
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 		UpperCAmelCase     (      a_		)						->      str:







				"""simple docstring"""



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





class 				UpperCAmelCase (    __lowercase    ):




	'''simple docstring'''





	def __init__(			self   :					Union[str, Any]				,A   :					str				,A   :					List[Any]=None				,A   :					List[str]=None					):
					__A       =					file_names
					__A       =					image_transform
					__A       =					label_to_id






	def __len__(			self   :					Union[str, Any]					):
					return len(self.file_names					)







	def __getitem__(			self   :					Tuple				,A   :					Any					):
					__A       =					self.file_names[idx]
					__A       =					PIL.Image.open(_a					)
					__A       =					raw_image.convert("RGB"					)
					if self.image_transform is not None:
									__A       =					self.image_transform(_a					)
					__A       =					extract_label(_a					)
					if self.label_to_id is not None:
									__A       =					self.label_to_id[label]
					return {"image": image, "label": label}





def 		UpperCAmelCase     (      a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if args.with_tracking:
								__A       =					Accelerator(
								    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with="all"						,  project_dir=args.project_dir		)
				else:
								__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"]		)
				__A       =					config['''image_size''']
				if not isinstance(__a						,  (list, tuple)		):
								__A       =					(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":
												__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

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

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

				# Build the label correspondences
				__A       =					[extract_label(__a		) for fname in file_names]
				__A       =					list(set(__a		)		)
				id_to_label.sort()
				__A       =					{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
				__A       =					np.random.permutation(len(__a		)		)
				__A       =					int(0.8 * len(__a		)		)
				__A       =					random_perm[:cut]
				__A       =					random_perm[cut:]

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

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

				# Instantiate dataloaders.
				__A       =					DataLoader(__a						,  shuffle=__a						,  batch_size=__a						,  num_workers=4		)
				__A       =					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)
				__A       =					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).
				__A       =					model.to(accelerator.device		)

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

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

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

				# Instantiate learning rate scheduler
				__A       =					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.
				__A       =					accelerator.prepare(
				    __a						,  __a						,  __a						,  __a						,  __a		)
				# We need to keep track of how many total steps we have iterated over
				__A       =					0
				# We also need to keep track of the starting epoch so files are named properly
				__A       =					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		)
												__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(__a		)[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(__a		)
												resume_step -= starting_epoch * len(__a		)

    # Now we train the model
				for epoch in range(__a						,  __a		):
								model.train()
								if args.with_tracking:
												__A       =					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
												__A       =					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
												__A       =					train_dataloader
								for batch in active_dataloader:
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												__A       =					{k: v.to(accelerator.device		) for k, v in batch.items()}
												__A       =					(batch['''image'''] - mean) / std
												__A       =					model(__a		)
												__A       =					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		):
																__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						,  __a		)
																				accelerator.save_state(__a		)
								model.eval()
								__A       =					0
								__A       =					0
								for step, batch in enumerate(__a		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												__A       =					{k: v.to(accelerator.device		) for k, v in batch.items()}
												__A       =					(batch['''image'''] - mean) / std
												with torch.no_grad():
																__A       =					model(__a		)
												__A       =					outputs.argmax(dim=-1		)
												__A       =					accelerator.gather_for_metrics((predictions, batch["label"])		)
												__A       =					predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								__A       =					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":
												__A       =					F'''epoch_{epoch}'''
												if args.output_dir is not None:
																__A       =					os.path.join(args.output_dir						,  __a		)
												accelerator.save_state(__a		)

				if args.with_tracking:
								accelerator.end_training()


def 		UpperCAmelCase     (      )						->      List[Any]:







				"""simple docstring"""



				__A       =					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"						,  )
				__A       =					parser.parse_args()
				__A       =					{'''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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
    '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
    '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
    '''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
    '''funnel-transformer/intermediate''': (
        '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
    ),
    '''funnel-transformer/intermediate-base''': (
        '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
    ),
    '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
    '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
    '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
    '''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "funnel"
							UpperCAmelCase__			:		Tuple             =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							}


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=[4, 4, 4]			,				_a=None			,				_a=2			,				_a=7_6_8			,				_a=1_2			,				_a=6_4			,				_a=3_0_7_2			,				_a="gelu_new"			,				_a=0.1			,				_a=0.1			,				_a=0.0			,				_a=0.1			,				_a=None			,				_a=1e-9			,				_a="mean"			,				_a="relative_shift"			,				_a=True			,				_a=True			,				_a=True			,				**_a			,				)       ->       List[Any]:
												_a      :       Optional[int]   =						vocab_size
												_a      :       Dict   =						block_sizes
												_a      :       Optional[int]   =						[1] * len(_a			) if block_repeats is None else block_repeats
												assert len(_a			) == len(
												    self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
												_a      :       int   =						num_decoder_layers
												_a      :       List[str]   =						d_model
												_a      :       Optional[Any]   =						n_head
												_a      :       Tuple   =						d_head
												_a      :       Dict   =						d_inner
												_a      :       List[str]   =						hidden_act
												_a      :       int   =						hidden_dropout
												_a      :       Union[str, Any]   =						attention_dropout
												_a      :       Tuple   =						activation_dropout
												_a      :       Optional[Any]   =						initializer_range
												_a      :       Dict   =						initializer_std
												_a      :       Union[str, Any]   =						layer_norm_eps
												assert pooling_type in [
												    "mean",
												    "max",
												], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
												_a      :       Any   =						pooling_type
												assert attention_type in [
												    "relative_shift",
												    "factorized",
												], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
												_a      :       Optional[Any]   =						attention_type
												_a      :       int   =						separate_cls
												_a      :       Tuple   =						truncate_seq
												_a      :       List[Any]   =						pool_q_only

												super().__init__(**_a			)


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return sum(self.block_sizes			)


							@num_hidden_layers.setter
							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												raise NotImplementedError(
												    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''			)


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return len(self.block_sizes			)




							@num_blocks.setter
							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''			)



