
"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		int 					=					fname.split(os.path.sep			)[-1]
					return re.search(R"""^(.*)_\d+\.jpg$"""				,    SCREAMING_SNAKE_CASE__			).groups()[0]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=None       ):
											snake_case_					:		Union[str, Any] 					=					file_names
											snake_case_					:		Tuple 					=					image_transform
											snake_case_					:		Union[str, Any] 					=					label_to_id







						def __len__(self       ):
											return len(self.file_names       )







						def __getitem__(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					self.file_names[idx]
											snake_case_					:		Dict 					=					PIL.Image.open(lowercase__       )
											snake_case_					:		Union[str, Any] 					=					raw_image.convert("""RGB"""       )
											if self.image_transform is not None:
																snake_case_					:		Union[str, Any] 					=					self.image_transform(lowercase__       )
											snake_case_					:		Dict 					=					extract_label(lowercase__       )
											if self.label_to_id is not None:
																snake_case_					:		Union[str, Any] 					=					self.label_to_id[label]
											return {"image": image, "label": label}







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					if args.with_tracking:
										snake_case_					:		Union[str, Any] 					=					Accelerator(
										    cpu=args.cpu				,    mixed_precision=args.mixed_precision				,    log_with="""all"""				,    project_dir=args.project_dir			)
					else:
										snake_case_					:		List[Any] 					=					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_					:		Tuple 					=					config["""lr"""]
					snake_case_					:		Any 					=					int(config["""num_epochs"""]			)
					snake_case_					:		str 					=					int(config["""seed"""]			)
					snake_case_					:		Any 					=					int(config["""batch_size"""]			)
					snake_case_					:		Any 					=					config["""image_size"""]
					if not isinstance(SCREAMING_SNAKE_CASE__				,    (list, tuple)			):
										snake_case_					:		Dict 					=					(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_					:		List[str] 					=					args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_					:		Any 					=					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_					:		Dict 					=					None

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

					# Grab all the image filenames
					snake_case_					:		Any 					=					[os.path.join(args.data_dir				,    SCREAMING_SNAKE_CASE__			) for fname in os.listdir(args.data_dir			) if fname.endswith(""".jpg"""			)]

					# Build the label correspondences
					snake_case_					:		int 					=					[extract_label(SCREAMING_SNAKE_CASE__			) for fname in file_names]
					snake_case_					:		List[str] 					=					list(set(SCREAMING_SNAKE_CASE__			)			)
					id_to_label.sort()
					snake_case_					:		str 					=					{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__			)}

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

					# Split our filenames between train and validation
					snake_case_					:		Union[str, Any] 					=					np.random.permutation(len(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		Dict 					=					int(0.8 * len(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		Union[str, Any] 					=					random_perm[:cut]
					snake_case_					:		Optional[int] 					=					random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					snake_case_					:		Any 					=					Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__				,    scale=(0.5, 1.0)			), ToTensor()]			)
					snake_case_					:		Tuple 					=					PetsDataset(
					    [file_names[i] for i in train_split]				,    image_transform=SCREAMING_SNAKE_CASE__				,    label_to_id=SCREAMING_SNAKE_CASE__			)

					# For evaluation, we use a deterministic Resize
					snake_case_					:		Tuple 					=					Compose([Resize(SCREAMING_SNAKE_CASE__			), ToTensor()]			)
					snake_case_					:		str 					=					PetsDataset([file_names[i] for i in eval_split]				,    image_transform=SCREAMING_SNAKE_CASE__				,    label_to_id=SCREAMING_SNAKE_CASE__			)

					# Instantiate dataloaders.
					snake_case_					:		int 					=					DataLoader(SCREAMING_SNAKE_CASE__				,    shuffle=SCREAMING_SNAKE_CASE__				,    batch_size=SCREAMING_SNAKE_CASE__				,    num_workers=4			)
					snake_case_					:		Optional[int] 					=					DataLoader(SCREAMING_SNAKE_CASE__				,    shuffle=SCREAMING_SNAKE_CASE__				,    batch_size=SCREAMING_SNAKE_CASE__				,    num_workers=4			)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_					:		Tuple 					=					create_model("""resnet50d"""				,    pretrained=SCREAMING_SNAKE_CASE__				,    num_classes=len(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).
					snake_case_					:		Tuple 					=					model.to(accelerator.device			)

					# Freezing the base model
					for param in model.parameters():
										snake_case_					:		Any 					=					False
					for param in model.get_classifier().parameters():
										snake_case_					:		List[str] 					=					True

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

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

					# Instantiate learning rate scheduler
					snake_case_					:		Dict 					=					OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__				,    max_lr=SCREAMING_SNAKE_CASE__				,    epochs=SCREAMING_SNAKE_CASE__				,    steps_per_epoch=len(SCREAMING_SNAKE_CASE__			)			)

					# 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_					:		Any 					=					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
					snake_case_					:		Optional[Any] 					=					0
					# We also need to keep track of the starting epoch so files are named properly
					snake_case_					:		List[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			)
															snake_case_					:		Tuple 					=					os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															snake_case_					:		List[Any] 					=					[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															snake_case_					:		int 					=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_					:		Union[str, Any] 					=					os.path.splitext(SCREAMING_SNAKE_CASE__			)[0]

										if "epoch" in training_difference:
															snake_case_					:		Tuple 					=					int(training_difference.replace("""epoch_"""				,    """"""			)			) + 1
															snake_case_					:		str 					=					None
										else:
															snake_case_					:		Tuple 					=					int(training_difference.replace("""step_"""				,    """"""			)			)
															snake_case_					:		Optional[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:
															snake_case_					:		Dict 					=					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_					:		Tuple 					=					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
															snake_case_					:		Dict 					=					train_dataloader
										for batch in active_dataloader:
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															snake_case_					:		Any 					=					{k: v.to(accelerator.device			) for k, v in batch.items()}
															snake_case_					:		List[str] 					=					(batch["""image"""] - mean) / std
															snake_case_					:		int 					=					model(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		List[str] 					=					torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__				,    batch["""label"""]			)
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(SCREAMING_SNAKE_CASE__			)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															overall_step += 1
															if isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
																				snake_case_					:		Dict 					=					f'step_{overall_step}'
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_					:		Dict 					=					os.path.join(args.output_dir				,    SCREAMING_SNAKE_CASE__			)
																									accelerator.save_state(SCREAMING_SNAKE_CASE__			)
										model.eval()
										snake_case_					:		List[str] 					=					0
										snake_case_					:		Any 					=					0
										for step, batch in enumerate(SCREAMING_SNAKE_CASE__			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															snake_case_					:		List[str] 					=					{k: v.to(accelerator.device			) for k, v in batch.items()}
															snake_case_					:		Optional[Any] 					=					(batch["""image"""] - mean) / std
															with torch.no_grad():
																				snake_case_					:		int 					=					model(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		List[Any] 					=					outputs.argmax(dim=-1			)
															snake_case_					:		Optional[int] 					=					accelerator.gather_for_metrics((predictions, batch["""label"""])			)
															snake_case_					:		Dict 					=					predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										snake_case_					:		Tuple 					=					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(SCREAMING_SNAKE_CASE__			),
															        """epoch""": epoch,
															    }				,    step=SCREAMING_SNAKE_CASE__				,    )
										if checkpointing_steps == "epoch":
															snake_case_					:		Dict 					=					f'epoch_{epoch}'
															if args.output_dir is not None:
																				snake_case_					:		Dict 					=					os.path.join(args.output_dir				,    SCREAMING_SNAKE_CASE__			)
															accelerator.save_state(SCREAMING_SNAKE_CASE__			)

					if args.with_tracking:
										accelerator.end_training()







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					argparse.ArgumentParser(description="""Simple example of training script."""			)
					parser.add_argument("""--data_dir"""				,    required=SCREAMING_SNAKE_CASE__				,    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=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."""				,    )
					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=SCREAMING_SNAKE_CASE__				,    default="""logs"""				,    help="""Location on where to store experiment tracking logs` and relevent project information"""				,    )
					snake_case_					:		Tuple 					=					parser.parse_args()
					snake_case_					:		Dict 					=					{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
					training_function(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
			main()









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




