




'''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 		__A      :

													@staticmethod
													def     A__						(		*__snake_case   :str    , **__snake_case   :str ):
																	'''simple docstring'''

																	pass




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Any								=hashlib.mda(image.tobytes()   )
				return m.hexdigest()[:10]




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Any								=np.array(lowerCamelCase   )
				__magic_name__	:	str								=npimg.shape
				return {"hash": hashimage(lowerCamelCase   ), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class 		__A      (			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     A__						(		self   :Any    , __snake_case   :int    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=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   :List[str]    , __snake_case   :Union[str, Any]    , __snake_case   :int ):
										'''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   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=pipeline("""mask-generation"""    , model="""facebook/sam-vit-huge""" )

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

										# Shortening by hashing
										__magic_name__	:	Dict								=[]
										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""": (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     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								="""facebook/sam-vit-huge"""
										__magic_name__	:	List[str]								=pipeline("""mask-generation"""    , model=__snake_case )

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

										# Shortening by hashing
										__magic_name__	:	List[str]								=[]
										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""": (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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_		:				Union[str, Any]									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




