


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







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







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





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= file_names
										SCREAMING_SNAKE_CASE_						:			List[str]       						= image_transform
										SCREAMING_SNAKE_CASE_						:			Dict       						= label_to_id






							def __len__(	self    ):





										"""simple docstring"""




										return len(self.file_names    )






							def __getitem__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.file_names[idx]
										SCREAMING_SNAKE_CASE_						:			Tuple       						= PIL.Image.open(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= raw_image.convert('RGB'    )
										if self.image_transform is not None:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.image_transform(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= extract_label(lowerCAmelCase__    )
										if self.label_to_id is not None:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= self.label_to_id[label]
										return {"image": image, "label": label}







def 		a__       (							A__,	A__							):
			# Initialize accelerator
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision							)

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

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

			# Build the label correspondences
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [extract_label(A__							) for fname in file_names]
			SCREAMING_SNAKE_CASE_						:			Dict       						= list(set(A__							)							)
			id_to_label.sort()
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= {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
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= np.random.permutation(len(A__							)							)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= int(0.8 * len(A__							)							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= random_perm[:cut]
			SCREAMING_SNAKE_CASE_						:			Dict       						= random_perm[cut:]

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

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE_						:			Dict       						= DataLoader(A__,	shuffle=A__,	batch_size=A__,	num_workers=4							)
			SCREAMING_SNAKE_CASE_						:			Tuple       						= 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)
			SCREAMING_SNAKE_CASE_						:			Dict       						= 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).
			SCREAMING_SNAKE_CASE_						:			List[str]       						= model.to(accelerator.device							)

			# Freezing the base model
			for param in model.parameters():
						SCREAMING_SNAKE_CASE_						:			Any       						= False
			for param in model.get_classifier().parameters():
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True

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

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

			# Instantiate learning rate scheduler
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 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.
			SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= accelerator.prepare(
			    A__,	A__,	A__,	A__,	A__							)

			# Now we train the model
			for epoch in range(A__							):
						model.train()
						for step, batch in enumerate(A__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									SCREAMING_SNAKE_CASE_						:			Tuple       						= {k: v.to(accelerator.device							) for k, v in batch.items()}
									SCREAMING_SNAKE_CASE_						:			List[str]       						= (batch['image'] - mean) / std
									SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(A__							)
									SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.nn.functional.cross_entropy(A__,	batch['label']							)
									accelerator.backward(A__							)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

						model.eval()
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0
						for _, batch in enumerate(A__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									SCREAMING_SNAKE_CASE_						:			List[str]       						= {k: v.to(accelerator.device							) for k, v in batch.items()}
									SCREAMING_SNAKE_CASE_						:			int       						= (batch['image'] - mean) / std
									with torch.no_grad():
												SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(A__							)
									SCREAMING_SNAKE_CASE_						:			Tuple       						= outputs.argmax(dim=-1							)
									SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= accelerator.gather_for_metrics((predictions, batch['label'])							)
									SCREAMING_SNAKE_CASE_						:			Dict       						= predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						SCREAMING_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}'''							)






def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			str       						= argparse.ArgumentParser(description='Simple example of training script.'							)
			parser.add_argument('--data_dir',	required=A__,	help='The data folder on disk.'							)
			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(
			    '--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('--cpu',	action='store_true',	help='If passed, will train on the CPU.'							)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= parser.parse_args()
			SCREAMING_SNAKE_CASE_						:			List[str]       						= {'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()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__       :	Any     				=logging.get_logger(__name__)

lowerCAmelCase__       :	Union[str, Any]     				={
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""reformer"""
							_UpperCAmelCase     =		["""past_buckets_states"""]
							_UpperCAmelCase     =		{}






							def __init__(	self       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,		lowerCAmelCase__=1.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=[6_4, 6_4]       ,		lowerCAmelCase__=[6_4, 1_9_2]       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5_1_2       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__="relu"       ,		lowerCAmelCase__=0.05       ,		lowerCAmelCase__=2_5_6       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=1E-12       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=0.05       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=6_4       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=4_0_9_6       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=3_2_0       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= hash_seed
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			Any       						= attention_head_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			Any       						= num_hashes
										SCREAMING_SNAKE_CASE_						:			int       						= len(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= tuple(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__       ,		lowerCAmelCase__    ) else num_buckets
										SCREAMING_SNAKE_CASE_						:			int       						= lsh_attn_chunk_length
										SCREAMING_SNAKE_CASE_						:			List[str]       						= local_attn_chunk_length
										SCREAMING_SNAKE_CASE_						:			Tuple       						= lsh_num_chunks_after
										SCREAMING_SNAKE_CASE_						:			str       						= lsh_num_chunks_before
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= local_num_chunks_after
										SCREAMING_SNAKE_CASE_						:			Any       						= local_num_chunks_before
										SCREAMING_SNAKE_CASE_						:			Any       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= feed_forward_size
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Tuple       						= lsh_attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Any       						= local_attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			str       						= max_position_embeddings
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			List[str]       						= axial_pos_embds
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tuple(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= tuple(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= axial_norm_std
										SCREAMING_SNAKE_CASE_						:			Any       						= chunk_size_lm_head
										SCREAMING_SNAKE_CASE_						:			int       						= attn_layers
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= use_cache
										SCREAMING_SNAKE_CASE_						:			Tuple       						= classifier_dropout
										super().__init__(
										    pad_token_id=lowerCAmelCase__       ,		eos_token_id=lowerCAmelCase__       ,		is_decoder=lowerCAmelCase__       ,		tie_word_embeddings=lowerCAmelCase__       ,		**lowerCAmelCase__       ,		)





