"""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 the checkpointing capability,
# 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									=	16
__lowerCamelCase									=	32






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__ = 16					):






							"""simple docstring"""





							A__						  =				AutoTokenizer.from_pretrained('bert-base-cased'					)
							A__						  =				load_dataset('glue'							,     'mrpc'					)

							def tokenize_function(UpperCamelCase__					):
														# max_length=None => use the model max length (it's actually the default)
														A__						  =				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():
														A__						  =				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
							A__						  =				tokenized_datasets.rename_column('label'							,     'labels'					)

							def collate_fn(UpperCamelCase__					):
														# 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(
														    _SCREAMING_SNAKE_CASE							,     padding='longest'							,     max_length=_SCREAMING_SNAKE_CASE							,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE							,     return_tensors='pt'							,     )

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

							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									=	mocked_dataloaders  # noqa: F811






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							if os.environ.get('TESTING_MOCKED_DATALOADERS'							,     _SCREAMING_SNAKE_CASE					) == "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']					)

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps							,     'isdigit'					):
														if args.checkpointing_steps == "epoch":
																					A__						  =				args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A__						  =				int(args.checkpointing_steps					)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
							else:
														A__						  =				None

							set_seed(_SCREAMING_SNAKE_CASE					)

							A__						  =				get_dataloaders(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE					)
							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

							# 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=_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).
							A__						  =				model.to(accelerator.device					)

							# Instantiate optimizer
							A__						  =				AdamW(params=model.parameters()							,     lr=_SCREAMING_SNAKE_CASE					)

							# Instantiate scheduler
							A__						  =				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.
							A__						  =				accelerator.prepare(
							    _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE					)

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							A__						  =				0
							# We also need to keep track of the stating epoch so files are named properly
							A__						  =				0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							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					)
																					A__						  =				os.path.basename(args.resume_from_checkpoint					)
														else:
																					# Get the most recent checkpoint
																					A__						  =				[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
																					dirs.sort(key=os.path.getctime					)
																					A__						  =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A__						  =				os.path.splitext(_SCREAMING_SNAKE_CASE					)[0]

														if "epoch" in training_difference:
																					A__						  =				int(training_difference.replace('epoch_'							,     ''					)					) + 1
																					A__						  =				None
														else:
																					A__						  =				int(training_difference.replace('step_'							,     ''					)					)
																					A__						  =				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()
														# New Code #
														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
																					A__						  =				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
																					A__						  =				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					)
																					A__						  =				model(**_SCREAMING_SNAKE_CASE					)
																					A__						  =				outputs.loss
																					A__						  =				loss / gradient_accumulation_steps
																					accelerator.backward(_SCREAMING_SNAKE_CASE					)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE					):
																												A__						  =				F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A__						  =				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` (the default).
																					batch.to(accelerator.device					)
																					with torch.no_grad():
																												A__						  =				model(**_SCREAMING_SNAKE_CASE					)
																					A__						  =				outputs.logits.argmax(dim=-1					)
																					A__						  =				accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=_SCREAMING_SNAKE_CASE							,     references=_SCREAMING_SNAKE_CASE							,     )

														A__						  =				metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''							,     _SCREAMING_SNAKE_CASE					)

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A__						  =				F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												A__						  =				os.path.join(args.output_dir							,     _SCREAMING_SNAKE_CASE					)
																					accelerator.save_state(_SCREAMING_SNAKE_CASE					)






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				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(
							    '--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(
							    '--resume_from_checkpoint'							,     type=_SCREAMING_SNAKE_CASE							,     default=_SCREAMING_SNAKE_CASE							,     help='If the training should continue from a checkpoint folder.'							,     )
							A__						  =				parser.parse_args()
							A__						  =				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				main()






"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__   			=      load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
lowercase__			,   lowercase__			,   lowercase__			,   lowercase__   			=      train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

lowercase__   			=      KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__   			=      [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__   			=      knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)


