


"""simple docstring"""







def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  a						:		Optional[Any]      							=			0

  # if input_string is "aba" than new_input_string become "a|b|a"
  a						:		int      							=			""
  a						:		Any      							=			""

  # append each character + "|" in new_string for range(0, length-1)
  for i in input_string[: len(A_       ) - 1]:
    new_input_string += i + "|"
  # append last character
  new_input_string += input_string[-1]

  # we will store the starting and ending of previous furthest ending palindromic
  # substring
  a							,		a						:		int      							=			0, 0

  # length[i] shows the length of palindromic substring with center i
  a						:		Optional[Any]      							=			[1 for i in range(len(A_       )       )]

  # for each character in new_string find corresponding palindromic string
  a						:		Optional[Any]      							=			0
  for j in range(len(A_       )       ):
    a						:		Any      							=			1 if j > r else min(length[l + r - j] // 2  ,   r - j + 1       )
    while (
        j - k >= 0
        and j + k < len(A_       )
        and new_input_string[k + j] == new_input_string[j - k]
    ):
      k += 1

    a						:		Optional[int]      							=			2 * k - 1

    # does this string is ending after the previously explored end (that is r) ?
    # if yes the update the new r to the last index of this
    if j + k - 1 > r:
      a						:		List[str]      							=			j - k + 1  # noqa: E741
      a						:		Union[str, Any]      							=			j + k - 1

    # update max_length and start position
    if max_length < length[j]:
      a						:		Optional[int]      							=			length[j]
      a						:		List[str]      							=			j

    # create that string
  a						:		Dict      							=			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
  for i in s:
    if i != "|":
      output_string += i

  return output_string


if __name__ == "__main__":
 import doctest

 doctest.testmod()







"""simple docstring"""






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:
						class 				snake_case       :




											"""simple docstring"""

											@staticmethod
											def 		__lowerCAmelCase						(				*lowerCamelCase__   :	List[Any]    ,**lowerCamelCase__   :	Union[str, Any]			):
															pass



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							hashlib.mda(image.tobytes()       )
				return m.hexdigest()[:1_0]



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							np.array(lowerCamelCase       )
				UpperCAmelCase__             =							npimg.shape
				return {"hash": hashimage(lowerCamelCase       ), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
					snake_case__				    = dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							MaskGenerationPipeline(model=lowerCamelCase__    ,image_processor=lowerCamelCase__			)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	str			):
									pass







					@require_tf
					@unittest.skip('Image segmentation not implemented in TF'			)
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					@slow
					@require_torch
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model='facebook/sam-vit-huge'			)

									UpperCAmelCase__             =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]    ,)
									# fmt: on







					@require_torch
					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							'facebook/sam-vit-huge'
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model=lowerCamelCase__			)

									UpperCAmelCase__             =							image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'    ,pred_iou_thresh=1    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]    ,)



