



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor







class 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   def __init__(    self							:	Optional[int] ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	Dict=7 ,						lowerCAmelCase							:	Union[str, Any]=3 ,						lowerCAmelCase							:	Union[str, Any]=18 ,						lowerCAmelCase							:	int=30 ,						lowerCAmelCase							:	str=400 ,						lowerCAmelCase							:	Tuple=True ,						lowerCAmelCase							:	Optional[int]=None ,						lowerCAmelCase							:	Dict=True ,						lowerCAmelCase							:	Optional[int]=None ,						lowerCAmelCase							:	Optional[Any]=True ,						lowerCAmelCase							:	Optional[Any]=[0.5, 0.5, 0.5] ,						lowerCAmelCase							:	Dict=[0.5, 0.5, 0.5] ,						) -> Any:

    """simple docstring"""







    _snake_case			:     List[str] 		=    size if size is not None else {'shortest_edge': 18}
    _snake_case			:     Any 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}
    _snake_case			:     Tuple 		=    parent
    _snake_case			:     int 		=    batch_size
    _snake_case			:     Dict 		=    num_channels
    _snake_case			:     Dict 		=    image_size
    _snake_case			:     Tuple 		=    min_resolution
    _snake_case			:     Any 		=    max_resolution
    _snake_case			:     List[str] 		=    do_resize
    _snake_case			:     Union[str, Any] 		=    size
    _snake_case			:     Optional[Any] 		=    do_center_crop
    _snake_case			:     Optional[int] 		=    crop_size
    _snake_case			:     Optional[int] 		=    do_normalize
    _snake_case			:     str 		=    image_mean
    _snake_case			:     Optional[int] 		=    image_std


   def    UpperCamelCase_     (    self							:	Any) -> List[str]:

    """simple docstring"""







    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }







@require_torch
@require_vision
class 						snake_case  (		a__    ,unittest.TestCase     ):



   '''simple docstring'''






   snake_case_    :       Optional[int]								=						LevitImageProcessor if is_vision_available() else None

   def    UpperCamelCase_     (    self							:	Union[str, Any]) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    LevitImageProcessingTester(self)

   @property
   def    UpperCamelCase_     (    self							:	Optional[int]) -> Optional[Any]:

    """simple docstring"""







    return self.image_processor_tester.prepare_image_processor_dict()

   def    UpperCamelCase_     (    self							:	Any) -> List[str]:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    self.image_processing_class(**self.image_processor_dict)
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""image_mean"""))
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""image_std"""))
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""do_normalize"""))
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""do_resize"""))
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""do_center_crop"""))
    self.assertTrue(hasattr(_UpperCAmelCase ,						"""size"""))

   def    UpperCamelCase_     (    self							:	Tuple) -> Tuple:

    """simple docstring"""







    _snake_case			:     int 		=    self.image_processing_class.from_dict(self.image_processor_dict)
    self.assertEqual(image_processor.size ,						{"""shortest_edge""": 18})
    self.assertEqual(image_processor.crop_size ,						{"""height""": 18, """width""": 18})

    _snake_case			:     Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict ,						size=42 ,						crop_size=84)
    self.assertEqual(image_processor.size ,						{"""shortest_edge""": 42})
    self.assertEqual(image_processor.crop_size ,						{"""height""": 84, """width""": 84})

   def    UpperCamelCase_     (    self							:	int) -> str:

    """simple docstring"""







    pass

   def    UpperCamelCase_     (    self							:	List[Any]) -> str:

    """simple docstring"""







    _snake_case			:     Any 		=    self.image_processing_class(**self.image_processor_dict)
    # create random PIL images
    _snake_case			:     Dict 		=    prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_UpperCAmelCase)
    for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase ,						Image.Image)

    # Test not batched input
    _snake_case			:     Optional[Any] 		=    image_processing(image_inputs[0] ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)

    # Test batched
    _snake_case			:     Optional[Any] 		=    image_processing(_UpperCAmelCase ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)

   def    UpperCamelCase_     (    self							:	Optional[int]) -> List[str]:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.image_processing_class(**self.image_processor_dict)
    # create random numpy tensors
    _snake_case			:     Union[str, Any] 		=    prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_UpperCAmelCase ,						numpify=_UpperCAmelCase)
    for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase ,						np.ndarray)

    # Test not batched input
    _snake_case			:     Tuple 		=    image_processing(image_inputs[0] ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)

    # Test batched
    _snake_case			:     Optional[Any] 		=    image_processing(_UpperCAmelCase ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)


   def    UpperCamelCase_     (    self							:	str) -> Any:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.image_processing_class(**self.image_processor_dict)
    # create random PyTorch tensors
    _snake_case			:     str 		=    prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_UpperCAmelCase ,						torchify=_UpperCAmelCase)
    for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase ,						torch.Tensor)

    # Test not batched input
    _snake_case			:     Dict 		=    image_processing(image_inputs[0] ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)

    # Test batched
    _snake_case			:     str 		=    image_processing(_UpperCAmelCase ,						return_tensors="""pt""").pixel_values
    self.assertEqual(
        encoded_images.shape ,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        ) ,						)







'''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  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     str:
     __A						: Dict      =       hashlib.mda(image.tobytes()			)
     return m.hexdigest()[:10]






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     Dict:
     __A						: Dict      =       np.array(__snake_case			)
     __A						: List[Any]      =       npimg.shape
     return {"hash": hashimage(__snake_case			), "shape": shape}







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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: 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 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          pass


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

          '''simple docstring'''






          pass


     @slow
     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: Tuple      =       image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'						,	points_per_batch=256)

          # Shortening by hashing
          __A						: int      =       []
          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': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
              ]						,	)
          # fmt: on


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

          # Shortening by hashing
          __A						: int      =       []
          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': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
              ]						,	)