

'''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 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     with open(lowercase__,       'rb'			) as f:
          __lowercase      		=Image.open(lowercase__			)
          return im.convert('RGB'			)


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
       lowerCAmelCase_												=			field(default=A ,      metadata={"help": "A folder containing the training data."} )
       lowerCAmelCase_												=			field(default=A ,      metadata={"help": "A folder containing the validation data."} )
       lowerCAmelCase_												=			field(
           default=0.1_5 ,      metadata={"help": "Percent to split off of train for validation."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           } ,      )







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







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






def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

     __lowercase      		=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.
     __lowercase      		=None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          __lowercase      		=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:
          __lowercase      		=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:
          __lowercase      		={}
          if data_args.train_dir is not None:
               __lowercase      		=os.path.join(data_args.train_dir,       '**'			)
          if data_args.validation_dir is not None:
               __lowercase      		=os.path.join(data_args.validation_dir,       '**'			)
          __lowercase      		=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.
     __lowercase      		=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:
          __lowercase      		=dataset['train'].train_test_split(data_args.train_val_split			)
          __lowercase      		=split['train']
          __lowercase      		=split['test']

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

     # Load the accuracy metric from the datasets package
     __lowercase      		=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__    :	List[Any]			):
          return metric.compute(predictions=np.argmax(p.predictions,       axis=1			),       references=p.label_ids			)

     __lowercase      		=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,       )
     __lowercase      		=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,       )
     __lowercase      		=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:
          __lowercase      		=image_processor.size['shortest_edge']
     else:
          __lowercase      		=(image_processor.size['height'], image_processor.size['width'])
     __lowercase      		=Normalize(mean=image_processor.image_mean,       std=image_processor.image_std			)
     __lowercase      		=Compose(
         [
             RandomResizedCrop(lowercase__			),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]			)
     __lowercase      		=Compose(
         [
             Resize(lowercase__			),
             CenterCrop(lowercase__			),
             ToTensor(),
             normalize,
         ]			)

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

     def val_transforms(lowercase__    :	List[str]			):
          __lowercase      		=[_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:
               __lowercase      		=(
                   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:
               __lowercase      		=(
                   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
     __lowercase      		=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:
          __lowercase      		=None
          if training_args.resume_from_checkpoint is not None:
               __lowercase      		=training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __lowercase      		=last_checkpoint
          __lowercase      		=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:
          __lowercase      		=trainer.evaluate()
          trainer.log_metrics('eval',       lowercase__			)
          trainer.save_metrics('eval',       lowercase__			)

     # Write model card and (optionally) push to hub
     __lowercase      		={
         '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, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A )
class 	lowerCAmelCase  (   A ):
       # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
       lowerCAmelCase_												=			field(default="summarization" ,      metadata={"include_in_asdict_even_if_is_default": True} )
       lowerCAmelCase_												=			Features({"text": Value("string" )} )
       lowerCAmelCase_												=			Features({"summary": Value("string" )} )
       lowerCAmelCase_												=			"text"
       lowerCAmelCase_												=			"summary"







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return {self.text_column: "text", self.summary_column: "summary"}


