






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_SCREAMING_SNAKE_CASE   =  {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       def __init__(				self	:List[str],      snake_case	:Tuple):



          """simple docstring"""

          super().__init__()
          _lowercase              =torchvision.models.resnetaaa(pretrained=snake_case)
          _lowercase              =list(model.children())[:-2]
          _lowercase              =nn.Sequential(*snake_case)
          _lowercase              =nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.pool(self.model(snake_case))
          _lowercase              =torch.flatten(snake_case,      start_dim=2)
          _lowercase              =out.transpose(1,      2).contiguous()
          return out  # BxNx2048






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:Tuple,      snake_case	:int,      snake_case	:Tuple,      snake_case	:Union[str, Any],      snake_case	:Any,      snake_case	:str):



          """simple docstring"""

          _lowercase              =[json.loads(snake_case) for l in open(snake_case)]
          _lowercase              =os.path.dirname(snake_case)
          _lowercase              =tokenizer
          _lowercase              =labels
          _lowercase              =len(snake_case)
          _lowercase              =max_seq_length

          _lowercase              =transforms





       def __len__(				self	:str):



          """simple docstring"""

          return len(self.data)





       def __getitem__(				self	:Tuple,      snake_case	:Optional[Any]):



          """simple docstring"""

          _lowercase              =torch.LongTensor(self.tokenizer.encode(self.data[index]['text'],      add_special_tokens=snake_case))
          _lowercase       ,       _lowercase       ,       _lowercase              =sentence[0], sentence[1:-1], sentence[-1]
          _lowercase              =sentence[: self.max_seq_length]

          _lowercase              =torch.zeros(self.n_classes)
          _lowercase              =1

          _lowercase              =Image.open(os.path.join(self.data_dir,      self.data[index]['img'])).convert('RGB')
          _lowercase              =self.transforms(snake_case)

          return {
              "image_start_token": start_token,
              "image_end_token": end_token,
              "sentence": sentence,
              "image": image,
              "label": label,
          }





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =Counter()
          for row in self.data:
             label_freqs.update(row['label'])
          return label_freqs





def        _snake_case   (_snake_case				:       List[str])					->	Optional[int]:
   _lowercase              =[len(row['sentence']) for row in batch]
   _lowercase       ,       _lowercase              =len(_snake_case), max(_snake_case)

   _lowercase              =torch.zeros(_snake_case      ,    _snake_case      ,    dtype=torch.long)
   _lowercase              =torch.zeros(_snake_case      ,    _snake_case      ,    dtype=torch.long)

   for i_batch, (input_row, length) in enumerate(zip(_snake_case      ,    _snake_case)):
      _lowercase              =input_row['sentence']
      _lowercase              =1

   _lowercase              =torch.stack([row['image'] for row in batch])
   _lowercase              =torch.stack([row['label'] for row in batch])
   _lowercase              =torch.stack([row['image_start_token'] for row in batch])
   _lowercase              =torch.stack([row['image_end_token'] for row in batch])

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def        _snake_case   ()					->	Optional[Any]:
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]





def        _snake_case   ()					->	Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(256),
           transforms.CenterCrop(224),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,    std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,    ),
       ])










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
_SCREAMING_SNAKE_CASE   =  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")

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





def        _snake_case   (_snake_case				:       str)					->	List[str]:
   with open(_snake_case      ,    'rb') as f:
      _lowercase              =Image.open(_snake_case)
      return im.convert('RGB')






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __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 				UpperCamelCase__						(				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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __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        _snake_case   (_snake_case				:       Any)					->	Dict:
   _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        _snake_case   ()					->	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.

   _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'      ,    _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()

   _lowercase              =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.
   _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=_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.
   _lowercase              =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:
      _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(_snake_case):
      _lowercase              =str(_snake_case)
      _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(_snake_case				:       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(_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      ,    )
   _lowercase              =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      ,    )
   _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(_snake_case),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ])
   _lowercase              =Compose(
       [
           Resize(_snake_case),
           CenterCrop(_snake_case),
           ToTensor(),
           normalize,
       ])

   def train_transforms(_snake_case				:       str):
      _lowercase              =[
          _train_transforms(pil_img.convert('RGB')) for pil_img in example_batch['image']
      ]
      return example_batch

   def val_transforms(_snake_case				:       Dict):
      _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(_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:
         _lowercase              =(
             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
   _lowercase              =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:
      _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=_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:
      _lowercase              =trainer.evaluate()
      trainer.log_metrics('eval'      ,    _snake_case)
      trainer.save_metrics('eval'      ,    _snake_case)

   # 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(**_snake_case)
   else:
      trainer.create_model_card(**_snake_case)


if __name__ == "__main__":
    main()


