






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
lowerCamelCase : Optional[Any]						   =       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''')

lowerCamelCase : Tuple						   =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase : str						   =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def       snake_case_		(     lowerCAmelCase_       :						str      ):
       with open(lowerCAmelCase_       ,       """rb"""      ) as f:
              __lowercase      :							Tuple				=     Image.open(lowerCAmelCase_      )
              return im.convert("""RGB"""      )

@dataclass
class 		lowerCAmelCase     :

     '''simple docstring'''

     _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=0.1_5      ,       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       lowerCAmelCase				(  self				:	str							)			->  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     :

     '''simple docstring'''

     _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       snake_case_		(     lowerCAmelCase_       :						Optional[Any]      ):
       __lowercase      :							Tuple				=     torch.stack([example["""pixel_values"""] for example in examples]      )
       __lowercase      :							Dict				=     torch.tensor([example["""labels"""] for example in examples]      )
       return {"pixel_values": pixel_values, "labels": labels}
def       snake_case_		(     ):
       # 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      :							Any				=     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      :							List[str]				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
       else:
              __lowercase      :							Optional[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"""       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

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

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

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

       def train_transforms(lowerCAmelCase_       :						Tuple      ):
              __lowercase      :							int				=     [
                  _train_transforms(pil_img.convert("""RGB"""      )      ) for pil_img in example_batch["""image"""]
              ]
              return example_batch

       def val_transforms(lowerCAmelCase_       :						int      ):
              __lowercase      :							Dict				=     [_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      :							List[str]				=     (
                         dataset["""train"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
                     )
              # Set the training transforms
              dataset["train"].set_transform(lowerCAmelCase_      )

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

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

       # Training
       if training_args.do_train:
              __lowercase      :							int				=     None
              if training_args.resume_from_checkpoint is not None:
                     __lowercase      :							str				=     training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __lowercase      :							List[Any]				=     last_checkpoint
              __lowercase      :							Any				=     trainer.train(resume_from_checkpoint=lowerCAmelCase_      )
              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      :							Union[str, Any]				=     trainer.evaluate()
              trainer.log_metrics("""eval"""       ,       lowerCAmelCase_      )
              trainer.save_metrics("""eval"""       ,       lowerCAmelCase_      )

       # Write model card and (optionally) push to hub
       __lowercase      :							Optional[int]				=     {
           """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(**lowerCAmelCase_      )
       else:
              trainer.create_model_card(**lowerCAmelCase_      )


if __name__ == "__main__":
 main()







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)