



"""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      UpperCAmelCase__		:



							@staticmethod
							def 					A_		( *snake_case  :    Any      ,							**snake_case  :    Any				)			->						str:






														'''simple docstring'''

														pass

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					hashlib.mda(image.tobytes()			)
							return m.hexdigest()[:10]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Dict:
							A         =					np.array(_A			)
							A         =					npimg.shape
							return {"hash": hashimage(_A			), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class      UpperCAmelCase__		(      unittest.TestCase     ):
		lowerCAmelCase_       :	List[Any] 							=     dict(
		    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
		lowerCAmelCase_       :	List[Any] 							=     dict(
		    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )



		def 					A_		( self  :    List[str]      ,							snake_case  :    Any      ,							snake_case  :    Dict      ,							snake_case  :    Dict				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					MaskGenerationPipeline(model=__UpperCamelCase      ,							image_processor=__UpperCamelCase				)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]



		def 					A_		( self  :    Tuple      ,							snake_case  :    List[Any]      ,							snake_case  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									pass



		@require_tf
		@unittest.skip('Image segmentation not implemented in TF'				)
		def 					A_		( self  :    Dict				)			->						List[str]:






									'''simple docstring'''

									pass



		@slow
		@require_torch
		def 					A_		( self  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline('mask-generation'      ,							model='facebook/sam-vit-huge'				)

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

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

									# fmt: off
									self.assertEqual(
									    nested_simplify(__UpperCamelCase      ,							decimals=4				)      ,							[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
									    ]      ,							)
									# fmt: on



		@require_torch
		@slow
		def 					A_		( self  :    Optional[int]				)			->						Any:






									'''simple docstring'''

									A         =					'facebook/sam-vit-huge'
									A         =					pipeline('mask-generation'      ,							model=__UpperCamelCase				)

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

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

									self.assertEqual(
									    nested_simplify(__UpperCamelCase      ,							decimals=4				)      ,							[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
									    ]      ,							)








"""simple docstring"""







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A    =			TypeVar('T')






class      UpperCAmelCase__		(      Generic[T]     ):
		lowerCAmelCase_       :	deque[T]  # Cache store of keys
		lowerCAmelCase_       :	set[T]  # References of the keys in cache
		lowerCAmelCase_       :	int 							=     10  # Maximum capacity of cache



		def __init__( self  :    int      ,							snake_case  :    int				)			->						None:






									'''simple docstring'''

									A         =					deque()
									A         =					set()
									if not n:
																A         =					sys.maxsize
									elif n < 0:
																raise ValueError('n should be an integer greater than 0.'				)
									else:
																A         =					n



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    T				)			->						None:






									'''simple docstring'''

									if x not in self.key_reference:
																if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
																							A         =					self.dq_store.pop()
																							self.key_reference.remove(snake_case				)
									else:
																self.dq_store.remove(snake_case				)

									self.dq_store.appendleft(snake_case				)
									self.key_reference.add(snake_case				)



		def 					A_		( self  :    Dict				)			->						None:






									'''simple docstring'''

									for k in self.dq_store:
																print(snake_case				)



		def __repr__( self  :    int				)			->						str:






									'''simple docstring'''

									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"""


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



