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     :  List[str]					     =       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     :  int					     =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A     :  Any					     =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def      a__ (		__UpperCamelCase				):
      with open(__UpperCamelCase     ,      "rb"				) as f:
            SCREAMING_SNAKE_CASE_          =  Image.open(__UpperCamelCase				)
            return im.convert("RGB"				)


@dataclass
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						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).'''
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''A folder containing the training data.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''A folder containing the validation data.'''}					)
  lowerCamelCase__     =						field(
      default=0.15  ,      metadata={'''help''': '''Percent to split off of train for validation.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }  ,      )
  lowerCamelCase__     =						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    __A     (							self :       str   )  ->	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"""







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



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  torch.stack([example["pixel_values"] for example in examples]				)
      SCREAMING_SNAKE_CASE_          =  torch.tensor([example["labels"] for example in examples]				)
      return {"pixel_values": pixel_values, "labels": labels}


def      a__ (		):
      # 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.

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

      # 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()

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
      main()



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


