
"""simple docstring"""






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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__UpperCAmelCase       =  logging.get_logger(__name__)




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Dict				 =						["pixel_values"]






			def __init__(					self       ,   __A = True       ,   __A = None       ,   __A = None       ,   __A = PILImageResampling.BILINEAR       ,   __A = True       ,   __A = 1 / 255       ,   __A = True       ,   __A = None       ,   __A = None       ,   **__A       ,   )			->     None:
						super().__init__(**__A       )
						lowerCAmelCase_    :Any			     =  size if size is not None else {"""shortest_edge""": 384}
						lowerCAmelCase_    :List[Any]			     =  get_size_dict(__A       ,   default_to_square=__A       )

						lowerCAmelCase_    :List[str]			     =  do_resize
						lowerCAmelCase_    :int			     =  size
						# Default value set here for backwards compatibility where the value in config is None
						lowerCAmelCase_    :Dict			     =  crop_pct if crop_pct is not None else 224 / 256
						lowerCAmelCase_    :Tuple			     =  resample
						lowerCAmelCase_    :List[Any]			     =  do_rescale
						lowerCAmelCase_    :Optional[Any]			     =  rescale_factor
						lowerCAmelCase_    :int			     =  do_normalize
						lowerCAmelCase_    :Optional[int]			     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase_    :Dict			     =  image_std if image_std is not None else IMAGENET_STANDARD_STD






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A = PILImageResampling.BICUBIC       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						lowerCAmelCase_    :List[Any]			     =  get_size_dict(__A       ,   default_to_square=__A       )
						if "shortest_edge" not in size:
									raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""       )
						lowerCAmelCase_    :Dict			     =  size["""shortest_edge"""]

						if shortest_edge < 384:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									lowerCAmelCase_    :List[Any]			     =  int(shortest_edge / crop_pct       )
									lowerCAmelCase_    :int			     =  get_resize_output_image_size(__A       ,   size=__A       ,   default_to_square=__A       )
									lowerCAmelCase_    :Tuple			     =  resize(image=__A       ,   size=__A       ,   resample=__A       ,   data_format=__A       ,   **__A       )
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=__A       ,   size=(shortest_edge, shortest_edge)       ,   data_format=__A       ,   **__A       )
						else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    __A       ,   size=(shortest_edge, shortest_edge)       ,   resample=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     Optional[Any]:
						return rescale(__A       ,   scale=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						return normalize(__A       ,   mean=__A       ,   std=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = ChannelDimension.FIRST       ,   **__A       ,   )			->     PIL.Image.Image:
						lowerCAmelCase_    :Any			     =  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase_    :Dict			     =  crop_pct if crop_pct is not None else self.crop_pct
						lowerCAmelCase_    :Optional[int]			     =  resample if resample is not None else self.resample
						lowerCAmelCase_    :Optional[Any]			     =  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase_    :int			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase_    :int			     =  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase_    :Tuple			     =  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase_    :str			     =  image_std if image_std is not None else self.image_std

						lowerCAmelCase_    :str			     =  size if size is not None else self.size
						lowerCAmelCase_    :Any			     =  get_size_dict(__A       ,   default_to_square=__A       )

						lowerCAmelCase_    :int			     =  make_list_of_images(__A       )

						if not valid_images(__A       ):
									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_resize and size["shortest_edge"] < 384 and crop_pct is None:
									raise ValueError("""crop_pct must be specified if size < 384."""       )

						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."""       )

						# All transformations expect numpy arrays.
						lowerCAmelCase_    :str			     =  [to_numpy_array(__A       ) for image in images]

						if do_resize:
									lowerCAmelCase_    :List[Any]			     =  [self.resize(image=__A       ,   size=__A       ,   crop_pct=__A       ,   resample=__A       ) for image in images]

						if do_rescale:
									lowerCAmelCase_    :Any			     =  [self.rescale(image=__A       ,   scale=__A       ) for image in images]

						if do_normalize:
									lowerCAmelCase_    :str			     =  [self.normalize(image=__A       ,   mean=__A       ,   std=__A       ) for image in images]

						lowerCAmelCase_    :Any			     =  [to_channel_dimension_format(__A       ,   __A       ) for image in images]

						lowerCAmelCase_    :Optional[Any]			     =  {"""pixel_values""": images}
						return BatchFeature(data=__A       ,   tensor_type=__A       )













'''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},
              ]						,	)