



'''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 	__magic_name__						:







														@staticmethod
														def 							__lowercase			(							*_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       str		):
																pass



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      str:
		_a     :      List[Any]        =							hashlib.mda(image.tobytes()  )
		return m.hexdigest()[:10]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		_a     :      Dict        =							np.array(lowerCAmelCase_  )
		_a     :      List[Any]        =							npimg.shape
		return {"hash": hashimage(lowerCAmelCase_  ), "shape": shape}

@is_pipeline_test
@require_vision
@require_torch
class 	__magic_name__						(    unittest.TestCase   ):
							lowerCAmelCase       :    List[str]   				=					dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
							lowerCAmelCase       :    str   				=					dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Any		):
									_a     :      Union[str, Any]        =							MaskGenerationPipeline(model=_UpperCAmelCase	,image_processor=_UpperCAmelCase		)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any		):
									pass







							@require_tf
							@unittest.skip('Image segmentation not implemented in TF'		)
							def 							__lowercase			(							self					:       Any		):
									pass







							@slow
							@require_torch
							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      int        =							pipeline('mask-generation'	,model='facebook/sam-vit-huge'		)

									_a     :      Union[str, Any]        =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'	,points_per_batch=256		)

									# Shortening by hashing
									_a     :      Optional[Any]        =							[]
									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': 1.04_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 							__lowercase			(							self					:       List[Any]		):
									_a     :      int        =							'facebook/sam-vit-huge'
									_a     :      int        =							pipeline('mask-generation'	,model=_UpperCAmelCase		)

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

									# Shortening by hashing
									_a     :      Union[str, Any]        =							[]
									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': 1.04_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},
									    ]	,)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase					:	Union[str, Any]       =  {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  ['''ChineseCLIPFeatureExtractor''']
		__lowerCamelCase					:	Optional[int]       =  ['''ChineseCLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	int       =  [
		    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ChineseCLIPModel''',
		    '''ChineseCLIPPreTrainedModel''',
		    '''ChineseCLIPTextModel''',
		    '''ChineseCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ChineseCLIPConfig,
		    ChineseCLIPOnnxConfig,
		    ChineseCLIPTextConfig,
		    ChineseCLIPVisionConfig,
		)
		from .processing_chinese_clip import ChineseCLIPProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ChineseCLIPModel,
				    ChineseCLIPPreTrainedModel,
				    ChineseCLIPTextModel,
				    ChineseCLIPVisionModel,
				)

else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
