  dataset.add_faiss_index("""embeddings"""			,					custom_index=lowercase_							)

  # And save the index
  snake_case_       						=      os.path.join(rag_example_args.output_dir			,					"""my_knowledge_dataset_hnsw_index.faiss"""							)
  dataset.get_index("""embeddings"""							).save(lowercase_							)
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

  ######################################
  logger.info("""Step 3 - Load RAG"""							)
  ######################################

  # Easy way to load the model
  snake_case_       						=      RagRetriever.from_pretrained(
      rag_example_args.rag_model_name			,					index_name="""custom"""			,					indexed_dataset=lowercase_							)
  snake_case_       						=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,					retriever=lowercase_							)
  snake_case_       						=      RagTokenizer.from_pretrained(rag_example_args.rag_model_name							)

  # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
  # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

  ######################################
  logger.info("""Step 4 - Have fun"""							)
  ######################################

  snake_case_       						=      rag_example_args.question or """What does Moses' rod turn into ?"""
  snake_case_       						=      tokenizer.question_encoder(lowercase_			,					return_tensors="""pt"""							)["""input_ids"""]
  snake_case_       						=      model.generate(lowercase_							)
  snake_case_       						=      tokenizer.batch_decode(lowercase_			,					skip_special_tokens=lowercase_							)[0]
  logger.info("""Q: """ + question							)
  logger.info("""A: """ + generated_string							)







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      str          =					field(
          default=str(Path(__snake_case	).parent / 'test_data' / 'my_knowledge_dataset.csv'	)  ,							metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}  ,							)
      lowerCamelCase_	:      Optional[str]          =					field(
          default=__snake_case  ,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}  ,							)
      lowerCamelCase_	:      str          =					field(
          default='facebook/rag-sequence-nq'  ,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}  ,							)
      lowerCamelCase_	:      str          =					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_	:      Optional[str]          =					field(
          default=__snake_case  ,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}  ,							)







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







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      int          =					field(
          default=768  ,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}  ,							)
      lowerCamelCase_	:      int          =					field(
          default=128  ,							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)

    lowerCamelCase_    =					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_    =					parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        lowerCamelCase_    =					rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_lowerCamelCase    :      Tuple	 =						'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

_lowerCamelCase    :      Optional[Any]	 =						logging.getLogger(__name__)


@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""roberta-large-mnli""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )
							lowercase__  :				Optional[str]									=							field(
							    default="""This example is {}.""" ,				metadata={
							        """help""": (
							            """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
							            """where class name is inserted."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							field(
							    default=32 ,				metadata={"""help""": """Batch size for generating teacher predictions."""}   )
							lowercase__  :				Optional[bool]									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    } ,				)
							lowercase__  :				Optional[float]									=							field(
							    default=1.0 ,				metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}   )



@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""distilbert-base-uncased""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )



@dataclass
class   lowercase			:
							lowercase__  :				str									=							field(metadata={"""help""": """Text file with one unlabeled instance per line."""}   )
							lowercase__  :				str									=							field(metadata={"""help""": """Text file with one class name per line."""}   )
							lowercase__  :				bool									=							field(
							    default=a ,				metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""} ,				)



@dataclass
class   lowercase			(   a   ):
							lowercase__  :				Optional[str]									=							field(
							    default=a ,				metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} ,				)
							lowercase__  :				int									=							field(
							    default=32 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}   )
							lowercase__  :				int									=							field(
							    default=128 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}   )
							lowercase__  :				float									=							field(default=1.0 ,				metadata={"""help""": """Total number of training epochs to perform."""}   )
							lowercase__  :				bool									=							field(default=a ,				metadata={"""help""": """Whether to run training of student model."""}   )
							lowercase__  :				bool									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
							            """after training."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							field(
							    default=0 ,				metadata={
							        """help""": (
							            """Limit the total amount of checkpoints."""
							            """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
							        )
							    } ,				)



class   lowercase			(   a   ):




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				inputs["labels"]
												SCREAMING_SNAKE_CASE				=				model(inputs["input_ids"]		,							attention_mask=inputs["attention_mask"]					)
												SCREAMING_SNAKE_CASE				=				outputs[0]

												SCREAMING_SNAKE_CASE				=				-torch.sum(target_p * logits.log_softmax(dim=-1					)		,							axis=-1					).mean()

												if return_outputs:
																	return loss, outputs

												return loss





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					with open(UpperCAmelCase__					,       "r"     ) as f:
										for line in f:
															SCREAMING_SNAKE_CASE				=				line.strip()
															if len(UpperCAmelCase__     ) > 0:
																				lines.append(UpperCAmelCase__     )
					return lines





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				[]
					for example in examples:
										for name in class_names:
															premises.append(UpperCAmelCase__     )
															hypotheses.append(hypothesis_template.format(UpperCAmelCase__     )     )
					return premises, hypotheses





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]     ):
					for label, ind in config.labelaid.items():
										if label.lower().startswith("entail"     ):
															return ind
					logger.warning("Could not identify entailment dimension from teacher config label2id. Setting to -1."     )
					return -1





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       ):
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				model.config
					if not no_cuda and torch.cuda.is_available():
										SCREAMING_SNAKE_CASE				=				nn.DataParallel(model.cuda()     )
										batch_size *= len(model.device_ids     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(UpperCAmelCase__					,       use_fast=UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				get_premise_hypothesis_pairs(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				[]

					for i in tqdm(range(0					,       len(UpperCAmelCase__     )					,       UpperCAmelCase__     )     ):
										SCREAMING_SNAKE_CASE				=				premises[i : i + batch_size]
										SCREAMING_SNAKE_CASE				=				hypotheses[i : i + batch_size]

										SCREAMING_SNAKE_CASE				=				tokenizer(
										    UpperCAmelCase__					,       UpperCAmelCase__					,       padding=UpperCAmelCase__					,       truncation="only_first"					,       return_tensors="pt"					,       )

										with torch.cuda.amp.autocast(enabled=UpperCAmelCase__     ):
															with torch.no_grad():
																				SCREAMING_SNAKE_CASE				=				model(**UpperCAmelCase__     )
										logits.append(outputs.logits.detach().cpu().float()     )

					SCREAMING_SNAKE_CASE				=				get_entailment_id(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				-1 if entail_id == 0 else 0
					SCREAMING_SNAKE_CASE				=				torch.cat(UpperCAmelCase__					,       dim=0     )  # N*K x 3
					SCREAMING_SNAKE_CASE				=				logits.reshape(len(UpperCAmelCase__     )					,       len(UpperCAmelCase__     )					,       -1     )[..., [contr_id, entail_id]]  # N x K x 2

					if multi_label:
										# softmax over (contr, entail) logits for each class independently
										SCREAMING_SNAKE_CASE				=				(nli_logits / temperature).softmax(-1     )
					else:
										# softmax over entail logits across classes s.t. class probabilities sum to 1.
										SCREAMING_SNAKE_CASE				=				(nli_logits / temperature).softmax(1     )

					return nli_prob[..., 1]  # N x K





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				HfArgumentParser(
					    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,       description=UpperCAmelCase__					,       )

					if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_json_file(
										    json_file=os.path.abspath(sys.argv[1]     )     )
					else:
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					SCREAMING_SNAKE_CASE				=				None
					if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
										SCREAMING_SNAKE_CASE				=				get_last_checkpoint(training_args.output_dir     )
										if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
															raise ValueError(
															    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
															    "Use --overwrite_output_dir to overcome."     )
										elif last_checkpoint is not None:
															logger.info(
															    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
															    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,       datefmt="%m/%d/%Y %H:%M:%S"					,       handlers=[logging.StreamHandler(sys.stdout     )]					,       )
					logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

					# Log on each process the small summary:
					logger.warning(
					    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
					    + F"distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}"     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank     ):
										utils.logging.set_verbosity_info()
										utils.logging.enable_default_handler()
										utils.logging.enable_explicit_format()

					if training_args.local_rank != -1:
										raise ValueError("Distributed training is not currently supported."     )
					if training_args.tpu_num_cores is not None:
										raise ValueError("TPU acceleration is not currently supported."     )

					logger.info(F"Training/evaluation parameters {training_args}"     )

					# Set seed before initializing model.
					set_seed(training_args.seed     )

					# 1. read in data
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.data_file     )
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.class_names_file     )

					# 2. get teacher predictions and load into dataset
					logger.info("Generating predictions from zero-shot teacher model"     )
					SCREAMING_SNAKE_CASE				=				get_teacher_predictions(
					    teacher_args.teacher_name_or_path					,       UpperCAmelCase__					,       UpperCAmelCase__					,       teacher_args.hypothesis_template					,       teacher_args.teacher_batch_size					,       teacher_args.temperature					,       teacher_args.multi_label					,       data_args.use_fast_tokenizer					,       training_args.no_cuda					,       training_args.fpaa					,       )
					SCREAMING_SNAKE_CASE				=				Dataset.from_dict(
					    {
					        "text": examples,
					        "labels": teacher_soft_preds,
					    }     )

					# 3. create student
					logger.info("Initializing student model"     )
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(
					    student_args.student_name_or_path					,       num_labels=len(UpperCAmelCase__     )     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(student_args.student_name_or_path					,       use_fast=data_args.use_fast_tokenizer     )
					SCREAMING_SNAKE_CASE				=				dict(enumerate(UpperCAmelCase__     )     )
					SCREAMING_SNAKE_CASE				=				{label: i for i, label in enumerate(UpperCAmelCase__     )}

					# 4. train student on teacher predictions
					SCREAMING_SNAKE_CASE				=				dataset.map(UpperCAmelCase__					,       input_columns="text"     )
					dataset.set_format("torch"     )

					def compute_metrics(UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Optional[int]=False     ):
										SCREAMING_SNAKE_CASE				=				p.predictions.argmax(-1     )
										SCREAMING_SNAKE_CASE				=				p.label_ids.argmax(-1     )  # "label_ids" are actually distributions
										return {"agreement": (preds == proxy_labels).mean().item()}

					SCREAMING_SNAKE_CASE				=				DistillationTrainer(
					    model=UpperCAmelCase__					,       tokenizer=UpperCAmelCase__					,       args=UpperCAmelCase__					,       train_dataset=UpperCAmelCase__					,       compute_metrics=UpperCAmelCase__					,       )

					if training_args.do_train:
										logger.info("Training student model on teacher predictions"     )
										trainer.train()

					if training_args.do_eval:
										SCREAMING_SNAKE_CASE				=				trainer.evaluate(eval_dataset=UpperCAmelCase__     )["eval_agreement"]
										logger.info(F"Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%"     )

					trainer.save_model()


if __name__ == "__main__":
							main()




