
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class       _snake_case					(     snake_case				):
			# to overwrite at feature extractactor specific tests
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.feat_extract_tester.prepare_feat_extract_dict()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				self.assertTrue(hasattr(_a						,   "feature_size"		)		)
				self.assertTrue(hasattr(_a						,   "sampling_rate"		)		)
				self.assertTrue(hasattr(_a						,   "padding_value"		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     self.feat_extract_tester.prepare_inputs_for_common()
				__magic_name__		:		Dict	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		List[Any]	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Dict	   =     BatchFeature({input_name: speech_inputs}		)

				self.assertTrue(all(len(_a		) == len(_a		) for x, y in zip(_a						,   processed_features[input_name]		)		)		)

				__magic_name__		:		int	   =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a		)
				__magic_name__		:		str	   =     BatchFeature({input_name: speech_inputs}						,   tensor_type="np"		)

				__magic_name__		:		int	   =     processed_features[input_name]

				if len(batch_features_input.shape		) < 3:
					__magic_name__		:		List[str]	   =     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)







			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a		)
				__magic_name__		:		str	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		Tuple	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Optional[int]	   =     BatchFeature({input_name: speech_inputs}						,   tensor_type="pt"		)

				__magic_name__		:		str	   =     processed_features[input_name]

				if len(batch_features_input.shape		) < 3:
					__magic_name__		:		Optional[int]	   =     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)







			@require_tf
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a		)
				__magic_name__		:		Dict	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		Dict	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Optional[int]	   =     BatchFeature({input_name: speech_inputs}						,   tensor_type="tf"		)

				__magic_name__		:		Union[str, Any]	   =     processed_features[input_name]

				if len(batch_features_input.shape		) < 3:
					__magic_name__		:		Union[str, Any]	   =     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=False		):
				def _inputs_have_equal_length(_a		):
					__magic_name__		:		Tuple	   =     len(input[0]		)
					for input_slice in input[1:]:
						if len(_a		) != length:
							return False
					return True

				def _inputs_are_equal(_a						,   _a		):
					if len(_a		) != len(_a		):
						return False

					for input_slice_a, input_slice_a in zip(_a						,   _a		):
						if not np.allclose(np.asarray(_a		)						,   np.asarray(_a		)						,   atol=1e-3		):
							return False
					return True

				__magic_name__		:		Dict	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		List[Any]	   =     self.feat_extract_tester.prepare_inputs_for_common(numpify=_a		)
				__magic_name__		:		List[Any]	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Union[str, Any]	   =     BatchFeature({input_name: speech_inputs}		)

				__magic_name__		:		Tuple	   =     self.feat_extract_tester.seq_length_diff
				__magic_name__		:		List[Any]	   =     self.feat_extract_tester.max_seq_length + pad_diff
				__magic_name__		:		Any	   =     self.feat_extract_tester.min_seq_length
				__magic_name__		:		Dict	   =     self.feat_extract_tester.batch_size
				__magic_name__		:		Optional[Any]	   =     self.feat_extract_tester.feature_size

				# test padding for List[int] + numpy
				__magic_name__		:		int	   =     feat_extract.pad(_a						,   padding=_a		)
				__magic_name__		:		int	   =     input_a[input_name]

				__magic_name__		:		int	   =     feat_extract.pad(_a						,   padding="longest"		)
				__magic_name__		:		int	   =     input_a[input_name]

				__magic_name__		:		List[Any]	   =     feat_extract.pad(_a						,   padding="max_length"						,   max_length=len(speech_inputs[-1]		)		)
				__magic_name__		:		List[Any]	   =     input_a[input_name]

				__magic_name__		:		str	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="np"		)
				__magic_name__		:		Any	   =     input_a[input_name]

				# max_length parameter has to be provided when setting `padding="max_length"`
				with self.assertRaises(_a		):
					feat_extract.pad(_a						,   padding="max_length"		)[input_name]

				__magic_name__		:		List[Any]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=_a						,   return_tensors="np"		)
				__magic_name__		:		Optional[int]	   =     input_a[input_name]

				self.assertFalse(_inputs_have_equal_length(_a		)		)
				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertTrue(_inputs_are_equal(_a						,   _a		)		)
				self.assertTrue(len(input_a[0]		) == pad_min_length		)
				self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
				self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
				self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

				if feature_size > 1:
					self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

				# test padding for `pad_to_multiple_of` for List[int] + numpy
				__magic_name__		:		str	   =     feat_extract.pad(_a						,   pad_to_multiple_of=10		)
				__magic_name__		:		int	   =     input_a[input_name]

				__magic_name__		:		Union[str, Any]	   =     feat_extract.pad(_a						,   padding="longest"						,   pad_to_multiple_of=10		)
				__magic_name__		:		Dict	   =     input_a[input_name]

				__magic_name__		:		List[Any]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   pad_to_multiple_of=10						,   max_length=_a		)
				__magic_name__		:		int	   =     input_a[input_name]

				__magic_name__		:		Optional[Any]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   pad_to_multiple_of=10						,   max_length=_a						,   return_tensors="np"						,   )
				__magic_name__		:		str	   =     input_a[input_name]

				self.assertTrue(all(len(_a		) % 10 == 0 for x in input_a		)		)
				self.assertTrue(_inputs_are_equal(_a						,   _a		)		)

				__magic_name__		:		List[str]	   =     pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
				self.assertTrue(all(len(_a		) == expected_mult_pad_length for x in input_a		)		)
				self.assertEqual(input_a.shape[:2]						,   (batch_size, expected_mult_pad_length)		)

				if feature_size > 1:
					self.assertTrue(input_a.shape[2] == feature_size		)

				# Check padding value is correct
				__magic_name__		:		Optional[int]	   =     (np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
				self.assertTrue(
				    abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
				    < 1e-3		)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
				    < 1e-3		)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
				    < 1e-3		)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1e-3		)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
				    < 1e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=False		):
				def _inputs_have_equal_length(_a		):
					__magic_name__		:		Optional[Any]	   =     len(input[0]		)
					for input_slice in input[1:]:
						if len(_a		) != length:
							return False
					return True

				def _inputs_are_equal(_a						,   _a		):
					if len(_a		) != len(_a		):
						return False

					for input_slice_a, input_slice_a in zip(_a						,   _a		):
						if not np.allclose(np.asarray(_a		)						,   np.asarray(_a		)						,   atol=1e-3		):
							return False
					return True

				__magic_name__		:		List[str]	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		Any	   =     self.feat_extract_tester.prepare_inputs_for_common(numpify=_a		)
				__magic_name__		:		Any	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Union[str, Any]	   =     BatchFeature({input_name: speech_inputs}		)

				# truncate to smallest
				__magic_name__		:		Tuple	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)						,   truncation=_a		)
				__magic_name__		:		Union[str, Any]	   =     input_a[input_name]

				__magic_name__		:		List[Any]	   =     feat_extract.pad(_a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)		)
				__magic_name__		:		Union[str, Any]	   =     input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertFalse(_inputs_have_equal_length(_a		)		)

				# truncate to smallest with np
				__magic_name__		:		Optional[int]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)						,   return_tensors="np"						,   truncation=_a						,   )
				__magic_name__		:		int	   =     input_a[input_name]

				__magic_name__		:		Optional[int]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)						,   return_tensors="np"		)
				__magic_name__		:		Optional[Any]	   =     input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(_a		)		)

				# truncate to middle
				__magic_name__		:		Optional[int]	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[1]		)						,   truncation=_a						,   return_tensors="np"						,   )
				__magic_name__		:		List[str]	   =     input_a[input_name]

				__magic_name__		:		Tuple	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[1]		)						,   truncation=_a		)
				__magic_name__		:		List[Any]	   =     input_a[input_name]

				__magic_name__		:		int	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[1]		)						,   return_tensors="np"		)
				__magic_name__		:		Union[str, Any]	   =     input_a[input_name]

				self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertTrue(_inputs_are_equal(_a						,   _a		)		)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(_a		)		)
				self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_a		):
					feat_extract.pad(_a						,   truncation=_a		)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_a		):
					feat_extract.pad(_a						,   padding="longest"						,   truncation=_a		)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_a		):
					feat_extract.pad(_a						,   padding="longest"						,   truncation=_a		)[input_name]

				# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
				with self.assertRaises(_a		):
					feat_extract.pad(_a						,   padding="max_length"						,   truncation=_a		)[input_name]

				# test truncation for `pad_to_multiple_of` for List[int] + numpy
				__magic_name__		:		List[str]	   =     12
				__magic_name__		:		Tuple	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)						,   pad_to_multiple_of=_a						,   truncation=_a						,   )
				__magic_name__		:		Optional[int]	   =     input_a[input_name]

				__magic_name__		:		Tuple	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=len(speech_inputs[0]		)						,   pad_to_multiple_of=_a						,   )
				__magic_name__		:		str	   =     input_a[input_name]

				# retrieve expected_length as multiple of pad_to_multiple_of
				__magic_name__		:		List[Any]	   =     len(speech_inputs[0]		)
				if expected_length % pad_to_multiple_of != 0:
					__magic_name__		:		Tuple	   =     ((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

				self.assertTrue(len(input_a[0]		) == expected_length		)
				self.assertTrue(_inputs_have_equal_length(_a		)		)
				self.assertFalse(_inputs_have_equal_length(_a		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._check_padding(numpify=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._check_padding(numpify=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._check_truncation(numpify=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._check_truncation(numpify=_a		)







			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		str	   =     self.feat_extract_tester.prepare_inputs_for_common()
				__magic_name__		:		Optional[Any]	   =     feat_extract.model_input_names[0]

				__magic_name__		:		List[Any]	   =     BatchFeature({input_name: speech_inputs}		)

				__magic_name__		:		List[str]	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="np"		)[input_name]
				__magic_name__		:		Dict	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="pt"		)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1e-2		)







			@require_tf
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.feature_extraction_class(**self.feat_extract_dict		)
				__magic_name__		:		Optional[int]	   =     self.feat_extract_tester.prepare_inputs_for_common()
				__magic_name__		:		Any	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Union[str, Any]	   =     BatchFeature({input_name: speech_inputs}		)

				__magic_name__		:		Tuple	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="np"		)[input_name]
				__magic_name__		:		List[str]	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="tf"		)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1e-2		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.feat_extract_dict
				__magic_name__		:		int	   =     True
				__magic_name__		:		Union[str, Any]	   =     self.feature_extraction_class(**_a		)
				__magic_name__		:		List[Any]	   =     self.feat_extract_tester.prepare_inputs_for_common()
				__magic_name__		:		List[str]	   =     [len(_a		) for x in speech_inputs]
				__magic_name__		:		Dict	   =     feat_extract.model_input_names[0]

				__magic_name__		:		Union[str, Any]	   =     BatchFeature({input_name: speech_inputs}		)

				__magic_name__		:		Optional[Any]	   =     feat_extract.pad(_a						,   padding="longest"						,   return_tensors="np"		)
				self.assertIn("attention_mask"						,   _a		)
				self.assertListEqual(list(processed.attention_mask.shape		)						,   list(processed[input_name].shape[:2]		)		)
				self.assertListEqual(processed.attention_mask.sum(-1		).tolist()						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.feat_extract_dict
				__magic_name__		:		Dict	   =     True
				__magic_name__		:		Any	   =     self.feature_extraction_class(**_a		)
				__magic_name__		:		str	   =     self.feat_extract_tester.prepare_inputs_for_common()
				__magic_name__		:		Union[str, Any]	   =     [len(_a		) for x in speech_inputs]
				__magic_name__		:		Dict	   =     feat_extract.model_input_names[0]

				__magic_name__		:		List[Any]	   =     BatchFeature({input_name: speech_inputs}		)
				__magic_name__		:		Dict	   =     min(_a		)

				__magic_name__		:		Dict	   =     feat_extract.pad(
				    _a						,   padding="max_length"						,   max_length=_a						,   truncation=_a						,   return_tensors="np"		)
				self.assertIn("attention_mask"						,   _a		)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape		)						,   [processed_pad[input_name].shape[0], max_length]		)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()						,   [max_length for x in speech_inputs]		)


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 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


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





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

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

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

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

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

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

				self.assertEqual(
				    nested_simplify(_a						,   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},
				    ]						,   )
