



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
_lowercase						  =		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")

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Tuple:
    with open(UpperCamelCase_ ,       '''rb'''	) as f:
        A__		 =					Image.open(UpperCamelCase_	)
        return im.convert('''RGB'''	)



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







def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  Union[str, Any]:
    A__		 =					torch.stack([example['''pixel_values'''] for example in examples]	)
    A__		 =					torch.tensor([example['''labels'''] for example in examples]	)
    return {"pixel_values": pixel_values, "labels": labels}



def 		lowerCAmelCase__    (					)->  Optional[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.

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

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

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

    # Load the accuracy metric from the datasets package
    A__		 =					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	)

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

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

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

    # Write model card and (optionally) push to hub
    A__		 =					{
        '''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()





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:



   class     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					depth_estimator(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ])
         self.assertEqual(
             [
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
