


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





class 							__UpperCamelCase				(		lowerCAmelCase_					):




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

						'''simple docstring'''





						__a			:       Any					    =  file_names
						__a			:       List[str]					    =  image_transform
						__a			:       List[str]					    =  label_to_id




	def __len__(   self							):

						'''simple docstring'''





						return len(self.file_names							)





	def __getitem__(   self						,				__a							):

						'''simple docstring'''





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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					# Initialize accelerator
					if args.with_tracking:
										__a			:       Optional[int]					    =  Accelerator(
										    cpu=args.cpu						,      mixed_precision=args.mixed_precision						,      log_with='all'						,      project_dir=args.project_dir   )
					else:
										__a			:       Tuple					    =  Accelerator(cpu=args.cpu						,      mixed_precision=args.mixed_precision   )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a			:       Optional[int]					    =  config['lr']
					__a			:       Optional[Any]					    =  int(config['num_epochs']   )
					__a			:       Tuple					    =  int(config['seed']   )
					__a			:       List[str]					    =  int(config['batch_size']   )
					__a			:       Union[str, Any]					    =  config['image_size']
					if not isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										__a			:       Optional[int]					    =  (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			:       Optional[int]					    =  args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__a			:       Dict					    =  int(args.checkpointing_steps   )
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
					else:
										__a			:       Optional[Any]					    =  None

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

					# Grab all the image filenames
					__a			:       Optional[int]					    =  [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
					__a			:       List[str]					    =  [extract_label(_SCREAMING_SNAKE_CASE   ) for fname in file_names]
					__a			:       Tuple					    =  list(set(_SCREAMING_SNAKE_CASE   )   )
					id_to_label.sort()
					__a			:       Optional[Any]					    =  {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
					__a			:       int					    =  np.random.permutation(len(_SCREAMING_SNAKE_CASE   )   )
					__a			:       str					    =  int(0.8 * len(_SCREAMING_SNAKE_CASE   )   )
					__a			:       List[Any]					    =  random_perm[:cut]
					__a			:       int					    =  random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					__a			:       str					    =  Compose([RandomResizedCrop(_SCREAMING_SNAKE_CASE						,      scale=(0.5, 1.0)   ), ToTensor()]   )
					__a			:       Dict					    =  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
					__a			:       Optional[int]					    =  Compose([Resize(_SCREAMING_SNAKE_CASE   ), ToTensor()]   )
					__a			:       Tuple					    =  PetsDataset([file_names[i] for i in eval_split]						,      image_transform=_SCREAMING_SNAKE_CASE						,      label_to_id=_SCREAMING_SNAKE_CASE   )

					# Instantiate dataloaders.
					__a			:       Dict					    =  DataLoader(_SCREAMING_SNAKE_CASE						,      shuffle=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE						,      num_workers=4   )
					__a			:       Optional[Any]					    =  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)
					__a			:       List[Any]					    =  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).
					__a			:       Optional[int]					    =  model.to(accelerator.device   )

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

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

					# Instantiate optimizer
					__a			:       Any					    =  torch.optim.Adam(params=model.parameters()						,      lr=lr / 25   )

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

										if "epoch" in training_difference:
															__a			:       Optional[int]					    =  int(training_difference.replace('epoch_'						,      ''   )   ) + 1
															__a			:       str					    =  None
										else:
															__a			:       str					    =  int(training_difference.replace('step_'						,      ''   )   )
															__a			:       Union[str, 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:
															__a			:       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
															__a			:       int					    =  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
															__a			:       Any					    =  train_dataloader
										for batch in active_dataloader:
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															__a			:       int					    =  {k: v.to(accelerator.device   ) for k, v in batch.items()}
															__a			:       Tuple					    =  (batch['image'] - mean) / std
															__a			:       List[str]					    =  model(_SCREAMING_SNAKE_CASE   )
															__a			:       List[Any]					    =  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   ):
																				__a			:       Optional[int]					    =  F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__a			:       List[Any]					    =  os.path.join(args.output_dir						,      _SCREAMING_SNAKE_CASE   )
																									accelerator.save_state(_SCREAMING_SNAKE_CASE   )
										model.eval()
										__a			:       str					    =  0
										__a			:       List[Any]					    =  0
										for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															__a			:       Dict					    =  {k: v.to(accelerator.device   ) for k, v in batch.items()}
															__a			:       Tuple					    =  (batch['image'] - mean) / std
															with torch.no_grad():
																				__a			:       List[Any]					    =  model(_SCREAMING_SNAKE_CASE   )
															__a			:       str					    =  outputs.argmax(dim=-1   )
															__a    ,				__a			:       Dict					    =  accelerator.gather_for_metrics((predictions, batch['label'])   )
															__a			:       Optional[int]					    =  predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										__a			:       int					    =  accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""   )
										if args.with_tracking:
															accelerator.log(
															    {
															        'accuracy': 100 * eval_metric,
															        'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE   ),
															        'epoch': epoch,
															    }						,      step=_SCREAMING_SNAKE_CASE						,      )
										if checkpointing_steps == "epoch":
															__a			:       str					    =  F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__a			:       Tuple					    =  os.path.join(args.output_dir						,      _SCREAMING_SNAKE_CASE   )
															accelerator.save_state(_SCREAMING_SNAKE_CASE   )

					if args.with_tracking:
										accelerator.end_training()






def   lowerCamelCase							():
					__a			:       Optional[int]					    =  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'						,      )
					__a			:       Tuple					    =  parser.parse_args()
					__a			:       int					    =  {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()










"""simple docstring"""
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:






		class 					_UpperCAmelCase   :


							@staticmethod
							def 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  depth_estimator(
												    [
												        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        # RGBA
												        dataset[0]["file"],
												        # LA
												        dataset[1]["file"],
												        # L
												        dataset[2]["file"],
												    ]		)
												self.assertEqual(
												    [
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


					@slow
					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  "Intel/dpt-large"
												A		=  pipeline("depth-estimation"     ,       model=__UpperCamelCase		)
												A		=  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
												A		=  hashimage(outputs["depth"]		)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)     ,       29.304		)
												self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)     ,       2.662		)


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# This is highly irregular to have no small tests.
												self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)


