


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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


SCREAMING_SNAKE_CASE :   Any							      =				logging.get_logger(__name__)






class _lowerCamelCase(		_a     ):
       lowercase_				:			str									=    ["""pixel_values"""]
       def __init__(      self,   lowerCamelCase = True,   lowerCamelCase = None,   lowerCamelCase = PILImageResampling.BILINEAR,   lowerCamelCase = True,   lowerCamelCase = None,   lowerCamelCase = True,   lowerCamelCase = 1 / 2_55,   lowerCamelCase = True,   lowerCamelCase = None,   lowerCamelCase = None,   **lowerCamelCase,   ) ->     None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							List[Any]   	=	size if size is not None else {'shortest_edge': 2_56}
           _lowercase :							Tuple   	=	get_size_dict(lowerCamelCase,   default_to_square=lowerCamelCase)
           _lowercase :							Optional[int]   	=	crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
           _lowercase :							Optional[int]   	=	get_size_dict(lowerCamelCase,   param_name='crop_size')
           _lowercase :							Dict   	=	do_resize
           _lowercase :							List[Any]   	=	size
           _lowercase :							int   	=	resample
           _lowercase :							Dict   	=	do_center_crop
           _lowercase :							str   	=	crop_size
           _lowercase :							int   	=	do_rescale
           _lowercase :							Tuple   	=	rescale_factor
           _lowercase :							Union[str, Any]   	=	do_normalize
           _lowercase :							Tuple   	=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _lowercase :							str   	=	image_std if image_std is not None else IMAGENET_STANDARD_STD
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = PILImageResampling.BICUBIC,   lowerCamelCase = None,   **lowerCamelCase,   ) ->     np.ndarray:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	get_size_dict(lowerCamelCase,   default_to_square=lowerCamelCase)
           if "shortest_edge" not in size:
               raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
           _lowercase :							Optional[int]   	=	get_resize_output_image_size(lowerCamelCase,   size=size['shortest_edge'],   default_to_square=lowerCamelCase)
           return resize(lowerCamelCase,   size=lowerCamelCase,   resample=lowerCamelCase,   data_format=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = None,   **lowerCamelCase,   ) ->     np.ndarray:




           """simple docstring"""

           _lowercase :							Tuple   	=	get_size_dict(lowerCamelCase)
           if "height" not in size or "width" not in size:
               raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''')
           return center_crop(lowerCamelCase,   size=(size['height'], size['width']),   data_format=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = None,   **lowerCamelCase) ->     np.ndarray:




           """simple docstring"""

           return rescale(lowerCamelCase,   scale=lowerCamelCase,   data_format=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = None,   **lowerCamelCase,   ) ->     np.ndarray:




           """simple docstring"""

           return normalize(lowerCamelCase,   mean=lowerCamelCase,   std=lowerCamelCase,   data_format=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = ChannelDimension.FIRST,   **lowerCamelCase,   ) ->     Union[str, Any]:




           """simple docstring"""

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

           _lowercase :							Optional[int]   	=	make_list_of_images(lowerCamelCase)

           if not valid_images(lowerCamelCase):
               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.')

           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.
           _lowercase :							List[str]   	=	[to_numpy_array(lowerCamelCase) for image in images]

           if do_resize:
               _lowercase :							Tuple   	=	[self.resize(image=lowerCamelCase,   size=lowerCamelCase,   resample=lowerCamelCase) for image in images]

           if do_center_crop:
               _lowercase :							Tuple   	=	[self.center_crop(image=lowerCamelCase,   size=lowerCamelCase) for image in images]

           if do_rescale:
               _lowercase :							int   	=	[self.rescale(image=lowerCamelCase,   scale=lowerCamelCase) for image in images]

           if do_normalize:
               _lowercase :							int   	=	[self.normalize(image=lowerCamelCase,   mean=lowerCamelCase,   std=lowerCamelCase) for image in images]

           _lowercase :							Any   	=	[to_channel_dimension_format(lowerCamelCase,   lowerCamelCase) for image in images]

           _lowercase :							Dict   	=	{'pixel_values': images}
           return BatchFeature(data=lowerCamelCase,   tensor_type=lowerCamelCase)




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	outputs.logits

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

               if is_torch_tensor(lowerCamelCase):
                   _lowercase :							int   	=	target_sizes.numpy()

               _lowercase :							Any   	=	[]

               for idx in range(len(lowerCamelCase)):
                   _lowercase :							Union[str, Any]   	=	torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0),   size=target_sizes[idx],   mode='bilinear',   align_corners=lowerCamelCase)
                   _lowercase :							Optional[Any]   	=	resized_logits[0].argmax(dim=0)
                   semantic_segmentation.append(lowerCamelCase)
           else:
               _lowercase :							Dict   	=	logits.argmax(dim=1)
               _lowercase :							Tuple   	=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

           return semantic_segmentation






def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


