





"""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
a		:				Dict  			=    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		:				Any  			=    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a		:				Tuple  			=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






 '''simple docstring'''






 with open(_lowercase					, "rb"      ) as f:
  a   :	List[str]			=       Image.open(_lowercase      )
  return im.convert("RGB"      )


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






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Dict:






 '''simple docstring'''






 a   :	Tuple			=       torch.stack([example["pixel_values"] for example in examples]      )
 a   :	Dict			=       torch.tensor([example["labels"] for example in examples]      )
 return {"pixel_values": pixel_values, "labels": labels}






def    _SCREAMING_SNAKE_CASE	(			)				->Any:






 '''simple docstring'''






 a   :	str			=       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.
  a   :	Union[str, Any]			=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
 else:
  a   :	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"					, _lowercase					, _lowercase      )

 # 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()

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

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

 # Load the accuracy metric from the datasets package
 a   :	Union[str, 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(_lowercase		:					Optional[int]      ):
  return metric.compute(predictions=np.argmax(p.predictions					, axis=1      )					, references=p.label_ids      )

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

 def train_transforms(_lowercase		:					List[str]      ):
  a   :	List[Any]			=       [
      _train_transforms(pil_img.convert("RGB"      )      ) for pil_img in example_batch["image"]
  ]
  return example_batch

 def val_transforms(_lowercase		:					List[Any]      ):
  a   :	Union[str, 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:
   a   :	Optional[int]			=       (
       dataset["train"].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
   )
  # Set the training transforms
  dataset["train"].set_transform(_lowercase      )

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

 # Initalize our trainer
 a   :	Union[str, Any]			=       Trainer(
     model=_lowercase					, args=_lowercase					, train_dataset=dataset["train"] if training_args.do_train else None					, eval_dataset=dataset["validation"] if training_args.do_eval else None					, compute_metrics=_lowercase					, tokenizer=_lowercase					, data_collator=_lowercase					, )

 # Training
 if training_args.do_train:
  a   :	List[str]			=       None
  if training_args.resume_from_checkpoint is not None:
   a   :	List[str]			=       training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   a   :	Dict			=       last_checkpoint
  a   :	List[str]			=       trainer.train(resume_from_checkpoint=_lowercase      )
  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:
  a   :	Dict			=       trainer.evaluate()
  trainer.log_metrics("eval"					, _lowercase      )
  trainer.save_metrics("eval"					, _lowercase      )

 # Write model card and (optionally) push to hub
 a   :	List[str]			=       {
     "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(**_lowercase      )
 else:
  trainer.create_model_card(**_lowercase      )


if __name__ == "__main__":
  main()









"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


