






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
#
########################################################################


__magic_name__       					=      1_6
__magic_name__       					=      3_2







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	if args.with_tracking:
		lowerCamelCase_			:							Optional[int]      =       Accelerator(
		    cpu=args.cpu      ,					mixed_precision=args.mixed_precision      ,					log_with="all"      ,					project_dir=args.project_dir)
	else:
		lowerCamelCase_			:							Dict      =       Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision)

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

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

	lowerCamelCase_			:							int      =       AutoTokenizer.from_pretrained("bert-base-cased")
	lowerCamelCase_			:							Optional[Any]      =       load_dataset("glue"      ,					"mrpc")
	lowerCamelCase_			:							str      =       evaluate.load("glue"      ,					"mrpc")

	def tokenize_function(lowerCAmelCase_):
		# max_length=None => use the model max length (it's actually the default)
		lowerCamelCase_			:							List[Any]      =       tokenizer(examples["sentence1"]      ,					examples["sentence2"]      ,					truncation=SCREAMING_SNAKE_CASE_      ,					max_length=SCREAMING_SNAKE_CASE_)
		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():
		lowerCamelCase_			:							Any      =       datasets.map(
		    SCREAMING_SNAKE_CASE_      ,					batched=SCREAMING_SNAKE_CASE_      ,					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
	lowerCamelCase_			:							Union[str, Any]      =       tokenized_datasets.rename_column("label"      ,					"labels")

	# If the batch size is too big we use gradient accumulation
	lowerCamelCase_			:							Any      =       1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		lowerCamelCase_			:							Optional[Any]      =       batch_size // MAX_GPU_BATCH_SIZE
		lowerCamelCase_			:							Tuple      =       MAX_GPU_BATCH_SIZE

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

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

	# Instantiate dataloaders.
	lowerCamelCase_			:							Optional[Any]      =       DataLoader(
	    tokenized_datasets["train"]      ,					shuffle=SCREAMING_SNAKE_CASE_      ,					collate_fn=SCREAMING_SNAKE_CASE_      ,					batch_size=SCREAMING_SNAKE_CASE_)
	lowerCamelCase_			:							Tuple      =       DataLoader(
	    tokenized_datasets["validation"]      ,					shuffle=SCREAMING_SNAKE_CASE_      ,					collate_fn=SCREAMING_SNAKE_CASE_      ,					batch_size=SCREAMING_SNAKE_CASE_)

	set_seed(SCREAMING_SNAKE_CASE_)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCamelCase_			:							List[str]      =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,					return_dict=SCREAMING_SNAKE_CASE_)

	# 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).
	lowerCamelCase_			:							Optional[int]      =       model.to(accelerator.device)

	# Instantiate optimizer
	lowerCamelCase_			:							List[str]      =       AdamW(params=model.parameters()      ,					lr=SCREAMING_SNAKE_CASE_)

	# Instantiate scheduler
	lowerCamelCase_			:							int      =       get_linear_schedule_with_warmup(
	    optimizer=SCREAMING_SNAKE_CASE_      ,					num_warmup_steps=100      ,					num_training_steps=(len(SCREAMING_SNAKE_CASE_) * 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.
	lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       accelerator.prepare(
	    SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_)

	# We need to keep track of how many total steps we have iterated over
	lowerCamelCase_			:							Union[str, Any]      =       0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCamelCase_			:							str      =       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)
			lowerCamelCase_			:							str      =       os.path.basename(args.resume_from_checkpoint)
		else:
			# Get the most recent checkpoint
			lowerCamelCase_			:							Tuple      =       [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
			dirs.sort(key=os.path.getctime)
			lowerCamelCase_			:							Any      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		lowerCamelCase_			:							Union[str, Any]      =       os.path.splitext(SCREAMING_SNAKE_CASE_)[0]

		if "epoch" in training_difference:
			lowerCamelCase_			:							Tuple      =       int(training_difference.replace("epoch_"      ,					"")) + 1
			lowerCamelCase_			:							int      =       None
		else:
			lowerCamelCase_			:							str      =       int(training_difference.replace("step_"      ,					""))
			lowerCamelCase_			:							Union[str, Any]      =       resume_step // len(SCREAMING_SNAKE_CASE_)
			resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_)

    # Now we train the model
	for epoch in range(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_):
		model.train()
		if args.with_tracking:
			lowerCamelCase_			:							List[str]      =       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
			lowerCamelCase_			:							Dict      =       accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			lowerCamelCase_			:							List[Any]      =       train_dataloader
		for step, batch in enumerate(SCREAMING_SNAKE_CASE_):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device)
			lowerCamelCase_			:							Optional[int]      =       model(**SCREAMING_SNAKE_CASE_)
			lowerCamelCase_			:							Any      =       outputs.loss
			lowerCamelCase_			:							Optional[Any]      =       loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(SCREAMING_SNAKE_CASE_)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_):
				lowerCamelCase_			:							Dict      =       F"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						lowerCamelCase_			:							Optional[int]      =       os.path.join(args.output_dir      ,					SCREAMING_SNAKE_CASE_)
					accelerator.save_state(SCREAMING_SNAKE_CASE_)

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

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

		if checkpointing_steps == "epoch":
			lowerCamelCase_			:							Any      =       F"""epoch_{epoch}"""
			if args.output_dir is not None:
				lowerCamelCase_			:							str      =       os.path.join(args.output_dir      ,					SCREAMING_SNAKE_CASE_)
			accelerator.save_state(SCREAMING_SNAKE_CASE_)

	if args.with_tracking:
		accelerator.end_training()







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument(
	    "--mixed_precision"      ,					type=SCREAMING_SNAKE_CASE_      ,					default=SCREAMING_SNAKE_CASE_      ,					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=SCREAMING_SNAKE_CASE_      ,					default=SCREAMING_SNAKE_CASE_      ,					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=SCREAMING_SNAKE_CASE_      ,					default=SCREAMING_SNAKE_CASE_      ,					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=SCREAMING_SNAKE_CASE_      ,					default="."      ,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,					)
	parser.add_argument(
	    "--project_dir"      ,					type=SCREAMING_SNAKE_CASE_      ,					default="logs"      ,					help="Location on where to store experiment tracking logs` and relevent project information"      ,					)
	lowerCamelCase_			:							Any      =       parser.parse_args()
	lowerCamelCase_			:							Any      =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_)


if __name__ == "__main__":
							main()





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




						def 					_UpperCamelCase			(   self     ):
							self.retriever.index.init_index()




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_				,							a_				,							a_				,							a_=None     ):
							if index is not None and index.is_initialized() and len(a_     ) > 0:
								raise ValueError(
								    "When using Ray for distributed fine-tuning, "
								    "you'll need to provide the paths instead, "
								    "as the dataset and the index are loaded "
								    "separately. More info in examples/rag/use_own_knowledge_dataset.py "     )

							super().__init__(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




						def 					_UpperCamelCase			(   self     ):
							logger.info("initializing retrieval"     )

							if len(self.retrieval_workers     ) > 0:
								ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
							else:
								# Non-distributed training. Load index into this same process.
								self.index.init_index()




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if len(self.retrieval_workers     ) > 0:
								# Select a random retrieval actor.
								lowerCamelCase_			:							List[Any]      =       self.retrieval_workers[random.randint(0				,							len(self.retrieval_workers     ) - 1     )]
								lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       ray.get(
								    random_worker.retrieve.remote(a_				,							a_     )     )
							else:
								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       self._main_retrieve(a_				,							a_     )
								lowerCamelCase_			:							Any      =       self.index.get_doc_dicts(a_     )
							return retrieved_doc_embeds, doc_ids, doc_dicts




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

							return cls(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							retrieval_workers=a_				,							index=a_				,							)




						def 					_UpperCamelCase			(   self     ):
							logger.info("re-loading the new dataset with embeddings"     )
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

							# build the index object again
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,							self.question_encoder_tokenizer				,							self.generator_tokenizer				,							a_     )
							        for worker in self.retrieval_workers
							    ]     )

