






'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

_A     :      List[str]   					=logging.get_logger(__name__)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[List[ImageInput]]:


       if isinstance(__A    ,      (list, tuple)				) and isinstance(videos[0]    ,      (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(__A    ,      (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(__A				):
              return [[videos]]

       raise ValueError(f'''Could not make batched video from {videos}'''				)



class _lowercase     (    _lowercase		):
       a    				=	['''pixel_values''']




       def __init__(							self:    List[Any]      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Union[int, float] = 1 / 255      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__:    Any      ,							):
              super().__init__(**_snake_case					)
              lowerCamelCase__ :     int	  =	size if size is not None else {"""shortest_edge""": 256}
              lowerCamelCase__ :     List[Any]	  =	get_size_dict(_snake_case      ,							default_to_square=_snake_case					)
              lowerCamelCase__ :     str	  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
              lowerCamelCase__ :     Optional[Any]	  =	get_size_dict(_snake_case      ,							param_name="""crop_size"""					)

              lowerCamelCase__ :     str	  =	do_resize
              lowerCamelCase__ :     Optional[Any]	  =	size
              lowerCamelCase__ :     List[str]	  =	do_center_crop
              lowerCamelCase__ :     Tuple	  =	crop_size
              lowerCamelCase__ :     Union[str, Any]	  =	resample
              lowerCamelCase__ :     Optional[Any]	  =	do_rescale
              lowerCamelCase__ :     Any	  =	rescale_factor
              lowerCamelCase__ :     Tuple	  =	offset
              lowerCamelCase__ :     List[Any]	  =	do_normalize
              lowerCamelCase__ :     Optional[Any]	  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              lowerCamelCase__ :     Dict	  =	image_std if image_std is not None else IMAGENET_STANDARD_STD




       def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    str      ,							):
              lowerCamelCase__ :     Tuple	  =	get_size_dict(_snake_case      ,							default_to_square=_snake_case					)
              if "shortest_edge" in size:
                     lowerCamelCase__ :     List[Any]	  =	get_resize_output_image_size(_snake_case      ,							size["""shortest_edge"""]      ,							default_to_square=_snake_case					)
              elif "height" in size and "width" in size:
                     lowerCamelCase__ :     str	  =	(size["""height"""], size["""width"""])
              else:
                     raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
              return resize(_snake_case      ,							size=_snake_case      ,							resample=_snake_case      ,							data_format=_snake_case      ,							**_snake_case					)




       def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Dict      ,							):
              lowerCamelCase__ :     Optional[int]	  =	get_size_dict(_snake_case					)
              if "height" not in size or "width" not in size:
                     raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
              return center_crop(_snake_case      ,							size=(size["""height"""], size["""width"""])      ,							data_format=_snake_case      ,							**_snake_case					)




       def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[int, float]      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Union[str, Any]      ,							):
              lowerCamelCase__ :     Tuple	  =	image.astype(np.floataa					)
              if offset:
                     lowerCamelCase__ :     Dict	  =	image - (scale / 2)
              return rescale(_snake_case      ,							scale=_snake_case      ,							data_format=_snake_case      ,							**_snake_case					)




       def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Union[str, Any]      ,							):
              return normalize(_snake_case      ,							mean=_snake_case      ,							std=_snake_case      ,							data_format=_snake_case      ,							**_snake_case					)




       def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    ImageInput      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    float = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[ChannelDimension] = ChannelDimension.FIRST      ,							):


              if do_resize and size is None or resample is None:
                     raise ValueError("""Size and resample 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."""					)

              if offset and not do_rescale:
                     raise ValueError("""For offset, do_rescale must also be set to True."""					)

              # All transformations expect numpy arrays.
              lowerCamelCase__ :     Optional[int]	  =	to_numpy_array(_snake_case					)

              if do_resize:
                     lowerCamelCase__ :     Tuple	  =	self.resize(image=_snake_case      ,							size=_snake_case      ,							resample=_snake_case					)

              if do_center_crop:
                     lowerCamelCase__ :     List[str]	  =	self.center_crop(_snake_case      ,							size=_snake_case					)

              if do_rescale:
                     lowerCamelCase__ :     Any	  =	self.rescale(image=_snake_case      ,							scale=_snake_case      ,							offset=_snake_case					)

              if do_normalize:
                     lowerCamelCase__ :     List[str]	  =	self.normalize(image=_snake_case      ,							mean=_snake_case      ,							std=_snake_case					)

              lowerCamelCase__ :     List[str]	  =	to_channel_dimension_format(_snake_case      ,							_snake_case					)
              return image




       def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    ImageInput      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    PILImageResampling = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    float = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    ChannelDimension = ChannelDimension.FIRST      ,							**UpperCamelCase__:    Tuple      ,							):
              lowerCamelCase__ :     Optional[int]	  =	do_resize if do_resize is not None else self.do_resize
              lowerCamelCase__ :     int	  =	resample if resample is not None else self.resample
              lowerCamelCase__ :     Optional[Any]	  =	do_center_crop if do_center_crop is not None else self.do_center_crop
              lowerCamelCase__ :     Optional[int]	  =	do_rescale if do_rescale is not None else self.do_rescale
              lowerCamelCase__ :     Any	  =	rescale_factor if rescale_factor is not None else self.rescale_factor
              lowerCamelCase__ :     Optional[int]	  =	offset if offset is not None else self.offset
              lowerCamelCase__ :     Union[str, Any]	  =	do_normalize if do_normalize is not None else self.do_normalize
              lowerCamelCase__ :     str	  =	image_mean if image_mean is not None else self.image_mean
              lowerCamelCase__ :     str	  =	image_std if image_std is not None else self.image_std

              lowerCamelCase__ :     List[str]	  =	size if size is not None else self.size
              lowerCamelCase__ :     Any	  =	get_size_dict(_snake_case      ,							default_to_square=_snake_case					)
              lowerCamelCase__ :     str	  =	crop_size if crop_size is not None else self.crop_size
              lowerCamelCase__ :     int	  =	get_size_dict(_snake_case      ,							param_name="""crop_size"""					)

              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."""					)

              lowerCamelCase__ :     Tuple	  =	make_batched(_snake_case					)

              lowerCamelCase__ :     int	  =	[
                  [
                      self._preprocess_image(
                          image=_snake_case      ,							do_resize=_snake_case      ,							size=_snake_case      ,							resample=_snake_case      ,							do_center_crop=_snake_case      ,							crop_size=_snake_case      ,							do_rescale=_snake_case      ,							rescale_factor=_snake_case      ,							offset=_snake_case      ,							do_normalize=_snake_case      ,							image_mean=_snake_case      ,							image_std=_snake_case      ,							data_format=_snake_case      ,							)
                      for img in video
                  ]
                  for video in videos
              ]

              lowerCamelCase__ :     Tuple	  =	{"""pixel_values""": videos}
              return BatchFeature(data=_snake_case      ,							tensor_type=_snake_case					)









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							lowerCamelCase__ :     List[str]	  =	np.zeros((image.shape[0], image.shape[1])				)

							# Iterating through the image and calculating the local binary pattern value
							# for each pixel.
							for i in range(0    ,      image.shape[0]				):
														for j in range(0    ,      image.shape[1]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

