



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 	lowerCamelCase__      :




									'''simple docstring'''



									@staticmethod
									def 		lowerCAmelCase__				(*__lowerCamelCase		,**__lowerCamelCase			)					->      Union[str, Any]:

													"""simple docstring"""






													pass






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Union[str, Any]												=					hashlib.mda(image.tobytes())
				return m.hexdigest()[:10]






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					np.array(lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					npimg.shape
				return {"hash": hashimage(lowerCamelCase_), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 	lowerCamelCase__      (							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		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					MaskGenerationPipeline(model=__SCREAMING_SNAKE_CASE		,image_processor=__SCREAMING_SNAKE_CASE			)
						return image_segmenter, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						pass



		@require_tf
		@unittest.skip('''Image segmentation not implemented in TF'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					pipeline('''mask-generation'''		,model='''facebook/sam-vit-huge'''			)

						lowerCAmelCase__					:    Optional[Any]												=					image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,points_per_batch=2_56			)

						# Shortening by hashing
						lowerCAmelCase__					:    Union[str, Any]												=					[]
						for i, o in enumerate(outputs['''masks''']			):
										new_outupt += [{"mask": mask_to_test_readable(__SCREAMING_SNAKE_CASE			), "scores": outputs["scores"][i]}]

						# fmt: off
						self.assertEqual(
						    nested_simplify(__SCREAMING_SNAKE_CASE		,decimals=4			)		,[
						        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
						        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.021},
						        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
						        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
						        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
						        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9967},
						        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.993},
						        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9909},
						        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9879},
						        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9834},
						        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9716},
						        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9612},
						        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9599},
						        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9552},
						        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9532},
						        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9516},
						        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9499},
						        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9483},
						        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9464},
						        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
						        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
						        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9408},
						        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9335},
						        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9326},
						        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9262},
						        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8999},
						        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8986},
						        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8984},
						        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8873},
						        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8871}
						    ]		,)
						# fmt: on



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

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					'''facebook/sam-vit-huge'''
						lowerCAmelCase__					:    int												=					pipeline('''mask-generation'''		,model=__SCREAMING_SNAKE_CASE			)

						lowerCAmelCase__					:    List[Any]												=					image_segmenter(
						    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,pred_iou_thresh=1		,points_per_batch=2_56			)

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

						self.assertEqual(
						    nested_simplify(__SCREAMING_SNAKE_CASE		,decimals=4			)		,[
						        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
						        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0210},
						        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
						        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
						        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
						    ]		,)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    :		Any  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vit_msn"""



		def __init__(self		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=2_24		,__lowerCamelCase=16		,__lowerCamelCase=3		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    str												=					num_hidden_layers
						lowerCAmelCase__					:    List[str]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_act
						lowerCAmelCase__					:    Tuple												=					hidden_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    int												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    str												=					patch_size
						lowerCAmelCase__					:    Optional[int]												=					num_channels
						lowerCAmelCase__					:    int												=					qkv_bias

