
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


lowercase__		:Optional[int]			  =   logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowercase__		:str			  =   "cuda" if torch.cuda.is_available() else "cpu"



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__=100			,      lowerCAmelCase__=" "		):
					'''simple docstring'''
					lowercase						     =						text.split(lowerCAmelCase__		)
					return [character.join(text[i : i + n]		).strip() for i in range(0			,      len(lowerCAmelCase__		)			,      lowerCAmelCase__		)]



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						[], []
					for title, text in zip(documents['''title''']			,      documents['''text''']		):
										if text is not None:
															for passage in split_text(lowerCAmelCase__		):
																				titles.append(title if title is not None else ''''''		)
																				texts.append(lowerCAmelCase__		)
					return {"title": titles, "text": texts}



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						ctx_tokenizer(
					    documents['''title''']			,      documents['''text''']			,      truncation=lowerCAmelCase__			,      padding='''longest'''			,      return_tensors='''pt'''		)['''input_ids''']
					lowercase						     =						ctx_encoder(input_ids.to(device=lowerCAmelCase__		)			,      return_dict=lowerCAmelCase__		).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      ):
					'''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
					lowercase						     =						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						     =						dataset.map(lowerCAmelCase__			,      batched=lowerCAmelCase__			,      num_proc=processing_args.num_proc		)

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

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

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







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







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







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

							lowercase__		:List[Any]			  =   HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							lowercase__  ,					lowercase__  ,					lowercase__		:Any			  =   parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														lowercase__		:Tuple			  =   rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__		:Union[str, Any]			  =   16
lowercase__		:Optional[Any]			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

					def tokenize_function(lowerCAmelCase__		):
										# max_length=None => use the model max length (it's actually the default)
										lowercase						     =						tokenizer(examples['''sentence1''']			,      examples['''sentence2''']			,      truncation=lowerCAmelCase__			,      max_length=lowerCAmelCase__		)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										lowercase						     =						datasets.map(
										    lowerCAmelCase__			,      batched=lowerCAmelCase__			,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,      )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					lowercase						     =						tokenized_datasets.rename_column('''label'''			,      '''labels'''		)

					def collate_fn(lowerCAmelCase__		):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowercase						     =						128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															lowercase						     =						16
										elif accelerator.mixed_precision != "no":
															lowercase						     =						8
										else:
															lowercase						     =						None

										return tokenizer.pad(
										    lowerCAmelCase__			,      padding='''longest'''			,      max_length=lowerCAmelCase__			,      pad_to_multiple_of=lowerCAmelCase__			,      return_tensors='''pt'''			,      )

					# Instantiate dataloaders.
					lowercase						     =						DataLoader(
					    tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)
					lowercase						     =						DataLoader(
					    tokenized_datasets['''validation''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowercase__		:List[str]			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowercase						     =						config['''lr''']
					lowercase						     =						int(config['''num_epochs''']		)
					lowercase						     =						int(config['''seed''']		)
					lowercase						     =						int(config['''batch_size''']		)

					lowercase						     =						evaluate.load('''glue'''			,      '''mrpc'''		)

					set_seed(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase						     =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,      return_dict=lowerCAmelCase__		)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					lowercase						     =						model.to(accelerator.device		)

					# Instantiate optimizer
					lowercase						     =						AdamW(params=model.parameters()			,      lr=lowerCAmelCase__		)

					# Instantiate scheduler
					lowercase						     =						get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__			,      num_warmup_steps=100			,      num_training_steps=(len(lowerCAmelCase__		) * num_epochs)			,      )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						accelerator.prepare(
					    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)

					# Now we train the model
					for epoch in range(lowerCAmelCase__		):
										model.train()
										for step, batch in enumerate(lowerCAmelCase__		):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device		)
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(lowerCAmelCase__		):
																				lowercase						     =						model(**lowerCAmelCase__		)
																				lowercase						     =						output.loss
																				accelerator.backward(lowerCAmelCase__		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										for step, batch in enumerate(lowerCAmelCase__		):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device		)
															with torch.no_grad():
																				lowercase						     =						model(**lowerCAmelCase__		)
															lowercase						     =						outputs.logits.argmax(dim=-1		)
															lowercase     ,						lowercase						     =						accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
															metric.add_batch(
															    predictions=lowerCAmelCase__			,      references=lowerCAmelCase__			,      )

										lowercase						     =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:'			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						argparse.ArgumentParser(description='''Simple example of training script.'''		)
					parser.add_argument(
					    '''--mixed_precision'''			,      type=lowerCAmelCase__			,      default=lowerCAmelCase__			,      choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,      help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      default=1			,      help='''The number of minibatches to be ran before gradients are accumulated.'''			,      )
					parser.add_argument('''--cpu'''			,      action='''store_true'''			,      help='''If passed, will train on the CPU.'''		)
					lowercase						     =						parser.parse_args()
					lowercase						     =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()




