from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


lowercase__    :       Tuple						=			logging.get_logger(__name__)

lowercase__    :       Tuple						=			{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
	import detectrona




class      lowercase_						(						lowercase_		):
	UpperCAmelCase_						:      Optional[int]     =       """layoutlmv2"""


	def __init__(							self	,    __SCREAMING_SNAKE_CASE=30522	,    __SCREAMING_SNAKE_CASE=768	,    __SCREAMING_SNAKE_CASE=12	,    __SCREAMING_SNAKE_CASE=12	,    __SCREAMING_SNAKE_CASE=3072	,    __SCREAMING_SNAKE_CASE="gelu"	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=512	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=0.0_2	,    __SCREAMING_SNAKE_CASE=1e-12	,    __SCREAMING_SNAKE_CASE=0	,    __SCREAMING_SNAKE_CASE=1024	,    __SCREAMING_SNAKE_CASE=128	,    __SCREAMING_SNAKE_CASE=32	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=256	,    __SCREAMING_SNAKE_CASE=64	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=[7, 7, 256]	,    __SCREAMING_SNAKE_CASE=128	,    __SCREAMING_SNAKE_CASE=128	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=False	,    __SCREAMING_SNAKE_CASE=None	,    **__SCREAMING_SNAKE_CASE	,    )		->List[Any]:
								super().__init__(
								    vocab_size=__SCREAMING_SNAKE_CASE	,    hidden_size=__SCREAMING_SNAKE_CASE	,    num_hidden_layers=__SCREAMING_SNAKE_CASE	,    num_attention_heads=__SCREAMING_SNAKE_CASE	,    intermediate_size=__SCREAMING_SNAKE_CASE	,    hidden_act=__SCREAMING_SNAKE_CASE	,    hidden_dropout_prob=__SCREAMING_SNAKE_CASE	,    attention_probs_dropout_prob=__SCREAMING_SNAKE_CASE	,    max_position_embeddings=__SCREAMING_SNAKE_CASE	,    type_vocab_size=__SCREAMING_SNAKE_CASE	,    initializer_range=__SCREAMING_SNAKE_CASE	,    layer_norm_eps=__SCREAMING_SNAKE_CASE	,    pad_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
								lowerCAmelCase				=	max_ad_position_embeddings
								lowerCAmelCase				=	max_rel_pos
								lowerCAmelCase				=	rel_pos_bins
								lowerCAmelCase				=	fast_qkv
								lowerCAmelCase				=	max_rel_ad_pos
								lowerCAmelCase				=	rel_ad_pos_bins
								lowerCAmelCase				=	convert_sync_batchnorm
								lowerCAmelCase				=	image_feature_pool_shape
								lowerCAmelCase				=	coordinate_size
								lowerCAmelCase				=	shape_size
								lowerCAmelCase				=	has_relative_attention_bias
								lowerCAmelCase				=	has_spatial_attention_bias
								lowerCAmelCase				=	has_visual_segment_embedding
								lowerCAmelCase				=	(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)


	@classmethod
	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
								    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
								    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
								    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
								    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
								    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
								    "MODEL.POST_NMS_TOPK_TEST": 1000,
								    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
								    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
								    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
								    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
								    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
								    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
								    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
								    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
								    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
								    "MODEL.RESNETS.DEPTH": 101,
								    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
								    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.RESNETS.NUM_GROUPS": 32,
								    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
								    "MODEL.RESNETS.STRIDE_IN_1X1": False,
								}




	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
								lowerCAmelCase				=	detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
															lowerCAmelCase				=	k.split('''.'''       )
															lowerCAmelCase				=	detectrona_config
															for attribute in attributes[:-1]:
																						lowerCAmelCase				=	getattr(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
															setattr(__SCREAMING_SNAKE_CASE	,    attributes[-1]	,    __SCREAMING_SNAKE_CASE       )

								return detectrona_config




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_  (		snake_case__							)  ->			Optional[Any]:
							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								lowerCAmelCase				=	file_names
								lowerCAmelCase				=	image_transform
								lowerCAmelCase				=	label_to_id


	def __len__(							self       )		->Optional[int]:
								return len(self.file_names       )




	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
								lowerCAmelCase				=	self.file_names[idx]
								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
								if self.image_transform is not None:
															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
								if self.label_to_id is not None:
															lowerCAmelCase				=	self.label_to_id[label]
								return {"image": image, "label": label}

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# Initialize accelerator
							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)

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

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

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

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

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

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

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

							# Instantiate dataloaders.
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCAmelCase				=	create_model('''resnet50d'''     ,						pretrained=snake_case__     ,						num_classes=len(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).
							lowerCAmelCase				=	model.to(accelerator.device							)

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

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

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

							# Instantiate learning rate scheduler
							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(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.
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)

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

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

														lowerCAmelCase				=	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      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
							parser.add_argument(
							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=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(
							    '''--checkpointing_steps'''     ,						type=snake_case__     ,						default=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('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''							)
							lowerCAmelCase				=	parser.parse_args()
							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(snake_case__     ,						snake_case__							)


if __name__ == "__main__":
	main()



