



'''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


lowercase							       =			logging.get_logger(__name__)







class 	UpperCAmelCase	(		__a):


	'''simple docstring'''




	__magic_name__					: Optional[int]  =      ["pixel_values"]




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



						"""simple docstring"""







						super().__init__(**lowercase_)
						a_       		=size if size is not None else {"shortest_edge": 2_2_4}
						a_       		=get_size_dict(lowercase_							,  default_to_square=lowercase_)
						a_       		=crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
						a_       		=get_size_dict(lowercase_							,  param_name="crop_size")

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




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = PIL.Image.BILINEAR							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



						"""simple docstring"""







						a_       		=get_size_dict(lowercase_							,  default_to_square=lowercase_)
						if "shortest_edge" not in size:
											raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""")
						a_       		=get_resize_output_image_size(lowercase_							,  size=size["shortest_edge"]							,  default_to_square=lowercase_)
						return resize(lowercase_							,  size=lowercase_							,  resample=lowercase_							,  data_format=lowercase_							,  **lowercase_)




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



						"""simple docstring"""







						a_       		=get_size_dict(lowercase_)
						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(lowercase_							,  size=(size["height"], size["width"])							,  data_format=lowercase_							,  **lowercase_)




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      int:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      np.ndarray:



						"""simple docstring"""







						return flip_channel_order(lowercase_							,  data_format=lowercase_)




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = ChannelDimension.FIRST							,  **lowerCAmelCase_							,  ) ->      PIL.Image.Image:



						"""simple docstring"""







						a_       		=do_resize if do_resize is not None else self.do_resize
						a_       		=resample if resample is not None else self.resample
						a_       		=do_rescale if do_rescale is not None else self.do_rescale
						a_       		=rescale_factor if rescale_factor is not None else self.rescale_factor
						a_       		=do_center_crop if do_center_crop is not None else self.do_center_crop
						a_       		=(
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

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

						a_       		=make_list_of_images(lowercase_)

						if not valid_images(lowercase_):
											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.
						a_       		=[to_numpy_array(lowercase_) for image in images]

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

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

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

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

						a_       		=[to_channel_dimension_format(lowercase_							,  lowercase_) for image in images]

						a_       		={"pixel_values": images}
						return BatchFeature(data=lowercase_							,  tensor_type=lowercase_)




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Optional[Any]:



						"""simple docstring"""







						a_       		=outputs.logits

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

											if is_torch_tensor(lowercase_):
																a_       		=target_sizes.numpy()

											a_       		=[]

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

						return semantic_segmentation







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


