




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:



							"""simple docstring"""






							@staticmethod
							def        __a      (	*lowerCamelCase			, **lowerCamelCase							)   ->     List[str]:





														"""simple docstring"""


														pass
def    snake_case_	(						SCREAMING_SNAKE_CASE_							)     ->					str:
							lowercase__    :			Optional[Any]     =				hashlib.mda(image.tobytes()							)
							return m.hexdigest()[:10]
def    snake_case_	(						SCREAMING_SNAKE_CASE_							)     ->					Dict:
							lowercase__    :			Dict     =				np.array(__lowerCamelCase							)
							lowercase__    :			List[str]     =				npimg.shape
							return {"hash": hashimage(__lowerCamelCase							), "shape": shape}







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



	"""simple docstring"""






	a      :   List[Any]			    =	dict(
	    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
	a      :   Optional[int]			    =	dict(
	    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase							)   ->     Dict:





								"""simple docstring"""


								lowercase__    :			Optional[Any]     =				MaskGenerationPipeline(model=snake_case_			, image_processor=snake_case_							)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase							)   ->     Optional[Any]:





								"""simple docstring"""


								pass

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





								"""simple docstring"""


								pass

	@slow
	@require_torch
	def        __a      (	self							)   ->     Any:





								"""simple docstring"""


								lowercase__    :			Dict     =				pipeline("mask-generation"			, model="facebook/sam-vit-huge"							)

								lowercase__    :			str     =				image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"			, points_per_batch=256							)

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

								# fmt: off
								self.assertEqual(
								    nested_simplify(snake_case_			, decimals=4							)			, [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
								    ]			, )
								# fmt: on







	@require_torch
	@slow
	def        __a      (	self							)   ->     Tuple:





								"""simple docstring"""


								lowercase__    :			Tuple     =				"facebook/sam-vit-huge"
								lowercase__    :			Optional[int]     =				pipeline("mask-generation"			, model=snake_case_							)

								lowercase__    :			str     =				image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"			, pred_iou_thresh=1			, points_per_batch=256							)

								# Shortening by hashing
								lowercase__    :			Optional[Any]     =				[]
								for i, o in enumerate(outputs["masks"]							):
															new_outupt += [{"mask": mask_to_test_readable(snake_case_							), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(snake_case_			, decimals=4							)			, [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
								    ]			, )




import unittest

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

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:






				class     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






@is_pipeline_test
@require_vision
class     __a			(  unittest.TestCase					):



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
						# python and torch versions.
						self.assertIn(
						    nested_simplify(snake_case_)					,						[
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@require_tf
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						framework="""tf""")
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@slow
	@require_torch
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						)
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)



	@slow
	@require_tf
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						framework="""tf""")
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)




