

"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
     import PIL

if is_torch_available():
     import torch


SCREAMING_SNAKE_CASE_     :			Dict	  =			logging.get_logger(__name__)


class 		_A       (			UpperCAmelCase__					):
      __a								=	['pixel_values']





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = 1 / 255    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = True    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					None:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			size if size is not None else {"""shortest_edge""": 224}
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						param_name="crop_size"		)

         lowerCamelCase__						=			do_resize
         lowerCamelCase__						=			size
         lowerCamelCase__						=			resample
         lowerCamelCase__						=			do_rescale
         lowerCamelCase__						=			rescale_factor
         lowerCamelCase__						=			do_center_crop
         lowerCamelCase__						=			crop_size
         lowerCamelCase__						=			do_flip_channel_order





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = PIL.Image.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
         if "shortest_edge" not in size:
            raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'		)
         lowerCamelCase__						=			get_resize_output_image_size(SCREAMING_SNAKE_CASE__    ,						size=size["shortest_edge"]    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
         return resize(SCREAMING_SNAKE_CASE__    ,						size=SCREAMING_SNAKE_CASE__    ,						resample=SCREAMING_SNAKE_CASE__    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         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()}'		)
         return center_crop(SCREAMING_SNAKE_CASE__    ,						size=(size["height"], size["width"])    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					np.ndarray:
         return flip_channel_order(SCREAMING_SNAKE_CASE__    ,						data_format=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					PIL.Image.Image:
         lowerCamelCase__						=			do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__						=			resample if resample is not None else self.resample
         lowerCamelCase__						=			do_rescale if do_rescale is not None else self.do_rescale
         lowerCamelCase__						=			rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCamelCase__						=			do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCamelCase__						=			(
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

         lowerCamelCase__						=			size if size is not None else self.size
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			crop_size if crop_size is not None else self.crop_size
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						param_name="crop_size"		)

         lowerCamelCase__						=			make_list_of_images(SCREAMING_SNAKE_CASE__		)

         if not valid_images(SCREAMING_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_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True."		)

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

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

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

         if do_center_crop:
            lowerCamelCase__						=			[self.center_crop(image=SCREAMING_SNAKE_CASE__    ,						size=SCREAMING_SNAKE_CASE__		) for image in images]

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

         # the pretrained checkpoints assume images are BGR, not RGB
         if do_flip_channel_order:
            lowerCamelCase__						=			[self.flip_channel_order(image=SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						=			{"""pixel_values""": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__    ,						tensor_type=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[str]:
         lowerCamelCase__						=			outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
            if len(SCREAMING_SNAKE_CASE__		) != len(SCREAMING_SNAKE_CASE__		):
               raise ValueError(
                   "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

            if is_torch_tensor(SCREAMING_SNAKE_CASE__		):
               lowerCamelCase__						=			target_sizes.numpy()

            lowerCamelCase__						=			[]

            for idx in range(len(SCREAMING_SNAKE_CASE__		)		):
               lowerCamelCase__						=			torch.nn.functional.interpolate(
                   logits[idx].unsqueeze(dim=0		)    ,						size=target_sizes[idx]    ,						mode="bilinear"    ,						align_corners=SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			resized_logits[0].argmax(dim=0		)
               semantic_segmentation.append(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			logits.argmax(dim=1		)
            lowerCamelCase__						=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

         return semantic_segmentation







"""simple docstring"""




import numpy as np



def 	UpperCAmelCase__   (				A__   )      ->      np.array:




   """simple docstring"""
   return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




