






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__ :
			@staticmethod
			def       __magic_name__    (    *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Tuple:
									pass

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Dict       						= hashlib.mda(image.tobytes()			)
						return m.hexdigest()[:10]

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Any       						= np.array(_A			)
						__magic_name__      :       Optional[int]       						= npimg.shape
						return {"hash": hashimage(_A			), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 	snake_case__ (							unittest.TestCase				):
	lowercase__						:					Any											=   dict(
	    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
	lowercase__						:					Dict											=   dict(
	    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Any       						= MaskGenerationPipeline(model=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)
							return image_segmenter, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							pass
	@require_tf
	@unittest.skip("""Image segmentation not implemented in TF"""			)
	def       __magic_name__    (    self			)							->				str:
							pass
	@slow
	@require_torch
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       List[str]       						= pipeline("""mask-generation"""      ,       model="""facebook/sam-vit-huge"""			)

							__magic_name__      :       str       						= image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""      ,       points_per_batch=2_56			)

							# Shortening by hashing
							__magic_name__      :       str       						= []
							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""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
							        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1},
							        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
							        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
							        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
							        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_6_7},
							        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_3},
							        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_0_9},
							        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_7_9},
							        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_3_4},
							        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9_7_1_6},
							        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9_6_1_2},
							        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_9_9},
							        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_5_2},
							        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_3_2},
							        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_1_6},
							        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_9_9},
							        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_8_3},
							        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_6_4},
							        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
							        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
							        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_0_8},
							        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_3_5},
							        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_2_6},
							        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9_2_6_2},
							        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_9_9},
							        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_6},
							        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_4},
							        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_3},
							        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_1}
							    ]      ,       )
							# fmt: on







	@require_torch
	@slow
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Union[str, Any]       						= """facebook/sam-vit-huge"""
							__magic_name__      :       Optional[Any]       						= pipeline("""mask-generation"""      ,       model=lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= image_segmenter(
							    """http://images.cocodataset.org/val2017/000000039769.jpg"""      ,       pred_iou_thresh=1      ,       points_per_batch=2_56			)

							# Shortening by hashing
							__magic_name__      :       List[Any]       						= []
							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""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
							        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1_0},
							        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
							        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
							        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
							    ]      ,       )








from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return math.sqrt(sum(pow(a - b,   2			) for a, b in zip(_A,   _A			)			)			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if dataset.ndim != value_array.ndim:
												__magic_name__      :       str       						= (
												    """Wrong input data's dimensions... """
												    f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
												)
												raise ValueError(_A			)

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		__magic_name__      :       Optional[Any]       						= (
																		    """Wrong input data's shape... """
																		    f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
																		)
																		raise ValueError(_A			)
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("""Wrong shape"""			)

						if dataset.dtype != value_array.dtype:
												__magic_name__      :       List[Any]       						= (
												    """Input data have different datatype... """
												    f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
												)
												raise TypeError(_A			)

						__magic_name__      :       Dict       						= []

						for value in value_array:
												__magic_name__      :       Tuple       						= euclidean(_A,   dataset[0]			)
												__magic_name__      :       Any       						= dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		__magic_name__      :       Any       						= euclidean(_A,   _A			)

																		if dist > temp_dist:
																								__magic_name__      :       Dict       						= temp_dist
																								__magic_name__      :       Any       						= dataset_value.tolist()

												answer.append([vector, dist]			)

						return answer

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return np.dot(_A,   _A			) / (norm(_A			) * norm(_A			))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
