



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      (    _lowerCAmelCase						)			->    Dict:



    """simple docstring"""






    A					:			Union[str, Any]		=  fname.split(os.path.sep						)[-1]
    return re.search(R"""^(.*)_\d+\.jpg$""" ,	__A						).groups()[0]






class SCREAMING_SNAKE_CASE__	(						__UpperCamelCase       ):




    '''simple docstring'''






    def __init__(			self,						lowerCamelCase__,						lowerCamelCase__=None,						lowerCamelCase__=None						):
        A					:			Optional[Any]		=  file_names
        A					:			Tuple		=  image_transform
        A					:			List[str]		=  label_to_id






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






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




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase						)			->    Optional[Any]:



    """simple docstring"""






    if args.with_tracking:
        A					:			int		=  Accelerator(
            cpu=args.cpu ,	mixed_precision=args.mixed_precision ,	log_with="""all""" ,	project_dir=args.project_dir						)
    else:
        A					:			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
    A					:			Union[str, Any]		=  config["lr"]
    A					:			List[str]		=  int(config["""num_epochs"""]						)
    A					:			Any		=  int(config["""seed"""]						)
    A					:			Union[str, Any]		=  int(config["""batch_size"""]						)
    A					:			Tuple		=  config["image_size"]
    if not isinstance(__A ,	(list, tuple)						):
        A					:			Tuple		=  (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					:			List[str]		=  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            A					:			Optional[int]		=  int(args.checkpointing_steps						)
        else:
            raise ValueError(
                f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
    else:
        A					:			List[str]		=  None

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

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

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

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

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

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

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

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

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

    # Instantiate learning rate scheduler
    A					:			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.
    A					:			List[str]		=  accelerator.prepare(
        __A ,	__A ,	__A ,	__A ,	__A						)
    # We need to keep track of how many total steps we have iterated over
    A					:			Optional[int]		=  0
    # We also need to keep track of the starting epoch so files are named properly
    A					:			int		=  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					:			Any		=  os.path.basename(args.resume_from_checkpoint						)
        else:
            # Get the most recent checkpoint
            A					:			Optional[int]		=  [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(__A						)[0]

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

        A					:			str		=  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(__A						),
                    """epoch""": epoch,
                } ,	step=__A ,	)
        if checkpointing_steps == "epoch":
            A					:			Dict		=  f'''epoch_{epoch}'''
            if args.output_dir is not None:
                A					:			List[str]		=  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					:			int		=  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					:			str		=  parser.parse_args()
    A					:			int		=  {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
    training_function(__A ,	__A						)


if __name__ == "__main__":
     main()










from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCAmelCase__      			=						logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase      )
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					int ,					**__UpperCAmelCase:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											requires_backends(self ,					"vision"   )
											requires_backends(self ,					"torch"   )

											if self.framework != "pt":
																raise ValueError(f'The {self.__class__} is only available in PyTorch.'   )

											self.check_model_type(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					**__UpperCAmelCase:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					{}
											__a					:						Union[str, Any]      =					{}
											__a					:						List[str]      =					{}
											# preprocess args
											if "points_per_batch" in kwargs:
																__a					:						Dict      =					kwargs["points_per_batch"]
											if "points_per_crop" in kwargs:
																__a					:						Tuple      =					kwargs["points_per_crop"]
											if "crops_n_layers" in kwargs:
																__a					:						Dict      =					kwargs["crops_n_layers"]
											if "crop_overlap_ratio" in kwargs:
																__a					:						int      =					kwargs["crop_overlap_ratio"]
											if "crop_n_points_downscale_factor" in kwargs:
																__a					:						Optional[int]      =					kwargs["crop_n_points_downscale_factor"]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																__a					:						int      =					kwargs["pred_iou_thresh"]
											if "stability_score_offset" in kwargs:
																__a					:						Any      =					kwargs["stability_score_offset"]
											if "mask_threshold" in kwargs:
																__a					:						int      =					kwargs["mask_threshold"]
											if "stability_score_thresh" in kwargs:
																__a					:						Any      =					kwargs["stability_score_thresh"]
											if "crops_nms_thresh" in kwargs:
																__a					:						int      =					kwargs["crops_nms_thresh"]
											if "output_rle_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_rle_mask"]
											if "output_bboxes_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_bboxes_mask"]
											return preprocess_kwargs, forward_params, postprocess_kwargs



						def __call__(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Union[str, Any]=None ,					**__UpperCAmelCase:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



											return super().__call__(__UpperCAmelCase ,					*__UpperCAmelCase ,					num_workers=__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=64 ,					__UpperCAmelCase:					int = 0 ,					__UpperCAmelCase:					float = 512 / 1500 ,					__UpperCAmelCase:					Optional[int] = 32 ,					__UpperCAmelCase:					Optional[int] = 1 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					load_image(__UpperCAmelCase   )
											__a					:						List[str]      =					self.image_processor.size["longest_edge"]
											__a      ,			__a      ,			__a      ,			__a					:						int      =					self.image_processor.generate_crop_boxes(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.image_processor(images=__UpperCAmelCase ,					return_tensors="pt"   )

											with self.device_placement():
																if self.framework == "pt":
																					__a					:						str      =					self.get_inference_context()
																					with inference_context():
																										__a					:						int      =					self._ensure_tensor_on_device(__UpperCAmelCase ,					device=self.device   )
																										__a					:						Union[str, Any]      =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
																										__a					:						Optional[Any]      =					image_embeddings

											__a					:						Dict      =					grid_points.shape[1]
											__a					:						Any      =					points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
																raise ValueError(
																    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
																    "To return all points at once, set points_per_batch to None"   )

											for i in range(0 ,					__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[str]      =					grid_points[:, i : i + points_per_batch, :, :]
																__a					:						str      =					input_labels[:, i : i + points_per_batch]
																__a					:						str      =					i == n_points - points_per_batch
																yield {
																    "input_points": batched_points,
																    "input_labels": labels,
																    "input_boxes": crop_boxes,
																    "is_last": is_last,
																    **model_inputs,
																}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any=0.88 ,					__UpperCAmelCase:					Optional[int]=0.95 ,					__UpperCAmelCase:					Optional[int]=0 ,					__UpperCAmelCase:					int=1 ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						List[Any]      =					model_inputs.pop("input_boxes"   )
											__a					:						Optional[Any]      =					model_inputs.pop("is_last"   )
											__a					:						Dict      =					model_inputs.pop("original_sizes"   ).tolist()
											__a					:						int      =					model_inputs.pop("reshaped_input_sizes"   ).tolist()

											__a					:						Optional[Any]      =					self.model(**__UpperCAmelCase   )

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											__a					:						Union[str, Any]      =					model_outputs["pred_masks"]
											__a					:						Optional[Any]      =					self.image_processor.post_process_masks(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					binarize=__UpperCAmelCase   )
											__a					:						Optional[Any]      =					model_outputs["iou_scores"]
											__a      ,			__a      ,			__a					:						Dict      =					self.image_processor.filter_masks(
											    masks[0] ,					iou_scores[0] ,					original_sizes[0] ,					input_boxes[0] ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					)
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=False ,					__UpperCAmelCase:					int=False ,					__UpperCAmelCase:					List[str]=0.7 ,					)    ->     Dict:




											'''simple docstring'''



											__a					:						Dict      =					[]
											__a					:						Optional[Any]      =					[]
											__a					:						Optional[Any]      =					[]
											for model_output in model_outputs:
																all_scores.append(model_output.pop("iou_scores"   )   )
																all_masks.extend(model_output.pop("masks"   )   )
																all_boxes.append(model_output.pop("boxes"   )   )

											__a					:						Any      =					torch.cat(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					torch.cat(__UpperCAmelCase   )
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					self.image_processor.post_process_for_mask_generation(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					defaultdict(__UpperCAmelCase   )
											for output in model_outputs:
																for k, v in output.items():
																					extra[k].append(__UpperCAmelCase   )

											__a					:						int      =					{}
											if output_rle_mask:
																__a					:						Optional[int]      =					rle_mask

											if output_bboxes_mask:
																__a					:						List[str]      =					bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





