





'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowerCAmelCase_   :   List[Any]       =					16
lowerCAmelCase_   :   Dict       =					32







def    _lowerCamelCase  (__lowerCamelCase  :     Accelerator					,  __lowerCamelCase  :     int = 16     )   ->    List[Any]:
			a__     = AutoTokenizer.from_pretrained("bert-base-cased"     )
			a__     = load_dataset("glue"					,  "mrpc"     )

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

			def collate_fn(__lowerCamelCase  :     Dict     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a__     = 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":
									a__     = 16
						elif accelerator.mixed_precision != "no":
									a__     = 8
						else:
									a__     = None

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

			# Instantiate dataloaders.
			a__     = DataLoader(
			    tokenized_datasets["train"]					,  shuffle=__lowerCamelCase					,  collate_fn=__lowerCamelCase					,  batch_size=__lowerCamelCase     )
			a__     = 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

		lowerCAmelCase_   :   Optional[int]       =					mocked_dataloaders  # noqa: F811







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

			a__     = evaluate.load("glue"					,  "mrpc"     )

			# If the batch size is too big we use gradient accumulation
			a__     = 1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						a__     = batch_size // MAX_GPU_BATCH_SIZE
						a__     = MAX_GPU_BATCH_SIZE

			set_seed(__lowerCamelCase     )
			a__   ,				a__     = get_dataloaders(__lowerCamelCase					,  __lowerCamelCase     )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__     = 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__     = model.to(accelerator.device     )

			# Instantiate optimizer
			a__     = AdamW(params=model.parameters()					,  lr=__lowerCamelCase     )

			# Instantiate scheduler
			a__     = 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.
			a__   ,				a__   ,				a__   ,				a__   ,				a__     = 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     )
									a__     = model(**__lowerCamelCase     )
									a__     = outputs.loss
									a__     = loss / gradient_accumulation_steps
									accelerator.backward(__lowerCamelCase     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						a__     = 0
						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__     = model(**__lowerCamelCase     )
									a__     = outputs.logits.argmax(dim=-1     )
									a__   ,				a__     = accelerator.gather((predictions, batch["labels"])     )
									# New Code #
									# First we check if it's a distributed system
									if accelerator.use_distributed:
												# Then see if we're on the last batch of our eval dataloader
												if step == len(__lowerCamelCase     ) - 1:
															# Last batch needs to be truncated on distributed systems as it contains additional samples
															a__     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
															a__     = references[: len(eval_dataloader.dataset     ) - samples_seen]
												else:
															# Otherwise we add the number of samples seen
															samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
									metric.add_batch(
									    predictions=__lowerCamelCase					,  references=__lowerCamelCase					,  )

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







def    _lowerCamelCase  ()   ->    Union[str, Any]:
			a__     = 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__     = parser.parse_args()
			a__     = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(__lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		main()










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



