





def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Optional[int]					=     [0 for i in range(len(A_))]

    # initialize interval's left pointer and right pointer
    UpperCamelCase__				, UpperCamelCase__:	Optional[Any]					=     0, 0

    for i in range(1    ,len(A_)):
        # case when current index is inside the interval
        if i <= right_pointer:
            UpperCamelCase__:	str					=     min(right_pointer - i + 1    ,z_result[i - left_pointer])
            UpperCamelCase__:	int					=     min_edge

        while go_next(A_    ,A_    ,A_):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            UpperCamelCase__				, UpperCamelCase__:	int					=     i, i + z_result[i] - 1

    return z_result



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    return i + z_result[i] < len(A_) and s[z_result[i]] == s[i + z_result[i]]



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    UpperCamelCase__:	Dict					=     z_function(pattern + input_str)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(A_):
            answer += 1

    return answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()








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):

      """simple docstring"""


      def __init__(		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[int]=7	,    __lowerCamelCase:			Any=3	,    __lowerCamelCase:			List[str]=18	,    __lowerCamelCase:			List[Any]=30	,    __lowerCamelCase:			Tuple=400	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			int=True	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			Dict=True	,    __lowerCamelCase:			List[Any]=[0.5, 0.5, 0.5]	,    __lowerCamelCase:			Optional[Any]=[0.5, 0.5, 0.5]	,    __lowerCamelCase:			int=False	,    ):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''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 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Dict					=     load_dataset("hf-internal-testing/fixtures_ade20k"    ,split="test")

    UpperCamelCase__:	Optional[Any]					=     Image.open(dataset[0]["file"])
    UpperCamelCase__:	str					=     Image.open(dataset[1]["file"])

    return image, map



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Dict					=     load_dataset("hf-internal-testing/fixtures_ade20k"    ,split="test")

    UpperCamelCase__:	int					=     Image.open(ds[0]["file"])
    UpperCamelCase__:	int					=     Image.open(ds[1]["file"])
    UpperCamelCase__:	List[str]					=     Image.open(ds[2]["file"])
    UpperCamelCase__:	List[Any]					=     Image.open(ds[3]["file"])

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




@require_torch
@require_vision
class        _a    (    UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__  		=       BeitImageProcessor if is_vision_available() else None






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     BeitImageProcessingTester(self					)






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return self.image_processor_tester.prepare_image_processor_dict()






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_resize"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "size"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_center_crop"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "center_crop"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_normalize"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "image_mean"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "image_std"					)					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     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	,    __lowerCamelCase					)

          UpperCamelCase__:	int					=     self.image_processing_class.from_dict(
              self.image_processor_dict	,    size=42	,    crop_size=84	,    reduce_labels=__lowerCamelCase					)
          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	,    __lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          pass






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          UpperCamelCase__:	Optional[Any]					=     prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase					)
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase	,    Image.Image					)

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






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          UpperCamelCase__:	List[str]					=     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
          UpperCamelCase__:	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
          UpperCamelCase__:	Dict					=     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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          UpperCamelCase__:	int					=     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
          UpperCamelCase__:	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
          UpperCamelCase__:	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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


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

          # Test not batched input
          UpperCamelCase__:	Dict					=     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__:	Any					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    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__:	str					=     prepare_semantic_single_inputs()

          UpperCamelCase__:	Any					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    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__:	List[str]					=     prepare_semantic_batch_inputs()

          UpperCamelCase__:	Optional[int]					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    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 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     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__:	Any					=     prepare_semantic_single_inputs()
          UpperCamelCase__:	int					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 150					)

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

