import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

UpperCamelCase			 =	32



def     lowerCamelCase_    (  _lowercase					,	_lowercase = 16				)							->       Union[str, Any]:
		__A							:    int         =      AutoTokenizer.from_pretrained("bert-base-cased"				)
		__A							:    Dict         =      load_dataset("glue"					,	"mrpc"				)

		def tokenize_function(_lowercase				):
				# max_length=None => use the model max length (it's actually the default)
				__A							:    str         =      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():
				__A							:    Dict         =      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
		__A							:    Any         =      tokenized_datasets.rename_column("label"					,	"labels"				)

		def collate_fn(_lowercase				):
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						__A							:    Union[str, Any]         =      16
				elif accelerator.mixed_precision != "no":
						__A							:    Union[str, Any]         =      8
				else:
						__A							:    Union[str, Any]         =      None

				return tokenizer.pad(
				    __lowerCamelCase					,	padding="longest"					,	pad_to_multiple_of=__lowerCamelCase					,	return_tensors="pt"					,	)

		# Instantiate dataloaders.
		__A							:    Dict         =      DataLoader(
		    tokenized_datasets["train"]					,	shuffle=__lowerCamelCase					,	collate_fn=__lowerCamelCase					,	batch_size=__lowerCamelCase				)
		__A							:    str         =      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

					UpperCamelCase			 =	mocked_dataloaders  # noqa: F811



def     lowerCamelCase_    (  _lowercase					,	_lowercase				)							->       str:
		# For testing only
		if os.environ.get("TESTING_MOCKED_DATALOADERS"					,	__lowerCamelCase				) == "1":
				__A							:    Dict         =      2
		# Initialize accelerator
		__A							:    int         =      Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision				)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__A							:    Tuple         =      config["lr"]
		__A							:    Dict         =      int(config["num_epochs"]				)
		__A							:    Any         =      int(config["seed"]				)
		__A							:    Optional[int]         =      int(config["batch_size"]				)

		__A							:    List[str]         =      evaluate.load("glue"					,	"mrpc"				)

		# New Code #
		# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
		# half each time. From this, we can calculate the number of gradient accumulation steps needed
		# and modify the Accelerator object as a result
		@find_executable_batch_size(starting_batch_size=int(__lowerCamelCase				)				)
		def inner_training_loop(_lowercase				):
				# Since we need to modify the outside accelerator object, we need to bring it
				# to the local scope
				nonlocal accelerator

				# We can calculate the number of gradient accumulation steps based on the current
				# batch size vs the starting batch size
				__A							:    List[str]         =      observed_batch_size // batch_size

				# And then set it in the Accelerator directly:
				__A							:    Union[str, Any]         =      num_gradient_accumulation_steps

				# Next we need to free all of the stored model references in the Accelerator each time
				accelerator.free_memory()

				# And set the seed so our results are reproducable each reset
				set_seed(__lowerCamelCase				)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__A							:    Optional[int]         =      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).
				__A							:    int         =      model.to(accelerator.device				)

				# Instantiate optimizer
				__A							:    int         =      AdamW(params=model.parameters()					,	lr=__lowerCamelCase				)
				__A  ,   __A							:    Tuple         =      get_dataloaders(__lowerCamelCase					,	__lowerCamelCase				)

				# Instantiate scheduler
				__A							:    Union[str, Any]         =      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.
				__A  ,   __A  ,   __A  ,   __A  ,   __A							:    List[str]         =      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				):
								# And perform gradient accumulation
								with accelerator.accumulate(__lowerCamelCase				):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device				)
										__A							:    str         =      model(**__lowerCamelCase				)
										__A							:    List[str]         =      outputs.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():
										__A							:    Dict         =      model(**__lowerCamelCase				)
								__A							:    Union[str, Any]         =      outputs.logits.argmax(dim=-1				)
								__A  ,   __A							:    Union[str, Any]         =      accelerator.gather_for_metrics((predictions, batch["labels"])				)
								metric.add_batch(
								    predictions=__lowerCamelCase					,	references=__lowerCamelCase					,	)

						__A							:    Dict         =      metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"epoch {epoch}:"					,	__lowerCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def     lowerCamelCase_    (  )							->       Optional[int]:
		__A							:    Optional[Any]         =      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."					,	)
		parser.add_argument("--cpu"					,	action="store_true"					,	help="If passed, will train on the CPU."				)
		__A							:    Optional[int]         =      parser.parse_args()
		# New Code #
		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
		__A							:    Dict         =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
		training_function(__lowerCamelCase					,	__lowerCamelCase				)


if __name__ == "__main__":
					main()






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_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					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
					__a							=   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
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   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
					__a							=   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
					__a							=   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
					__a							=   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

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

					# Easy way to load the model
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   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'						)
					######################################

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


@dataclass
class 						a__							:
			A__				:   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\''}      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,						)
			A__				:   str								=			field(
			    default='facebook/rag-sequence-nq'      ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}      ,						)
			A__				:   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\''
			        )
			    }      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






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






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			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)

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




