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        UpperCAmelCase_					(				unittest.TestCase     ):




    def __init__(       self	: List[Any]   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: List[Any]=7   ,   lowerCAmelCase	: int=3   ,   lowerCAmelCase	: Optional[Any]=18   ,   lowerCAmelCase	: Dict=30   ,   lowerCAmelCase	: Any=400   ,   lowerCAmelCase	: Dict=True   ,   lowerCAmelCase	: List[Any]=None   ,   lowerCAmelCase	: Optional[Any]=True   ,   lowerCAmelCase	: Optional[Any]=[0.5, 0.5, 0.5]   ,   lowerCAmelCase	: List[str]=[0.5, 0.5, 0.5]   ,   )						->			List[str]:
      a__  		=  size if size is not None else {"height": 18, "width": 18}
      a__  		=  parent
      a__  		=  batch_size
      a__  		=  num_channels
      a__  		=  image_size
      a__  		=  min_resolution
      a__  		=  max_resolution
      a__  		=  do_resize
      a__  		=  size
      a__  		=  do_normalize
      a__  		=  image_mean
      a__  		=  image_std




    def     __lowerCamelCase			(       self	: Any )						->			str:
      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        UpperCAmelCase_					(				_a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							Union[str, Any]    			=				ViTImageProcessor if is_vision_available() else None




    def     __lowerCamelCase			(       self	: str )						->			Optional[int]:
      a__  		=  ViTImageProcessingTester(self )




    @property
    def     __lowerCamelCase			(       self	: Any )						->			Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()




    def     __lowerCamelCase			(       self	: List[str] )						->			Union[str, Any]:
      a__  		=  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     __lowerCamelCase			(       self	: Optional[Any] )						->			List[Any]:
      a__  		=  self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size   ,   {"height": 18, "width": 18} )

      a__  		=  self.image_processing_class.from_dict(self.image_processor_dict   ,   size=42 )
      self.assertEqual(image_processor.size   ,   {"height": 42, "width": 42} )




    def     __lowerCamelCase			(       self	: Any )						->			int:
      pass




    def     __lowerCamelCase			(       self	: str )						->			Optional[Any]:
      # Initialize image_processing
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      a__  		=  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__  		=  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__  		=  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     __lowerCamelCase			(       self	: int )						->			List[Any]:
      # Initialize image_processing
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      a__  		=  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__  		=  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__  		=  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     __lowerCamelCase			(       self	: Optional[Any] )						->			Dict:
      # Initialize image_processing
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      a__  		=  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__  		=  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__  		=  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


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







class        UpperCAmelCase_					(				folder_based_builder.FolderBasedBuilderConfig     ):
    UpperCAmelCase_      :							bool    			=				None
    UpperCAmelCase_      :							bool    			=				None







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

_SCREAMING_SNAKE_CASE     =     [
    """.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""",
]
_SCREAMING_SNAKE_CASE     =     IMAGE_EXTENSIONS

