


"""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,
    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


__snake_case             =   logging.get_logger(__name__)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Dict							  =  ['''pixel_values''']







   def __init__(     self						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = PILImageResampling.BILINEAR						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = 1 / 255						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		None:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)
       snake_case   :				Optional[int]				 =   size if size is not None else {"shortest_edge": 256}
       snake_case   :				Union[str, Any]				 =   get_size_dict(UpperCamelCase__						,     default_to_square=UpperCamelCase__						)
       snake_case   :				int				 =   crop_size if crop_size is not None else {"height": 224, "width": 224}
       snake_case   :				List[str]				 =   get_size_dict(UpperCamelCase__						,     param_name="crop_size"						)
       snake_case   :				Optional[int]				 =   do_resize
       snake_case   :				Optional[int]				 =   size
       snake_case   :				int				 =   resample
       snake_case   :				Union[str, Any]				 =   do_center_crop
       snake_case   :				Optional[int]				 =   crop_size
       snake_case   :				Union[str, Any]				 =   do_rescale
       snake_case   :				Any				 =   rescale_factor
       snake_case   :				Optional[Any]				 =   do_normalize
       snake_case   :				Tuple				 =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case   :				Optional[Any]				 =   image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = PILImageResampling.BICUBIC						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   get_size_dict(UpperCamelCase__						,     default_to_square=UpperCamelCase__						)
       if "shortest_edge" not in size:
           raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
       snake_case   :				int				 =   get_resize_output_image_size(UpperCamelCase__						,     size=size["shortest_edge"]						,     default_to_square=UpperCamelCase__						)
       return resize(UpperCamelCase__						,     size=UpperCamelCase__						,     resample=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       snake_case   :				Any				 =   get_size_dict(UpperCamelCase__						)
       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(UpperCamelCase__						,     size=(size["height"], size["width"])						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     **UpperCamelCase__						)     ->		np.ndarray:

       '''simple docstring'''




       return rescale(UpperCamelCase__						,     scale=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		np.ndarray:

       '''simple docstring'''




       return normalize(UpperCamelCase__						,     mean=UpperCamelCase__						,     std=UpperCamelCase__						,     data_format=UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = ChannelDimension.FIRST						,     **UpperCamelCase__						,     )     ->		List[str]:

       '''simple docstring'''




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

       snake_case   :				Tuple				 =   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:
           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.
       snake_case   :				Union[str, Any]				 =   [to_numpy_array(UpperCamelCase__						) for image in images]

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

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

       if do_rescale:
           snake_case   :				Optional[Any]				 =   [self.rescale(image=UpperCamelCase__						,     scale=UpperCamelCase__						) for image in images]

       if do_normalize:
           snake_case   :				List[Any]				 =   [self.normalize(image=UpperCamelCase__						,     mean=UpperCamelCase__						,     std=UpperCamelCase__						) for image in images]

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

       snake_case   :				Optional[int]				 =   {"pixel_values": images}
       return BatchFeature(data=UpperCamelCase__						,     tensor_type=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   outputs.logits

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

           if is_torch_tensor(UpperCamelCase__						):
               snake_case   :				Optional[Any]				 =   target_sizes.numpy()

           snake_case   :				Optional[int]				 =   []

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

       return semantic_segmentation









"""simple docstring"""



from __future__ import annotations

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]





