




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__					:  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')

lowerCamelCase__					:  Optional[int]      							=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase__					:  str      							=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Optional[Any]:
      with open(__UpperCAmelCase     ,     'rb'				) as f:
            SCREAMING_SNAKE_CASE_      		=     Image.open(__UpperCAmelCase				)
            return im.convert('RGB'				)





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       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)."
     }			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "A folder containing the training data."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "A folder containing the validation data."}   )
 lowercase_							       =       field(
     default=0.1_5			,  metadata={"help": "Percent to split off of train for validation."}   )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,  )
 lowercase_							       =       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  lowerCAmelCase_    (				self    :						List[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'''







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


def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					List[str]:
      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    UpperCAmelCase_				( )   ->					Union[str, 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.

      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		:      Any				):
            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		:      Dict				):
            SCREAMING_SNAKE_CASE_      		=     [
                _train_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['image']
            ]
            return example_batch

      def val_transforms(__UpperCAmelCase		:      Any				):
            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 transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets




class A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

                    assert all(len(lowercase_					) == 512 for x in inputs.input_ids					)
                    assert all(len(lowercase_					) == 128 for x in outputs.input_ids					)

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

             # map train dataset
             _lowerCamelCase			:						Tuple            =train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             train_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             # same for validation dataset
             _lowerCamelCase			:						Tuple            =val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             val_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


