import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=7				,  lowerCAmelCase_=3				,  lowerCAmelCase_=18				,  lowerCAmelCase_=30				,  lowerCAmelCase_=4_00				,  lowerCAmelCase_=True				,  lowerCAmelCase_=None				,  lowerCAmelCase_=True				,  lowerCAmelCase_=[0.5, 0.5, 0.5]				,  lowerCAmelCase_=[0.5, 0.5, 0.5]				,  )						->Dict:
        a	:     int						      =    size if size is not None else {'''height''': 18, '''width''': 18}
        a	:     Optional[Any]						      =    parent
        a	:     List[Any]						      =    batch_size
        a	:     Any						      =    num_channels
        a	:     Any						      =    image_size
        a	:     Tuple						      =    min_resolution
        a	:     Optional[int]						      =    max_resolution
        a	:     List[str]						      =    do_resize
        a	:     Dict						      =    size
        a	:     Optional[Any]						      =    do_normalize
        a	:     str						      =    image_mean
        a	:     Tuple						      =    image_std


   def _lowercase(     self     )						->Optional[int]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }






@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE( snake_case__							,   unittest.TestCase						):
   UpperCamelCase_					:    Optional[Any]			      = ViTImageProcessor if is_vision_available() else None
   def _lowercase(     self     )						->Tuple:
        a	:     List[str]						      =    ViTImageProcessingTester(self     )
   @property
   def _lowercase(     self     )						->List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def _lowercase(     self     )						->Any:
        a	:     int						      =    self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''image_mean'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''image_std'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''do_normalize'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''do_resize'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''size'''     )     )
   def _lowercase(     self     )						->Dict:
        a	:     int						      =    self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size				,  {'''height''': 18, '''width''': 18}     )

        a	:     Union[str, Any]						      =    self.image_processing_class.from_dict(self.image_processor_dict				,  size=42     )
        self.assertEqual(image_processor.size				,  {'''height''': 42, '''width''': 42}     )
   def _lowercase(     self     )						->Dict:
        pass
   def _lowercase(     self     )						->int:
        # Initialize image_processing
        a	:     List[Any]						      =    self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        a	:     int						      =    prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase_     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_				,  Image.Image     )

        # Test not batched input
        a	:     Dict						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )

        # Test batched
        a	:     Optional[Any]						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )
   def _lowercase(     self     )						->Optional[Any]:
        # Initialize image_processing
        a	:     Dict						      =    self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        a	:     Optional[int]						      =    prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase_				,  numpify=lowerCAmelCase_     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_				,  np.ndarray     )

        # Test not batched input
        a	:     Optional[Any]						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )

        # Test batched
        a	:     Any						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )


   def _lowercase(     self     )						->Tuple:
        # Initialize image_processing
        a	:     Union[str, Any]						      =    self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        a	:     Optional[Any]						      =    prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase_				,  torchify=lowerCAmelCase_     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_				,  torch.Tensor     )

        # Test not batched input
        a	:     Union[str, Any]						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )

        # Test batched
        a	:     List[str]						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,  )
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase				 =   datasets.utils.logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilderConfig						):
   UpperCamelCase_					:    bool			      = None
   UpperCamelCase_					:    bool			      = None






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilder						):
   UpperCamelCase_					:    List[str]			      = datasets.Image()
   UpperCamelCase_					:    Any			      = 'image'
   UpperCamelCase_					:    str			      = ImageFolderConfig
   UpperCamelCase_					:    List[str]  # definition at the bottom of the script
   UpperCamelCase_					:    Union[str, Any]			      = ImageClassification(image_column='image'							,   label_column='label'						)

UpperCAmelCase				 =   [
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
UpperCAmelCase				 =   IMAGE_EXTENSIONS