



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   UpperCAmelCase_    (       unittest.TestCase):







							def __init__(   self,			__a,			__a=7,			__a=3,			__a=18,			__a=30,			__a=400,			__a=True,			__a=None,			__a=True,			__a=None,			__a=True,			__a=[0.5, 0.5, 0.5],			__a=[0.5, 0.5, 0.5],			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    size if size is not None else {"height": 20, "width": 20}
														_lowerCAmelCase     :							int    =    crop_size if crop_size is not None else {"height": 18, "width": 18}
														_lowerCAmelCase     :							Optional[Any]    =    parent
														_lowerCAmelCase     :							int    =    batch_size
														_lowerCAmelCase     :							List[str]    =    num_channels
														_lowerCAmelCase     :							Optional[int]    =    image_size
														_lowerCAmelCase     :							int    =    min_resolution
														_lowerCAmelCase     :							int    =    max_resolution
														_lowerCAmelCase     :							str    =    do_resize
														_lowerCAmelCase     :							str    =    size
														_lowerCAmelCase     :							Tuple    =    do_center_crop
														_lowerCAmelCase     :							Union[str, Any]    =    crop_size
														_lowerCAmelCase     :							Union[str, Any]    =    do_normalize
														_lowerCAmelCase     :							Union[str, Any]    =    image_mean
														_lowerCAmelCase     :							List[Any]    =    image_std
														_lowerCAmelCase     :							Union[str, Any]    =    do_reduce_labels







							def    snake_case__      (   self):

														'''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 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    load_dataset("hf-internal-testing/fixtures_ade20k"				, split="test"  )

							_lowerCAmelCase     :							Optional[int]    =    Image.open(dataset[0]["file"]  )
							_lowerCAmelCase     :							Dict    =    Image.open(dataset[1]["file"]  )

							return image, map






def 				A				(					):





							'''simple docstring'''




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

							_lowerCAmelCase     :							List[str]    =    Image.open(ds[0]["file"]  )
							_lowerCAmelCase     :							str    =    Image.open(ds[1]["file"]  )
							_lowerCAmelCase     :							Tuple    =    Image.open(ds[2]["file"]  )
							_lowerCAmelCase     :							Tuple    =    Image.open(ds[3]["file"]  )

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



@require_torch
@require_vision
class   UpperCAmelCase_    (       _UpperCAmelCase   ,					unittest.TestCase):
							lowerCamelCase__   			=		BeitImageProcessor if is_vision_available() else None







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    BeitImageProcessingTester(self)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.image_processor_tester.prepare_image_processor_dict()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.image_processing_class(**self.image_processor_dict)
														self.assertTrue(hasattr(a__,			"do_resize"))
														self.assertTrue(hasattr(a__,			"size"))
														self.assertTrue(hasattr(a__,			"do_center_crop"))
														self.assertTrue(hasattr(a__,			"center_crop"))
														self.assertTrue(hasattr(a__,			"do_normalize"))
														self.assertTrue(hasattr(a__,			"image_mean"))
														self.assertTrue(hasattr(a__,			"image_std"))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    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,			a__)

														_lowerCAmelCase     :							Union[str, Any]    =    self.image_processing_class.from_dict(
														    self.image_processor_dict,			size=42,			crop_size=84,			reduce_labels=a__)
														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,			a__)







							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.image_processing_class(**self.image_processor_dict)
														# create random PIL images
														_lowerCAmelCase     :							Union[str, Any]    =    prepare_image_inputs(self.image_processor_tester,			equal_resolution=a__)
														for image in image_inputs:
																					self.assertIsInstance(a__,			Image.Image)

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

														# Test batched
														_lowerCAmelCase     :							str    =    image_processing(a__,			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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.image_processing_class(**self.image_processor_dict)
														# create random numpy tensors
														_lowerCAmelCase     :							List[str]    =    prepare_image_inputs(self.image_processor_tester,			equal_resolution=a__,			numpify=a__)
														for image in image_inputs:
																					self.assertIsInstance(a__,			np.ndarray)

														# Test not batched input
														_lowerCAmelCase     :							str    =    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
														_lowerCAmelCase     :							int    =    image_processing(a__,			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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.image_processing_class(**self.image_processor_dict)
														# create random PyTorch tensors
														_lowerCAmelCase     :							str    =    prepare_image_inputs(self.image_processor_tester,			equal_resolution=a__,			torchify=a__)
														for image in image_inputs:
																					self.assertIsInstance(a__,			torch.Tensor)

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

														# Test batched
														_lowerCAmelCase     :							Optional[int]    =    image_processing(a__,			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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.image_processing_class(**self.image_processor_dict)
														# create random PyTorch tensors
														_lowerCAmelCase     :							Union[str, Any]    =    prepare_image_inputs(self.image_processor_tester,			equal_resolution=a__,			torchify=a__)
														_lowerCAmelCase     :							List[Any]    =    []
														for image in image_inputs:
																					self.assertIsInstance(a__,			torch.Tensor)
																					maps.append(torch.zeros(image.shape[-2:]).long())

														# Test not batched input
														_lowerCAmelCase     :							str    =    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
														_lowerCAmelCase     :							Optional[Any]    =    image_processing(a__,			a__,			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)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    prepare_semantic_single_inputs()

														_lowerCAmelCase     :							List[Any]    =    image_processing(a__,			a__,			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)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    prepare_semantic_batch_inputs()

														_lowerCAmelCase     :							List[Any]    =    image_processing(a__,			a__,			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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    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
														_lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    prepare_semantic_single_inputs()
														_lowerCAmelCase     :							List[str]    =    image_processing(a__,			a__,			return_tensors="pt")
														self.assertTrue(encoding["labels"].min().item() >= 0)
														self.assertTrue(encoding["labels"].max().item() <= 150)

														_lowerCAmelCase     :							str    =    True
														_lowerCAmelCase     :							str    =    image_processing(a__,			a__,			return_tensors="pt")
														self.assertTrue(encoding["labels"].min().item() >= 0)
														self.assertTrue(encoding["labels"].max().item() <= 255)







from math import loga






def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if a < 0:
      raise ValueError('''Input value must be a positive integer'''       )
   elif isinstance(snake_case_       ,      snake_case_       ):
      raise TypeError('''Input value must be a \'int\' type'''       )
   return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

