




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
a    				=      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")

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




def 							_SCREAMING_SNAKE_CASE   (		snake_case							)    ->					List[Any]:
						with open(a__      ,  """rb"""							) as f:
												_UpperCAmelCase	     =      Image.open(a__							)
												return im.convert("""RGB"""							)




@dataclass
class    _A					:
		__a            =							field(
		    default=_lowercase							,  metadata={
		        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
		    }							,  )
		__a            =							field(
		    default=_lowercase							,  metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
		__a            =							field(default=_lowercase							,  metadata={"""help""": """A folder containing the training data."""}  )
		__a            =							field(default=_lowercase							,  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=_lowercase							,  metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of training examples to this """
		            """value if set."""
		        )
		    }							,  )
		__a            =							field(
		    default=_lowercase							,  metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
		            """value if set."""
		        )
		    }							,  )






		def 					UpperCAmelCase			(     self      ):
								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    _A					:
		__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=_lowercase							,  metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_lowercase  )}							,  )
		__a            =							field(
		    default=_lowercase							,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		__a            =							field(
		    default=_lowercase							,  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=_lowercase							,  metadata={"""help""": """Name or path of preprocessor config."""}  )
		__a            =							field(
		    default=_lowercase							,  metadata={
		        """help""": (
		            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
		            """with private models)."""
		        )
		    }							,  )
		__a            =							field(
		    default=_lowercase							,  metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}							,  )




def 							_SCREAMING_SNAKE_CASE   (		snake_case							)    ->					Tuple:
						_UpperCAmelCase	     =      torch.stack([example["""pixel_values"""] for example in examples]							)
						_UpperCAmelCase	     =      torch.tensor([example["""labels"""] for example in examples]							)
						return {"pixel_values": pixel_values, "labels": labels}




def 							_SCREAMING_SNAKE_CASE   (		)    ->					List[Any]:
						# 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.

						_UpperCAmelCase	     =      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.
												_UpperCAmelCase	     =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
						else:
												_UpperCAmelCase	     =      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"""      ,  a__      ,  a__							)

						# 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()

						_UpperCAmelCase	     =      training_args.get_process_log_level()
						logger.setLevel(a__							)
						transformers.utils.logging.set_verbosity(a__							)
						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.
						_UpperCAmelCase	     =      None
						if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
												_UpperCAmelCase	     =      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:
												_UpperCAmelCase	     =      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:
												_UpperCAmelCase	     =      {}
												if data_args.train_dir is not None:
																		_UpperCAmelCase	     =      os.path.join(data_args.train_dir      ,  """**"""							)
												if data_args.validation_dir is not None:
																		_UpperCAmelCase	     =      os.path.join(data_args.validation_dir      ,  """**"""							)
												_UpperCAmelCase	     =      load_dataset(
												    """imagefolder"""      ,  data_files=a__      ,  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.
						_UpperCAmelCase	     =      None if """validation""" in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split      ,  a__							) and data_args.train_val_split > 0.0:
												_UpperCAmelCase	     =      dataset["""train"""].train_test_split(data_args.train_val_split							)
												_UpperCAmelCase	     =      split["""train"""]
												_UpperCAmelCase	     =      split["""test"""]

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

						# Load the accuracy metric from the datasets package
						_UpperCAmelCase	     =      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(snake_case							):
												return metric.compute(predictions=np.argmax(p.predictions      ,  axis=1							)      ,  references=p.label_ids							)

						_UpperCAmelCase	     =      AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path      ,  num_labels=len(a__							)      ,  labelaid=a__      ,  idalabel=a__      ,  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      ,  )
						_UpperCAmelCase	     =      AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path      ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)      ,  config=a__      ,  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      ,  )
						_UpperCAmelCase	     =      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:
												_UpperCAmelCase	     =      image_processor.size["""shortest_edge"""]
						else:
												_UpperCAmelCase	     =      (image_processor.size["""height"""], image_processor.size["""width"""])
						_UpperCAmelCase	     =      Normalize(mean=image_processor.image_mean      ,  std=image_processor.image_std							)
						_UpperCAmelCase	     =      Compose(
						    [
						        RandomResizedCrop(a__							),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]							)
						_UpperCAmelCase	     =      Compose(
						    [
						        Resize(a__							),
						        CenterCrop(a__							),
						        ToTensor(),
						        normalize,
						    ]							)

						def train_transforms(snake_case							):
												_UpperCAmelCase	     =      [
												    _train_transforms(pil_img.convert("""RGB"""							)							) for pil_img in example_batch["""image"""]
												]
												return example_batch

						def val_transforms(snake_case							):
												_UpperCAmelCase	     =      [_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:
																		_UpperCAmelCase	     =      (
																		    dataset["""train"""].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
																		)
												# Set the training transforms
												dataset["train"].set_transform(a__							)

						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:
																		_UpperCAmelCase	     =      (
																		    dataset["""validation"""].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
																		)
												# Set the validation transforms
												dataset["validation"].set_transform(a__							)

						# Initalize our trainer
						_UpperCAmelCase	     =      Trainer(
						    model=a__      ,  args=a__      ,  train_dataset=dataset["""train"""] if training_args.do_train else None      ,  eval_dataset=dataset["""validation"""] if training_args.do_eval else None      ,  compute_metrics=a__      ,  tokenizer=a__      ,  data_collator=a__      ,  )

						# Training
						if training_args.do_train:
												_UpperCAmelCase	     =      None
												if training_args.resume_from_checkpoint is not None:
																		_UpperCAmelCase	     =      training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		_UpperCAmelCase	     =      last_checkpoint
												_UpperCAmelCase	     =      trainer.train(resume_from_checkpoint=a__							)
												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:
												_UpperCAmelCase	     =      trainer.evaluate()
												trainer.log_metrics("""eval"""      ,  a__							)
												trainer.save_metrics("""eval"""      ,  a__							)

						# Write model card and (optionally) push to hub
						_UpperCAmelCase	     =      {
						    """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(**a__							)
						else:
												trainer.create_model_card(**a__							)


if __name__ == "__main__":
				main()


import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)





