
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_			(				snake_case				: Union[str, Any]			)   ->     Optional[Any]:



						'''simple docstring'''

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      file_names
													__UpperCamelCase  	=      image_transform
													__UpperCamelCase  	=      label_to_id



							def __len__(	self      )-> Any:





													'''simple docstring'''






													return len(self.file_names      )



							def __getitem__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






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





def 						A_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     int:



						'''simple docstring'''

						__UpperCamelCase  	=      Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision			)

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

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

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

						# For training we use a simple RandomResizedCrop
						__UpperCamelCase  	=      Compose([RandomResizedCrop(snake_case			,   scale=(0.5, 1.0)			), ToTensor()]			)
						__UpperCamelCase  	=      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
						__UpperCamelCase  	=      Compose([Resize(snake_case			), ToTensor()]			)
						__UpperCamelCase  	=      PetsDataset([file_names[i] for i in eval_split]			,   image_transform=snake_case			,   label_to_id=snake_case			)

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

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

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

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

						# Instantiate learning rate scheduler
						__UpperCamelCase  	=      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.
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      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`.
																		__UpperCamelCase  	=      {k: v.to(accelerator.device			) for k, v in batch.items()}
																		__UpperCamelCase  	=      (batch['''image'''] - mean) / std
																		__UpperCamelCase  	=      model(snake_case			)
																		__UpperCamelCase  	=      torch.nn.functional.cross_entropy(snake_case			,   batch['''label''']			)
																		accelerator.backward(snake_case			)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

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

												__UpperCamelCase  	=      accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"			)




def 						A_			(				)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      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.'''			)
						__UpperCamelCase  	=      parser.parse_args()
						__UpperCamelCase  	=      {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
						training_function(snake_case			,   snake_case			)


if __name__ == "__main__":
				main()





from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import (
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


_lowerCAmelCase        =       Dict[str, Any]
_lowerCAmelCase        =       List[Prediction]






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

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

        requires_backends(self ,       '''vision'''      )
        self.check_model_type(
            dict(
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				load_image(__magic_name__      )
        A_							:       int	      =				[(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
               if subtask is None:
                      A_							:       int	      =				{}
               else:
                      A_							:       str	      =				{'''task_inputs''': [subtask]}
               A_							:       List[Any]	      =				self.image_processor(images=[image] ,       return_tensors='''pt''' ,       **__magic_name__      )
               A_							:       int	      =				self.tokenizer(
                   inputs['''task_inputs'''] ,       padding='''max_length''' ,       max_length=self.model.config.task_seq_len ,       return_tensors=self.framework ,       )['''input_ids''']
        else:
               A_							:       Any	      =				self.image_processor(images=[image] ,       return_tensors='''pt'''      )
        A_							:       Optional[int]	      =				target_size
        return inputs
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

        if fn is not None:
               A_							:       Dict	      =				fn(
                   __magic_name__ ,       threshold=__magic_name__ ,       mask_threshold=__magic_name__ ,       overlap_mask_area_threshold=__magic_name__ ,       target_sizes=model_outputs['''target_size'''] ,       )[0]

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

               for segment in outputs["segments_info"]:
                      A_							:       Optional[int]	      =				(segmentation == segment['''id''']) * 255
                      A_							:       int	      =				Image.fromarray(mask.numpy().astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       Union[str, Any]	      =				self.model.config.idalabel[segment['''label_id''']]
                      A_							:       Optional[int]	      =				segment['''score''']
                      annotation.append({'''score''': score, '''label''': label, '''mask''': mask}      )

        elif subtask in {"semantic", None} and hasattr(self.image_processor ,       '''post_process_semantic_segmentation'''      ):
               A_							:       int	      =				self.image_processor.post_process_semantic_segmentation(
                   __magic_name__ ,       target_sizes=model_outputs['''target_size''']      )[0]

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				self.model.config.idalabel[label]
                      annotation.append({'''score''': None, '''label''': label, '''mask''': mask}      )
        else:
               raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model      )}"""      )
        return annotation


