



'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCamelCase											=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

_UpperCamelCase											=		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCamelCase											=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 			a_	(				_lowerCAmelCase			) -> str:
  with open(_lowerCAmelCase			,'rb'			) as f:
    __lowerCamelCase			:			Optional[Any]       			=					Image.open(_lowerCAmelCase			)
    return im.convert('RGB'			)






@dataclass
class    lowerCamelCase_       :
  """simple docstring"""




  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={
          """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
      }  ,  )
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
  a_				   =field(default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """A folder containing the training data."""}	)
  a_				   =field(default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """A folder containing the validation data."""}	)
  a_				   =field(
      default=0.15  ,  metadata={"""help""": """Percent to split off of train for validation."""}	)
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }  ,  )
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }  ,  )




  def 		_lowercase	(					self :      Tuple  )   ->   Any:
    if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
      raise ValueError(
          'You must specify either a dataset name from the hub or a train and/or validation directory.'  )






@dataclass
class    lowerCamelCase_       :
  """simple docstring"""




  a_				   =field(
      default="""google/vit-base-patch16-224-in21k"""  ,  metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  ,  )
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(SCREAMING_SNAKE_CASE_	)}  ,  )
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}	)
  a_				   =field(
      default="""main"""  ,  metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,  )
  a_				   =field(default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """Name or path of preprocessor config."""}	)
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }  ,  )
  a_				   =field(
      default=SCREAMING_SNAKE_CASE_  ,  metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}  ,  )




def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			Any       			=					torch.stack([example['pixel_values'] for example in examples]			)
  __lowerCamelCase			:			Dict       			=					torch.tensor([example['labels'] for example in examples]			)
  return {"pixel_values": pixel_values, "labels": labels}

def 			a_	(				) -> Optional[int]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  __lowerCamelCase			:			Optional[Any]       			=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
  if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry('run_image_classification'			,_lowerCAmelCase			,_lowerCAmelCase			)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,datefmt='%m/%d/%Y %H:%M:%S'			,handlers=[logging.StreamHandler(sys.stdout			)]			,)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  __lowerCamelCase			:			Any       			=					training_args.get_process_log_level()
  logger.setLevel(_lowerCAmelCase			)
  transformers.utils.logging.set_verbosity(_lowerCAmelCase			)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
      + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
  logger.info(F'Training/evaluation parameters {training_args}'			)

  # Detecting last checkpoint.
  __lowerCamelCase			:			Tuple       			=					None
  if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
    __lowerCamelCase			:			Dict       			=					get_last_checkpoint(training_args.output_dir			)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
      raise ValueError(
          F'Output directory ({training_args.output_dir}) already exists and is not empty. '
          'Use --overwrite_output_dir to overcome.'			)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'			)

    # Set seed before initializing model.
  set_seed(training_args.seed			)

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    __lowerCamelCase			:			List[Any]       			=					load_dataset(
        data_args.dataset_name			,data_args.dataset_config_name			,cache_dir=model_args.cache_dir			,task='image-classification'			,use_auth_token=True if model_args.use_auth_token else None			,)
  else:
    __lowerCamelCase			:			int       			=					{}
    if data_args.train_dir is not None:
      __lowerCamelCase			:			Dict       			=					os.path.join(data_args.train_dir			,'**'			)
    if data_args.validation_dir is not None:
      __lowerCamelCase			:			List[Any]       			=					os.path.join(data_args.validation_dir			,'**'			)
    __lowerCamelCase			:			List[str]       			=					load_dataset(
        'imagefolder'			,data_files=_lowerCAmelCase			,cache_dir=model_args.cache_dir			,task='image-classification'			,)

  # If we don't have a validation split, split off a percentage of train as validation.
  __lowerCamelCase			:			str       			=					None if 'validation' in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split			,_lowerCAmelCase			) and data_args.train_val_split > 0.0:
    __lowerCamelCase			:			List[Any]       			=					dataset['train'].train_test_split(data_args.train_val_split			)
    __lowerCamelCase			:			Any       			=					split['train']
    __lowerCamelCase			:			Any       			=					split['test']

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  __lowerCamelCase			:			Tuple       			=					dataset['train'].features['labels'].names
  __lowerCamelCase			,__lowerCamelCase			:			Any       			=					{}, {}
  for i, label in enumerate(_lowerCAmelCase			):
    __lowerCamelCase			:			Optional[Any]       			=					str(_lowerCAmelCase			)
    __lowerCamelCase			:			str       			=					label

  # Load the accuracy metric from the datasets package
  __lowerCamelCase			:			List[Any]       			=					evaluate.load('accuracy'			)

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(_lowerCAmelCase			):
    return metric.compute(predictions=np.argmax(p.predictions			,axis=1			)			,references=p.label_ids			)

  __lowerCamelCase			:			Any       			=					AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path			,num_labels=len(_lowerCAmelCase			)			,labelaid=_lowerCAmelCase			,idalabel=_lowerCAmelCase			,finetuning_task='image-classification'			,cache_dir=model_args.cache_dir			,revision=model_args.model_revision			,use_auth_token=True if model_args.use_auth_token else None			,)
  __lowerCamelCase			:			int       			=					AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path			,from_tf=bool('.ckpt' in model_args.model_name_or_path			)			,config=_lowerCAmelCase			,cache_dir=model_args.cache_dir			,revision=model_args.model_revision			,use_auth_token=True if model_args.use_auth_token else None			,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,)
  __lowerCamelCase			:			str       			=					AutoImageProcessor.from_pretrained(
      model_args.image_processor_name or model_args.model_name_or_path			,cache_dir=model_args.cache_dir			,revision=model_args.model_revision			,use_auth_token=True if model_args.use_auth_token else None			,)

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    __lowerCamelCase			:			List[str]       			=					image_processor.size['shortest_edge']
  else:
    __lowerCamelCase			:			int       			=					(image_processor.size['height'], image_processor.size['width'])
  __lowerCamelCase			:			str       			=					Normalize(mean=image_processor.image_mean			,std=image_processor.image_std			)
  __lowerCamelCase			:			List[Any]       			=					Compose(
      [
          RandomResizedCrop(_lowerCAmelCase			),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]			)
  __lowerCamelCase			:			Any       			=					Compose(
      [
          Resize(_lowerCAmelCase			),
          CenterCrop(_lowerCAmelCase			),
          ToTensor(),
          normalize,
      ]			)

  def train_transforms(_lowerCAmelCase			):
    __lowerCamelCase			:			int       			=					[
        _train_transforms(pil_img.convert('RGB'			)			) for pil_img in example_batch['image']
    ]
    return example_batch

  def val_transforms(_lowerCAmelCase			):
    __lowerCamelCase			:			Optional[Any]       			=					[_val_transforms(pil_img.convert('RGB'			)			) for pil_img in example_batch['image']]
    return example_batch

  if training_args.do_train:
    if "train" not in dataset:
      raise ValueError('--do_train requires a train dataset'			)
    if data_args.max_train_samples is not None:
      __lowerCamelCase			:			str       			=					(
          dataset['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
      )
    # Set the training transforms
    dataset["train"].set_transform(_lowerCAmelCase			)

  if training_args.do_eval:
    if "validation" not in dataset:
      raise ValueError('--do_eval requires a validation dataset'			)
    if data_args.max_eval_samples is not None:
      __lowerCamelCase			:			str       			=					(
          dataset['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
      )
    # Set the validation transforms
    dataset["validation"].set_transform(_lowerCAmelCase			)

  # Initalize our trainer
  __lowerCamelCase			:			Optional[int]       			=					Trainer(
      model=_lowerCAmelCase			,args=_lowerCAmelCase			,train_dataset=dataset['train'] if training_args.do_train else None			,eval_dataset=dataset['validation'] if training_args.do_eval else None			,compute_metrics=_lowerCAmelCase			,tokenizer=_lowerCAmelCase			,data_collator=_lowerCAmelCase			,)

  # Training
  if training_args.do_train:
    __lowerCamelCase			:			str       			=					None
    if training_args.resume_from_checkpoint is not None:
      __lowerCamelCase			:			Tuple       			=					training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __lowerCamelCase			:			List[Any]       			=					last_checkpoint
    __lowerCamelCase			:			List[str]       			=					trainer.train(resume_from_checkpoint=_lowerCAmelCase			)
    trainer.save_model()
    trainer.log_metrics('train'			,train_result.metrics			)
    trainer.save_metrics('train'			,train_result.metrics			)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    __lowerCamelCase			:			str       			=					trainer.evaluate()
    trainer.log_metrics('eval'			,_lowerCAmelCase			)
    trainer.save_metrics('eval'			,_lowerCAmelCase			)

  # Write model card and (optionally) push to hub
  __lowerCamelCase			:			Dict       			=					{
      'finetuned_from': model_args.model_name_or_path,
      'tasks': 'image-classification',
      'dataset': data_args.dataset_name,
      'tags': ['image-classification', 'vision'],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_lowerCAmelCase			)
  else:
    trainer.create_model_card(**_lowerCAmelCase			)


if __name__ == "__main__":
   main()











'''simple docstring'''





from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_UpperCamelCase											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
          f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
          'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
          ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
          ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
          ' file'
      )
      deprecate('steps_offset!=1'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          f'The configuration file of this scheduler: {scheduler} has not set the configuration'
          ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
          ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
          ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
          ' Hub, it would be very nice if you could open a Pull request for the'
          ' `scheduler/scheduler_config.json` file'
      )
      deprecate('skip_prk_steps not set'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    if safety_checker is None:
      logger.warning(
          f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
          ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
          ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
          ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
          ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
          ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'  )

    self.register_modules(
        segmentation_model=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					torch.device('cuda'  )

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
      if cpu_offloaded_model is not None:
        cpu_offload(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_hf_hook'  )
          and hasattr(module._hf_hook     ,      'execution_device'  )
          and module._hf_hook.execution_device is not None
      ):
        return torch.device(module._hf_hook.execution_device  )
    return self.device




  @torch.no_grad()
  def __call__(					self :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					StableDiffusionInpaintPipeline(
        vae=self.vae     ,      text_encoder=self.text_encoder     ,      tokenizer=self.tokenizer     ,      unet=self.unet     ,      scheduler=self.scheduler     ,      safety_checker=self.safety_checker     ,      feature_extractor=self.feature_extractor     ,      )
    return inpainting_pipeline(
        prompt=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






