






'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class        a_		(    unittest.TestCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=18    ,	_SCREAMING_SNAKE_CASE=30    ,	_SCREAMING_SNAKE_CASE=400    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	_SCREAMING_SNAKE_CASE=False    ,	)						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      size if size is not None else {"""height""": 20, """width""": 20}
        UpperCamelCase       						=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      min_resolution
        UpperCamelCase       						=      max_resolution
        UpperCamelCase       						=      do_resize
        UpperCamelCase       						=      size
        UpperCamelCase       						=      do_center_crop
        UpperCamelCase       						=      crop_size
        UpperCamelCase       						=      do_normalize
        UpperCamelCase       						=      image_mean
        UpperCamelCase       						=      image_std
        UpperCamelCase       						=      do_reduce_labels


  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }







def 					lowercase__				(   )->				Union[str, Any]:
      UpperCamelCase       						=      load_dataset("""hf-internal-testing/fixtures_ade20k"""	,		split="""test"""					)

      UpperCamelCase       						=      Image.open(dataset[0]["""file"""]					)
      UpperCamelCase       						=      Image.open(dataset[1]["""file"""]					)

      return image, map







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      load_dataset("""hf-internal-testing/fixtures_ade20k"""	,		split="""test"""					)

      UpperCamelCase       						=      Image.open(ds[0]["""file"""]					)
      UpperCamelCase       						=      Image.open(ds[1]["""file"""]					)
      UpperCamelCase       						=      Image.open(ds[2]["""file"""]					)
      UpperCamelCase       						=      Image.open(ds[3]["""file"""]					)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class        a_		(    lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   BeitImageProcessor if is_vision_available() else None

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      BeitImageProcessingTester(self   )

  @property
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_resize"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""size"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_center_crop"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""center_crop"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_normalize"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_mean"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_std"""   )   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size    ,	{"""height""": 20, """width""": 20}   )
        self.assertEqual(image_processor.crop_size    ,	{"""height""": 18, """width""": 18}   )
        self.assertEqual(image_processor.do_reduce_labels    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.image_processing_class.from_dict(
            self.image_processor_dict    ,	size=42    ,	crop_size=84    ,	reduce_labels=_SCREAMING_SNAKE_CASE   )
        self.assertEqual(image_processor.size    ,	{"""height""": 42, """width""": 42}   )
        self.assertEqual(image_processor.crop_size    ,	{"""height""": 84, """width""": 84}   )
        self.assertEqual(image_processor.do_reduce_labels    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	Image.Image   )

        # Test not batched input
        UpperCamelCase       						=      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	numpify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	np.ndarray   )

        # Test not batched input
        UpperCamelCase       						=      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	torchify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	torch.Tensor   )

        # Test not batched input
        UpperCamelCase       						=      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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	torchify=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      []
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	torch.Tensor   )
              maps.append(torch.zeros(image.shape[-2:]   ).long()   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	maps[0]    ,	return_tensors="""pt"""   )
        self.assertEqual(
            encoding["""pixel_values"""].shape    ,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(
            encoding["""labels"""].shape    ,	(
                1,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(encoding["""labels"""].dtype    ,	torch.long   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 255   )

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )
        self.assertEqual(
            encoding["""pixel_values"""].shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(
            encoding["""labels"""].shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(encoding["""labels"""].dtype    ,	torch.long   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 255   )

        # Test not batched input (PIL images)
        UpperCamelCase						,UpperCamelCase       						=      prepare_semantic_single_inputs()

        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )
        self.assertEqual(
            encoding["""pixel_values"""].shape    ,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(
            encoding["""labels"""].shape    ,	(
                1,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(encoding["""labels"""].dtype    ,	torch.long   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 255   )

        # Test batched input (PIL images)
        UpperCamelCase						,UpperCamelCase       						=      prepare_semantic_batch_inputs()

        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )
        self.assertEqual(
            encoding["""pixel_values"""].shape    ,	(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(
            encoding["""labels"""].shape    ,	(
                2,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)
        self.assertEqual(encoding["""labels"""].dtype    ,	torch.long   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 255   )


  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        UpperCamelCase						,UpperCamelCase       						=      prepare_semantic_single_inputs()
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 150   )

        UpperCamelCase       						=      True
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )
        self.assertTrue(encoding["""labels"""].min().item() >= 0   )
        self.assertTrue(encoding["""labels"""].max().item() <= 255   )








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            raise ValueError("""iterations must be defined as integers"""					)
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					) or not number >= 1:
            raise ValueError(
                """starting number must be
                         and integer and be more than 0"""					)
      if not iterations >= 1:
            raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""					)

      UpperCamelCase       						=      """"""
      while number <= iterations:
            if number % 3 == 0:
                  out += "Fizz"
            if number % 5 == 0:
                  out += "Buzz"
            if 0 not in (number % 3, number % 5):
                  out += str(__UpperCamelCase					)

            # print(out)
            number += 1
            out += " "
      return out


if __name__ == "__main__":
     import doctest

     doctest.testmod()
