"""simple docstring"""
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple					)					->				str:





						"""simple docstring"""






						snake_case								=			fname.split(os.path.sep					)[-1]
						return re.search(r'^(.*)_\d+\.jpg$' ,			A__					).groups()[0]



class   lowerCAmelCase_    (			__lowerCamelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=None		,    lowerCAmelCase=None    ):






										"""simple docstring"""

										snake_case								=			file_names
										snake_case								=			image_transform
										snake_case								=			label_to_id

				def __len__(				self    ):






										"""simple docstring"""

										return len(self.file_names    )

				def __getitem__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.file_names[idx]
										snake_case								=			PIL.Image.open(UpperCamelCase_    )
										snake_case								=			raw_image.convert('RGB'    )
										if self.image_transform is not None:
																snake_case								=			self.image_transform(UpperCamelCase_    )
										snake_case								=			extract_label(UpperCamelCase_    )
										if self.label_to_id is not None:
																snake_case								=			self.label_to_id[label]
										return {"image": image, "label": label}





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				List[Any]					)					->				Any:





						"""simple docstring"""






						if args.with_tracking:
												snake_case								=			Accelerator(
												    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			log_with='all' ,			project_dir=args.project_dir					)
						else:
												snake_case								=			Accelerator(cpu=args.cpu ,			mixed_precision=args.mixed_precision					)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case								=			config['lr']
						snake_case								=			int(config['num_epochs']					)
						snake_case								=			int(config['seed']					)
						snake_case								=			int(config['batch_size']					)
						snake_case								=			config['image_size']
						if not isinstance(A__ ,			(list, tuple)					):
												snake_case								=			(image_size, image_size)

						# 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":
																		snake_case								=			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case								=			int(args.checkpointing_steps					)
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""					)
						else:
												snake_case								=			None

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

						# Grab all the image filenames
						snake_case								=			[os.path.join(args.data_dir ,			A__					) for fname in os.listdir(args.data_dir					) if fname.endswith('.jpg'					)]

						# Build the label correspondences
						snake_case								=			[extract_label(A__					) for fname in file_names]
						snake_case								=			list(set(A__					)					)
						id_to_label.sort()
						snake_case								=			{lbl: i for i, lbl in enumerate(A__					)}

						# Set the seed before splitting the data.
						np.random.seed(A__					)
						torch.manual_seed(A__					)
						torch.cuda.manual_seed_all(A__					)

						# Split our filenames between train and validation
						snake_case								=			np.random.permutation(len(A__					)					)
						snake_case								=			int(0.8 * len(A__					)					)
						snake_case								=			random_perm[:cut]
						snake_case								=			random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						snake_case								=			Compose([RandomResizedCrop(A__ ,			scale=(0.5, 1.0)					), ToTensor()]					)
						snake_case								=			PetsDataset(
						    [file_names[i] for i in train_split] ,			image_transform=A__ ,			label_to_id=A__					)

						# For evaluation, we use a deterministic Resize
						snake_case								=			Compose([Resize(A__					), ToTensor()]					)
						snake_case								=			PetsDataset([file_names[i] for i in eval_split] ,			image_transform=A__ ,			label_to_id=A__					)

						# Instantiate dataloaders.
						snake_case								=			DataLoader(A__ ,			shuffle=A__ ,			batch_size=A__ ,			num_workers=4					)
						snake_case								=			DataLoader(A__ ,			shuffle=A__ ,			batch_size=A__ ,			num_workers=4					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case								=			create_model('resnet50d' ,			pretrained=A__ ,			num_classes=len(A__					)					)

						# 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).
						snake_case								=			model.to(accelerator.device					)

						# Freezing the base model
						for param in model.parameters():
												snake_case								=			False
						for param in model.get_classifier().parameters():
												snake_case								=			True

						# We normalize the batches of images to be a bit faster.
						snake_case								=			torch.tensor(model.default_cfg['mean']					)[None, :, None, None].to(accelerator.device					)
						snake_case								=			torch.tensor(model.default_cfg['std']					)[None, :, None, None].to(accelerator.device					)

						# Instantiate optimizer
						snake_case								=			torch.optim.Adam(params=model.parameters() ,			lr=lr / 2_5					)

						# Instantiate learning rate scheduler
						snake_case								=			OneCycleLR(optimizer=A__ ,			max_lr=A__ ,			epochs=A__ ,			steps_per_epoch=len(A__					)					)

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

												if "epoch" in training_difference:
																		snake_case								=			int(training_difference.replace('epoch_' ,			''					)					) + 1
																		snake_case								=			None
												else:
																		snake_case								=			int(training_difference.replace('step_' ,			''					)					)
																		snake_case								=			resume_step // len(A__					)
																		resume_step -= starting_epoch * len(A__					)

    # Now we train the model
						for epoch in range(A__ ,			A__					):
												model.train()
												if args.with_tracking:
																		snake_case								=			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
																		snake_case								=			accelerator.skip_first_batches(A__ ,			A__					)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case								=			train_dataloader
												for batch in active_dataloader:
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		snake_case								=			{k: v.to(accelerator.device					) for k, v in batch.items()}
																		snake_case								=			(batch['image'] - mean) / std
																		snake_case								=			model(A__					)
																		snake_case								=			torch.nn.functional.cross_entropy(A__ ,			batch['label']					)
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(A__					)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()
																		overall_step += 1
																		if isinstance(A__ ,			A__					):
																								snake_case								=			f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case								=			os.path.join(args.output_dir ,			A__					)
																														accelerator.save_state(A__					)
												model.eval()
												snake_case								=			0
												snake_case								=			0
												for step, batch in enumerate(A__					):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		snake_case								=			{k: v.to(accelerator.device					) for k, v in batch.items()}
																		snake_case								=			(batch['image'] - mean) / std
																		with torch.no_grad():
																								snake_case								=			model(A__					)
																		snake_case								=			outputs.argmax(dim=-1					)
																		snake_case					,snake_case								=			accelerator.gather_for_metrics((predictions, batch['label'])					)
																		snake_case								=			predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												snake_case								=			accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}: {1_0_0 * eval_metric:.2f}"""					)
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': 1_0_0 * eval_metric,
																		        'train_loss': total_loss.item() / len(A__					),
																		        'epoch': epoch,
																		    } ,			step=A__ ,			)
												if checkpointing_steps == "epoch":
																		snake_case								=			f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								snake_case								=			os.path.join(args.output_dir ,			A__					)
																		accelerator.save_state(A__					)

						if args.with_tracking:
												accelerator.end_training()





def    lowerCAmelCase__    (			)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			argparse.ArgumentParser(description='Simple example of training script.'					)
						parser.add_argument('--data_dir' ,			required=A__ ,			help='The data folder on disk.'					)
						parser.add_argument('--fp16' ,			action='store_true' ,			help='If passed, will use FP16 training.'					)
						parser.add_argument(
						    '--mixed_precision' ,			type=A__ ,			default=A__ ,			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=A__ ,			default=A__ ,			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=A__ ,			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=A__ ,			default=A__ ,			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(
						    '--project_dir' ,			type=A__ ,			default='logs' ,			help='Location on where to store experiment tracking logs` and relevent project information' ,			)
						snake_case								=			parser.parse_args()
						snake_case								=			{'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
						training_function(A__ ,			A__					)


if __name__ == "__main__":
			main()



"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
					raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

										train_parser.add_argument(
										    '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
										train_parser.add_argument(
										    '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
										train_parser.add_argument(
										    '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
										train_parser.add_argument(
										    '--column_id'		,    type=lowerCAmelCase		,    default=2		,    help='Column of the dataset csv file with example ids.'    )
										train_parser.add_argument(
										    '--skip_first_row'		,    action='store_true'		,    help='Skip the first row of the csv file (headers).'    )

										train_parser.add_argument('--validation_data'		,    type=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
										train_parser.add_argument(
										    '--validation_split'		,    type=lowerCAmelCase		,    default=0.1		,    help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'		,    )

										train_parser.add_argument('--output'		,    type=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

										train_parser.add_argument(
										    '--task'		,    type=lowerCAmelCase		,    default='text_classification'		,    help='Task to train the model on.'    )
										train_parser.add_argument(
										    '--model'		,    type=lowerCAmelCase		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'    )
										train_parser.add_argument('--train_batch_size'		,    type=lowerCAmelCase		,    default=32		,    help='Batch size for training.'    )
										train_parser.add_argument('--valid_batch_size'		,    type=lowerCAmelCase		,    default=64		,    help='Batch size for validation.'    )
										train_parser.add_argument('--learning_rate'		,    type=lowerCAmelCase		,    default=3E-5		,    help='Learning rate.'    )
										train_parser.add_argument('--adam_epsilon'		,    type=lowerCAmelCase		,    default=1E-08		,    help='Epsilon for Adam optimizer.'    )
										train_parser.set_defaults(func=lowerCAmelCase    )

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			TextClassificationPipeline.from_pretrained(args.model    )
										elif args.task == "token_classification":
																raise NotImplementedError
										elif args.task == "question_answering":
																raise NotImplementedError

										self.logger.info(F"""Loading dataset from {args.train_data}"""    )
										snake_case								=			Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			Processor.create_from_csv(
																    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										snake_case								=			args.validation_split
										snake_case								=			args.train_batch_size
										snake_case								=			args.valid_batch_size
										snake_case								=			args.learning_rate
										snake_case								=			args.adam_epsilon

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output    )


