


'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["pixel_values"]






       def __init__(     self    ,				snake_case = True    ,				snake_case = None    ,				snake_case = PILImageResampling.BICUBIC    ,				snake_case = True    ,				snake_case = True    ,				snake_case = 1 / 2_5_5    ,				snake_case = None    ,				snake_case = True    ,				snake_case = None    ,				snake_case = None    ,				**snake_case    ,				)		->    None:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase  :  List[str]				       =size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase  :  Tuple				       =get_size_dict(snake_case)
          _UpperCAmelCase  :  Dict				       =crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase  :  Any				       =get_size_dict(snake_case    ,				default_to_square=snake_case    ,				param_name='crop_size')

          _UpperCAmelCase  :  Tuple				       =do_resize
          _UpperCAmelCase  :  Optional[int]				       =do_rescale
          _UpperCAmelCase  :  Optional[int]				       =do_normalize
          _UpperCAmelCase  :  str				       =do_center_crop
          _UpperCAmelCase  :  int				       =crop_size
          _UpperCAmelCase  :  Optional[int]				       =size
          _UpperCAmelCase  :  Dict				       =resample
          _UpperCAmelCase  :  str				       =rescale_factor
          _UpperCAmelCase  :  str				       =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          _UpperCAmelCase  :  List[Any]				       =image_std if image_std is not None else IMAGENET_DEFAULT_STD






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = PILImageResampling.BILINEAR    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =get_size_dict(snake_case)

          if "shortest_edge" in size:
             _UpperCAmelCase  :  Any				       =get_resize_output_image_size(snake_case    ,				size=size['shortest_edge']    ,				default_to_square=snake_case)
             # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
             _UpperCAmelCase  :  List[str]				       =(size['height'], size['width'])
          else:
             raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}")
          return resize(snake_case    ,				size=snake_case    ,				resample=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =get_size_dict(snake_case)
          if "height" not in size or "width" not in size:
             raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
          return center_crop(snake_case    ,				size=(size['height'], size['width'])    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case)		->    np.ndarray:




          '''simple docstring'''
          return rescale(snake_case    ,				scale=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          return normalize(snake_case    ,				mean=snake_case    ,				std=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = ChannelDimension.FIRST    ,				**snake_case    ,				)		->    BatchFeature:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase  :  int				       =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase  :  str				       =do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase  :  List[str]				       =do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase  :  Optional[int]				       =crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase  :  Optional[Any]				       =get_size_dict(snake_case    ,				param_name='crop_size'    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  Union[str, Any]				       =resample if resample is not None else self.resample
          _UpperCAmelCase  :  Any				       =rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase  :  Optional[Any]				       =image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase  :  Dict				       =image_std if image_std is not None else self.image_std

          _UpperCAmelCase  :  Tuple				       =size if size is not None else self.size
          _UpperCAmelCase  :  int				       =get_size_dict(snake_case)

          if not is_batched(snake_case):
             _UpperCAmelCase  :  Union[str, Any]				       =[images]

          if not valid_images(snake_case):
             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:
             raise ValueError('Size must be specified if do_resize is True.')

          if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.')

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

          # All transformations expect numpy arrays.
          _UpperCAmelCase  :  Optional[Any]				       =[to_numpy_array(snake_case) for image in images]

          if do_resize:
             _UpperCAmelCase  :  Optional[Any]				       =[self.resize(image=snake_case    ,				size=snake_case    ,				resample=snake_case) for image in images]

          if do_center_crop:
             _UpperCAmelCase  :  Optional[Any]				       =[self.center_crop(image=snake_case    ,				size=snake_case) for image in images]

          if do_rescale:
             _UpperCAmelCase  :  Union[str, Any]				       =[self.rescale(image=snake_case    ,				scale=snake_case) for image in images]

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

          _UpperCAmelCase  :  Optional[int]				       =[to_channel_dimension_format(snake_case    ,				snake_case) for image in images]

          _UpperCAmelCase  :  int				       ={'pixel_values': images}
          return BatchFeature(data=snake_case    ,				tensor_type=snake_case)





'''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},
              ]    ,				)

