

'''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_       						=							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    A_  ( snake_case ):
  with open(snake_case      ,       "rb" ) as f:
    SCREAMING_SNAKE_CASE:List[Any]						      =					Image.open(snake_case )
    return im.convert("RGB" )





@dataclass
class 							_snake_case :
   _A       :     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).'''
       }	,  )
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
   _A       :     Optional[str]       =					field(default=_a	,  metadata={'''help''': '''A folder containing the training data.'''}						)
   _A       :     Optional[str]       =					field(default=_a	,  metadata={'''help''': '''A folder containing the validation data.'''}						)
   _A       :     Optional[float]       =					field(
       default=0.15	,  metadata={'''help''': '''Percent to split off of train for validation.'''}						)
   _A       :     Optional[int]       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       }	,  )
   _A       :     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     __UpperCamelCase						( self			:      List[str]							):
     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 							_snake_case :
   _A       :     str       =					field(
       default='''google/vit-base-patch16-224-in21k'''	,  metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	,  )
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_a						)}	,  )
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}						)
   _A       :     str       =					field(
       default='''main'''	,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,  )
   _A       :     str       =					field(default=_a	,  metadata={'''help''': '''Name or path of preprocessor config.'''}						)
   _A       :     bool       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }	,  )
   _A       :     bool       =					field(
       default=_a	,  metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}	,  )





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					torch.stack([example["pixel_values"] for example in examples] )
  SCREAMING_SNAKE_CASE:Any						      =					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:Optional[int]						      =					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:str						      =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					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"      ,       snake_case      ,       snake_case )

  # 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:Optional[Any]						      =					training_args.get_process_log_level()
  logger.setLevel(snake_case )
  transformers.utils.logging.set_verbosity(snake_case )
  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:str						      =					None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    SCREAMING_SNAKE_CASE: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:
    SCREAMING_SNAKE_CASE: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:
    SCREAMING_SNAKE_CASE:Any						      =					{}
    if data_args.train_dir is not None:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					os.path.join(data_args.train_dir      ,       "**" )
    if data_args.validation_dir is not None:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					os.path.join(data_args.validation_dir      ,       "**" )
    SCREAMING_SNAKE_CASE:Tuple						      =					load_dataset(
        "imagefolder"      ,       data_files=snake_case      ,       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:Optional[int]						      =					None if "validation" in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split      ,       snake_case ) and data_args.train_val_split > 0.0:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					dataset["train"].train_test_split(data_args.train_val_split )
    SCREAMING_SNAKE_CASE:List[str]						      =					split["train"]
    SCREAMING_SNAKE_CASE:List[Any]						      =					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:Tuple						      =					dataset["train"].features["labels"].names
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					{}, {}
  for i, label in enumerate(snake_case ):
    SCREAMING_SNAKE_CASE:Any						      =					str(snake_case )
    SCREAMING_SNAKE_CASE:str						      =					label

  # Load the accuracy metric from the datasets package
  SCREAMING_SNAKE_CASE:Optional[int]						      =					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 )

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

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

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

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

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

  # Training
  if training_args.do_train:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					None
    if training_args.resume_from_checkpoint is not None:
      SCREAMING_SNAKE_CASE:Tuple						      =					training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      SCREAMING_SNAKE_CASE:Any						      =					last_checkpoint
    SCREAMING_SNAKE_CASE:Tuple						      =					trainer.train(resume_from_checkpoint=snake_case )
    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:Tuple						      =					trainer.evaluate()
    trainer.log_metrics("eval"      ,       snake_case )
    trainer.save_metrics("eval"      ,       snake_case )

  # Write model card and (optionally) push to hub
  SCREAMING_SNAKE_CASE: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(**snake_case )
  else:
    trainer.create_model_card(**snake_case )


if __name__ == "__main__":
    main()









'''simple docstring'''

from ....utils import logging


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=2_048							):
     SCREAMING_SNAKE_CASE:int						      =					config.__dict__
     SCREAMING_SNAKE_CASE:List[str]						      =					modal_hidden_size
     if num_labels:
       SCREAMING_SNAKE_CASE:str						      =					num_labels






