



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__A							       =		logging.get_logger(__name__)






class 						lowercase		(					snake_case__):






	"""simple docstring"""


	a__ :   Optional[int]						  =				["pixel_values"]




	def __init__(      self      :		List[str]		,    __UpperCAmelCase      :		List[Any] = True		,    __UpperCAmelCase      :		str = None		,    __UpperCAmelCase      :		str = PILImageResampling.BICUBIC		,    __UpperCAmelCase      :		Optional[int] = True		,    __UpperCAmelCase      :		Any = 1 / 255		,    __UpperCAmelCase      :		Any = True		,    __UpperCAmelCase      :		Optional[Any] = None		,    __UpperCAmelCase      :		Any = None		,    __UpperCAmelCase      :		str = True		,    **__UpperCAmelCase      :		Union[str, Any]		,    )     ->					None:
							super().__init__(**__UpperCAmelCase	)
							UpperCAmelCase_=  size if size is not None else {'height': 384, 'width': 384}
							UpperCAmelCase_=  get_size_dict(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase	)

							UpperCAmelCase_=  do_resize
							UpperCAmelCase_=  size
							UpperCAmelCase_=  resample
							UpperCAmelCase_=  do_rescale
							UpperCAmelCase_=  rescale_factor
							UpperCAmelCase_=  do_normalize
							UpperCAmelCase_=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							UpperCAmelCase_=  image_std if image_std is not None else OPENAI_CLIP_STD
							UpperCAmelCase_=  do_convert_rgb




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		List[Any]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		Tuple = PILImageResampling.BICUBIC		,    __UpperCAmelCase      :		Any = None		,    **__UpperCAmelCase      :		Union[str, Any]		,    )     ->					np.ndarray:
							UpperCAmelCase_=  get_size_dict(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase	)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""	)
							UpperCAmelCase_=  (size['height'], size['width'])
							return resize(__UpperCAmelCase		,    size=__UpperCAmelCase		,    resample=__UpperCAmelCase		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		List[str]		,    __UpperCAmelCase      :		Tuple = None		,    **__UpperCAmelCase      :		Dict		,    )     ->					Any:
							return rescale(__UpperCAmelCase		,    scale=__UpperCAmelCase		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		List[Any] = None		,    **__UpperCAmelCase      :		Any		,    )     ->					np.ndarray:
							return normalize(__UpperCAmelCase		,    mean=__UpperCAmelCase		,    std=__UpperCAmelCase		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]		,    __UpperCAmelCase      :		Optional[int]		,    __UpperCAmelCase      :		Any = None		,    __UpperCAmelCase      :		Dict = None		,    __UpperCAmelCase      :		Dict = None		,    __UpperCAmelCase      :		Dict = None		,    __UpperCAmelCase      :		str = None		,    __UpperCAmelCase      :		Any = None		,    __UpperCAmelCase      :		int = None		,    __UpperCAmelCase      :		Any = None		,    __UpperCAmelCase      :		str = None		,    __UpperCAmelCase      :		Tuple = None		,    __UpperCAmelCase      :		Union[str, Any] = ChannelDimension.FIRST		,    **__UpperCAmelCase      :		Optional[int]		,    )     ->					PIL.Image.Image:
							UpperCAmelCase_=  do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_=  resample if resample is not None else self.resample
							UpperCAmelCase_=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_=  image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_=  image_std if image_std is not None else self.image_std
							UpperCAmelCase_=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCAmelCase_=  size if size is not None else self.size
							UpperCAmelCase_=  get_size_dict(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase	)

							UpperCAmelCase_=  make_list_of_images(__UpperCAmelCase	)

							if not valid_images(__UpperCAmelCase	):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray."""	)

							if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True."""	)

							if do_rescale and rescale_factor is None:
													raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													UpperCAmelCase_=  [convert_to_rgb(__UpperCAmelCase	) for image in images]

							# All transformations expect numpy arrays.
							UpperCAmelCase_=  [to_numpy_array(__UpperCAmelCase	) for image in images]

							if do_resize:
													UpperCAmelCase_=  [self.resize(image=__UpperCAmelCase		,    size=__UpperCAmelCase		,    resample=__UpperCAmelCase	) for image in images]

							if do_rescale:
													UpperCAmelCase_=  [self.rescale(image=__UpperCAmelCase		,    scale=__UpperCAmelCase	) for image in images]

							if do_normalize:
													UpperCAmelCase_=  [self.normalize(image=__UpperCAmelCase		,    mean=__UpperCAmelCase		,    std=__UpperCAmelCase	) for image in images]

							UpperCAmelCase_=  [to_channel_dimension_format(__UpperCAmelCase		,    __UpperCAmelCase	) for image in images]

							UpperCAmelCase_=  BatchFeature(data={"""pixel_values""": images}		,    tensor_type=__UpperCAmelCase	)

							return encoded_outputs








'''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      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						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      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =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      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          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_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






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




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          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_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

