

"""simple docstring"""






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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowerCamelCase__			:					List[Any]							  =					16
lowerCamelCase__			:					Any							  =					32
def  UpperCamelCase   (     _lowerCAmelCase       :  int,					_lowerCAmelCase       :  Tuple	)			->     Optional[Any]:
	# Initialize accelerator
	if args.with_tracking:
		_UpperCAmelCase      :							Dict       =   Accelerator(
		    cpu=args.cpu,					mixed_precision=args.mixed_precision,					log_with="""all""",					project_dir=args.project_dir	)
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision	)

	if hasattr(args.checkpointing_steps,					"""isdigit"""	):
		if args.checkpointing_steps == "epoch":
			_UpperCAmelCase      :							str       =   args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			_UpperCAmelCase      :							Tuple       =   int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
	else:
		_UpperCAmelCase      :							Optional[Any]       =   None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_UpperCAmelCase      :							Union[str, Any]       =   config["""lr"""]
	_UpperCAmelCase      :							str       =   int(config["""num_epochs"""]	)
	_UpperCAmelCase      :							Any       =   int(config["""seed"""]	)
	_UpperCAmelCase      :							str       =   int(config["""batch_size"""]	)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		_UpperCAmelCase      :							Optional[Any]       =   os.path.split(_lowerCAmelCase	)[-1].split("""."""	)[0]
		accelerator.init_trackers(_lowerCAmelCase,					_lowerCAmelCase	)

	_UpperCAmelCase      :							List[str]       =   AutoTokenizer.from_pretrained("""bert-base-cased"""	)
	_UpperCAmelCase      :							Tuple       =   load_dataset("""glue""",					"""mrpc"""	)
	_UpperCAmelCase      :							Any       =   evaluate.load("""glue""",					"""mrpc"""	)

	def tokenize_function(_lowerCAmelCase       :  Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCAmelCase      :							int       =   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():
		_UpperCAmelCase      :							List[Any]       =   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
	_UpperCAmelCase      :							Optional[Any]       =   tokenized_datasets.rename_column("""label""",					"""labels"""	)

	# If the batch size is too big we use gradient accumulation
	_UpperCAmelCase      :							Optional[int]       =   1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		_UpperCAmelCase      :							List[str]       =   batch_size // MAX_GPU_BATCH_SIZE
		_UpperCAmelCase      :							Union[str, Any]       =   MAX_GPU_BATCH_SIZE

	def collate_fn(_lowerCAmelCase       :  List[str]	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_UpperCAmelCase      :							List[Any]       =   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":
			_UpperCAmelCase      :							Union[str, Any]       =   16
		elif accelerator.mixed_precision != "no":
			_UpperCAmelCase      :							Any       =   8
		else:
			_UpperCAmelCase      :							Union[str, Any]       =   None

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

	# Instantiate dataloaders.
	_UpperCAmelCase      :							Optional[int]       =   DataLoader(
	    tokenized_datasets["""train"""],					shuffle=_lowerCAmelCase,					collate_fn=_lowerCAmelCase,					batch_size=_lowerCAmelCase	)
	_UpperCAmelCase      :							List[str]       =   DataLoader(
	    tokenized_datasets["""validation"""],					shuffle=_lowerCAmelCase,					collate_fn=_lowerCAmelCase,					batch_size=_lowerCAmelCase	)

	set_seed(_lowerCAmelCase	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCAmelCase      :							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).
	_UpperCAmelCase      :							Dict       =   model.to(accelerator.device	)

	# Instantiate optimizer
	_UpperCAmelCase      :							str       =   AdamW(params=model.parameters(),					lr=_lowerCAmelCase	)

	# Instantiate scheduler
	_UpperCAmelCase      :							Any       =   get_linear_schedule_with_warmup(
	    optimizer=_lowerCAmelCase,					num_warmup_steps=100,					num_training_steps=(len(_lowerCAmelCase	) * num_epochs) // gradient_accumulation_steps,					)

	# 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.
	_UpperCAmelCase      :							Optional[Any]       =   accelerator.prepare(
	    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase	)

	# We need to keep track of how many total steps we have iterated over
	_UpperCAmelCase      :							Any       =   0
	# We also need to keep track of the stating epoch so files are named properly
	_UpperCAmelCase      :							Dict       =   0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
			accelerator.load_state(args.resume_from_checkpoint	)
			_UpperCAmelCase      :							Optional[int]       =   os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			_UpperCAmelCase      :							Union[str, Any]       =   [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			_UpperCAmelCase      :							Optional[int]       =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		_UpperCAmelCase      :							Optional[Any]       =   os.path.splitext(_lowerCAmelCase	)[0]

		if "epoch" in training_difference:
			_UpperCAmelCase      :							List[Any]       =   int(training_difference.replace("""epoch_""",					""""""	)	) + 1
			_UpperCAmelCase      :							Dict       =   None
		else:
			_UpperCAmelCase      :							Optional[int]       =   int(training_difference.replace("""step_""",					""""""	)	)
			_UpperCAmelCase      :							Union[str, Any]       =   resume_step // len(_lowerCAmelCase	)
			resume_step -= starting_epoch * len(_lowerCAmelCase	)

    # Now we train the model
	for epoch in range(_lowerCAmelCase,					_lowerCAmelCase	):
		model.train()
		if args.with_tracking:
			_UpperCAmelCase      :							int       =   0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			_UpperCAmelCase      :							str       =   accelerator.skip_first_batches(_lowerCAmelCase,					_lowerCAmelCase	)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			_UpperCAmelCase      :							str       =   train_dataloader
		for step, batch in enumerate(_lowerCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			_UpperCAmelCase      :							int       =   model(**_lowerCAmelCase	)
			_UpperCAmelCase      :							Tuple       =   outputs.loss
			_UpperCAmelCase      :							Tuple       =   loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(_lowerCAmelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(_lowerCAmelCase,					_lowerCAmelCase	):
				_UpperCAmelCase      :							Dict       =   f'''step_{overall_step}'''
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						_UpperCAmelCase      :							Optional[int]       =   os.path.join(args.output_dir,					_lowerCAmelCase	)
					accelerator.save_state(_lowerCAmelCase	)

		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():
				_UpperCAmelCase      :							List[str]       =   model(**_lowerCAmelCase	)
			_UpperCAmelCase      :							List[Any]       =   outputs.logits.argmax(dim=-1	)
			_UpperCAmelCase      :							Dict       =   accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
			metric.add_batch(
			    predictions=_lowerCAmelCase,					references=_lowerCAmelCase,					)

		_UpperCAmelCase      :							Any       =   metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'''epoch {epoch}:''',					_lowerCAmelCase	)
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(_lowerCAmelCase	),
			        """epoch""": epoch,
			    },					step=_lowerCAmelCase,					)

		if checkpointing_steps == "epoch":
			_UpperCAmelCase      :							int       =   f'''epoch_{epoch}'''
			if args.output_dir is not None:
				_UpperCAmelCase      :							str       =   os.path.join(args.output_dir,					_lowerCAmelCase	)
			accelerator.save_state(_lowerCAmelCase	)

	if args.with_tracking:
		accelerator.end_training()
def  UpperCamelCase   (     )			->     Optional[int]:
	_UpperCAmelCase      :							Dict       =   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."""	)
	parser.add_argument(
	    """--checkpointing_steps""",					type=_lowerCAmelCase,					default=_lowerCAmelCase,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""",					)
	parser.add_argument(
	    """--resume_from_checkpoint""",					type=_lowerCAmelCase,					default=_lowerCAmelCase,					help="""If the training should continue from a checkpoint folder.""",					)
	parser.add_argument(
	    """--with_tracking""",					action="""store_true""",					help="""Whether to load in all available experiment trackers from the environment and use them for logging.""",					)
	parser.add_argument(
	    """--output_dir""",					type=_lowerCAmelCase,					default=""".""",					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""",					)
	parser.add_argument(
	    """--project_dir""",					type=_lowerCAmelCase,					default="""logs""",					help="""Location on where to store experiment tracking logs` and relevent project information""",					)
	_UpperCAmelCase      :							Optional[int]       =   parser.parse_args()
	_UpperCAmelCase      :							List[str]       =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(_lowerCAmelCase,					_lowerCAmelCase	)


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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase__			:					int							  =					logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__			:					int							  =					'''cuda''' if torch.cuda.is_available() else '''cpu'''
def  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  int=100,					_lowerCAmelCase       :  List[str]=" "	)			->     List[str]:
	_UpperCAmelCase      :							Tuple       =   text.split(_lowerCAmelCase	)
	return [character.join(text[i : i + n]	).strip() for i in range(0,					len(_lowerCAmelCase	),					_lowerCAmelCase	)]
def  UpperCamelCase   (     _lowerCAmelCase       :  dict	)			->     dict:
	_UpperCAmelCase  ,				_UpperCAmelCase      :							List[str]       =   [], []
	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       :  dict,					_lowerCAmelCase       :  DPRContextEncoder,					_lowerCAmelCase       :  DPRContextEncoderTokenizerFast	)			->     dict:
	_UpperCAmelCase      :							int       =   ctx_tokenizer(
	    documents["""title"""],					documents["""text"""],					truncation=_lowerCAmelCase,					padding="""longest""",					return_tensors="""pt"""	)["""input_ids"""]
	_UpperCAmelCase      :							Optional[Any]       =   ctx_encoder(input_ids.to(device=_lowerCAmelCase	),					return_dict=_lowerCAmelCase	).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}
def  UpperCamelCase   (     _lowerCAmelCase       :  "RagExampleArguments",					_lowerCAmelCase       :  "ProcessingArguments",					_lowerCAmelCase       :  "IndexHnswArguments",					)			->     int:
	######################################
	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
	_UpperCAmelCase      :							Optional[int]       =   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
	_UpperCAmelCase      :							List[str]       =   dataset.map(_lowerCAmelCase,					batched=_lowerCAmelCase,					num_proc=processing_args.num_proc	)

	# And compute the embeddings
	_UpperCAmelCase      :							Optional[Any]       =   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=_lowerCAmelCase	)
	_UpperCAmelCase      :							Tuple       =   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
	_UpperCAmelCase      :							int       =   Features(
	    {"""text""": Value("""string"""	), """title""": Value("""string"""	), """embeddings""": Sequence(Value("""float32"""	)	)}	)  # optional, save as float32 instead of float64 to save space
	_UpperCAmelCase      :							Any       =   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
	_UpperCAmelCase      :							Dict       =   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
	_UpperCAmelCase      :							List[Any]       =   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
	_UpperCAmelCase      :							List[str]       =   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
	_UpperCAmelCase      :							List[Any]       =   RagRetriever.from_pretrained(
	    rag_example_args.rag_model_name,					index_name="""custom""",					indexed_dataset=_lowerCAmelCase	)
	_UpperCAmelCase      :							Dict       =   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,					retriever=_lowerCAmelCase	)
	_UpperCAmelCase      :							Tuple       =   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"""	)
	######################################

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






@dataclass
class       _UpperCAmelCase     :
			__a	:    str			   =							field(
			    default=str(Path(__a).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=__a      ,       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=__a      ,       metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,       )






@dataclass
class       _UpperCAmelCase     :
			__a	:    Optional[int]			   =							field(
			    default=__a      ,       metadata={
			        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
			    }      ,       )
			__a	:    int			   =							field(
			    default=1_6      ,       metadata={
			        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
			    }      ,       )






@dataclass
class       _UpperCAmelCase     :
			__a	:    int			   =							field(
			    default=7_6_8      ,       metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,       )
			__a	:    int			   =							field(
			    default=1_2_8      ,       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__			:					Dict							  =					parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												lowerCamelCase__			:					List[str]							  =					rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)





