




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCAmelCase_   							=      logging.get_logger(__name__)


if is_vision_available():
       import PIL





class        lowerCamelCase__(   lowercase__):
      UpperCAmelCase__ :      Dict       						=  ['pixel_values']
      def __init__(							self:					Tuple       ,			UpperCamelCase_:					Optional[int] = True       ,			UpperCamelCase_:					Optional[Any] = None       ,			UpperCamelCase_:					Any = PILImageResampling.BILINEAR       ,			UpperCamelCase_:					Union[str, Any] = True       ,			UpperCamelCase_:					Union[str, Any] = False       ,			UpperCamelCase_:					Union[str, Any] = True       ,			UpperCamelCase_:					Tuple = True       ,			UpperCamelCase_:					int = 1 / 2_55       ,			UpperCamelCase_:					Optional[Any] = True       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					int = None       ,			**UpperCamelCase_:					Any       ,			):
             super().__init__(**UpperCamelCase_  )

             __lowerCamelCase  =		size if size is not None else {"height": 25_60, "width": 19_20}
             if isinstance(UpperCamelCase_       ,			(tuple, list)  ):
                    # The previous feature extractor size parameter was in (width, height) format
                    __lowerCamelCase  =		size[::-1]
             __lowerCamelCase  =		get_size_dict(UpperCamelCase_  )

             __lowerCamelCase  =		do_resize
             __lowerCamelCase  =		size
             __lowerCamelCase  =		resample
             __lowerCamelCase  =		do_thumbnail
             __lowerCamelCase  =		do_align_long_axis
             __lowerCamelCase  =		do_pad
             __lowerCamelCase  =		do_rescale
             __lowerCamelCase  =		rescale_factor
             __lowerCamelCase  =		do_normalize
             __lowerCamelCase  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             __lowerCamelCase  =		image_std if image_std is not None else IMAGENET_STANDARD_STD
      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					str = None  ):
             __lowerCamelCase  =		get_image_size(UpperCamelCase_  )
             __lowerCamelCase  =		size["height"], size["width"]

             if (output_width < output_height and input_width > input_height) or (
                 output_width > output_height and input_width < input_height
             ):
                    __lowerCamelCase  =		np.rotaa(UpperCamelCase_       ,			3  )

             if data_format is not None:
                    __lowerCamelCase  =		to_channel_dimension_format(UpperCamelCase_       ,			UpperCamelCase_  )

             return image
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Optional[Any] = False       ,			UpperCamelCase_:					int = None       ,			):
             __lowerCamelCase  =		size["height"], size["width"]
             __lowerCamelCase  =		get_image_size(UpperCamelCase_  )

             __lowerCamelCase  =		output_width - input_width
             __lowerCamelCase  =		output_height - input_height

             if random_padding:
                    __lowerCamelCase  =		np.random.randint(low=0       ,			high=delta_height + 1  )
                    __lowerCamelCase  =		np.random.randint(low=0       ,			high=delta_width + 1  )
             else:
                    __lowerCamelCase  =		delta_height // 2
                    __lowerCamelCase  =		delta_width // 2

             __lowerCamelCase  =		delta_height - pad_top
             __lowerCamelCase  =		delta_width - pad_left

             __lowerCamelCase  =		((pad_top, pad_bottom), (pad_left, pad_right))
             return pad(UpperCamelCase_       ,			UpperCamelCase_       ,			data_format=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			*UpperCamelCase_:					Optional[int]       ,			**UpperCamelCase_:					Optional[int]  ):
             logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
             return self.pad_image(*UpperCamelCase_       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[int]       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					List[Any]       ,			UpperCamelCase_:					List[Any] = PILImageResampling.BICUBIC       ,			UpperCamelCase_:					Dict = None       ,			**UpperCamelCase_:					Any       ,			):
             __lowerCamelCase  =		get_image_size(UpperCamelCase_  )
             __lowerCamelCase  =		size["height"], size["width"]

             # We always resize to the smallest of either the input or output size.
             __lowerCamelCase  =		min(UpperCamelCase_       ,			UpperCamelCase_  )
             __lowerCamelCase  =		min(UpperCamelCase_       ,			UpperCamelCase_  )

             if height == input_height and width == input_width:
                    return image

             if input_height > input_width:
                    __lowerCamelCase  =		int(input_width * height / input_height  )
             elif input_width > input_height:
                    __lowerCamelCase  =		int(input_height * width / input_width  )

             return resize(
                 UpperCamelCase_       ,			size=(height, width)       ,			resample=UpperCamelCase_       ,			reducing_gap=2.0       ,			data_format=UpperCamelCase_       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					Dict       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Optional[Any] = PILImageResampling.BICUBIC       ,			UpperCamelCase_:					Optional[int] = None       ,			**UpperCamelCase_:					Dict       ,			):
             __lowerCamelCase  =		get_size_dict(UpperCamelCase_  )
             __lowerCamelCase  =		min(size["""height"""]       ,			size["""width"""]  )
             __lowerCamelCase  =		get_resize_output_image_size(UpperCamelCase_       ,			size=UpperCamelCase_       ,			default_to_square=UpperCamelCase_  )
             __lowerCamelCase  =		resize(UpperCamelCase_       ,			size=UpperCamelCase_       ,			resample=UpperCamelCase_       ,			data_format=UpperCamelCase_       ,			**UpperCamelCase_  )
             return resized_image
      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Union[str, Any]       ,			UpperCamelCase_:					Union[str, Any]       ,			UpperCamelCase_:					List[Any] = None       ,			**UpperCamelCase_:					str       ,			):
             return rescale(UpperCamelCase_       ,			scale=UpperCamelCase_       ,			data_format=UpperCamelCase_       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					List[Any]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					int = None       ,			**UpperCamelCase_:					Tuple       ,			):
             return normalize(UpperCamelCase_       ,			mean=UpperCamelCase_       ,			std=UpperCamelCase_       ,			data_format=UpperCamelCase_       ,			**UpperCamelCase_  )




      def      lowerCAmelCase__		(							self:					str       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					List[str] = None       ,			UpperCamelCase_:					int = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					List[str] = None       ,			UpperCamelCase_:					List[str] = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					List[Any] = False       ,			UpperCamelCase_:					Any = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					str = None       ,			UpperCamelCase_:					Tuple = None       ,			UpperCamelCase_:					List[Any] = None       ,			UpperCamelCase_:					Dict = None       ,			UpperCamelCase_:					List[Any] = ChannelDimension.FIRST       ,			**UpperCamelCase_:					List[str]       ,			):
             __lowerCamelCase  =		do_resize if do_resize is not None else self.do_resize
             __lowerCamelCase  =		size if size is not None else self.size
             if isinstance(UpperCamelCase_       ,			(tuple, list)  ):
                    # Previous feature extractor had size in (width, height) format
                    __lowerCamelCase  =		size[::-1]
             __lowerCamelCase  =		get_size_dict(UpperCamelCase_  )
             __lowerCamelCase  =		resample if resample is not None else self.resample
             __lowerCamelCase  =		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
             __lowerCamelCase  =		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
             __lowerCamelCase  =		do_pad if do_pad is not None else self.do_pad
             __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_normalize if do_normalize is not None else self.do_normalize
             __lowerCamelCase  =		image_mean if image_mean is not None else self.image_mean
             __lowerCamelCase  =		image_std if image_std is not None else self.image_std

             __lowerCamelCase  =		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_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

             if do_pad and size is None:
                    raise ValueError("""Size must be specified if do_pad 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.
             __lowerCamelCase  =		[to_numpy_array(UpperCamelCase_  ) for image in images]

             if do_align_long_axis:
                    __lowerCamelCase  =		[self.align_long_axis(UpperCamelCase_       ,			size=UpperCamelCase_  ) for image in images]

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

             if do_thumbnail:
                    __lowerCamelCase  =		[self.thumbnail(image=UpperCamelCase_       ,			size=UpperCamelCase_  ) for image in images]

             if do_pad:
                    __lowerCamelCase  =		[self.pad_image(image=UpperCamelCase_       ,			size=UpperCamelCase_       ,			random_padding=UpperCamelCase_  ) for image in images]

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

             if do_normalize:
                    __lowerCamelCase  =		[self.normalize(image=UpperCamelCase_       ,			mean=UpperCamelCase_       ,			std=UpperCamelCase_  ) for image in images]

             __lowerCamelCase  =		[to_channel_dimension_format(UpperCamelCase_       ,			UpperCamelCase_  ) for image in images]

             __lowerCamelCase  =		{"pixel_values": images}
             return BatchFeature(data=UpperCamelCase_       ,			tensor_type=UpperCamelCase_  )






def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[int]:






      '''simple docstring'''







      if hue < 0 or hue > 3_6_0:
            raise Exception("hue should be between 0 and 360"     )

      if saturation < 0 or saturation > 1:
            raise Exception("saturation should be between 0 and 1"     )

      if value < 0 or value > 1:
            raise Exception("value should be between 0 and 1"     )

      UpperCAmelCase_	:						Tuple					   =		value * saturation
      UpperCAmelCase_	:						Union[str, Any]					   =		hue / 6_0
      UpperCAmelCase_	:						Tuple					   =		chroma * (1 - abs(hue_section % 2 - 1     ))
      UpperCAmelCase_	:						Tuple					   =		value - chroma

      if hue_section >= 0 and hue_section <= 1:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 1 and hue_section <= 2:
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 2 and hue_section <= 3:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )
      elif hue_section > 3 and hue_section <= 4:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Optional[Any]					   =		round(2_5_5 * (chroma + match_value)     )
      elif hue_section > 4 and hue_section <= 5:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (chroma + match_value)     )
      else:
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )

      return [red, green, blue]







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[float]:






      '''simple docstring'''







      if red < 0 or red > 2_5_5:
            raise Exception("red should be between 0 and 255"     )

      if green < 0 or green > 2_5_5:
            raise Exception("green should be between 0 and 255"     )

      if blue < 0 or blue > 2_5_5:
            raise Exception("blue should be between 0 and 255"     )

      UpperCAmelCase_	:						str					   =		red / 2_5_5
      UpperCAmelCase_	:						Dict					   =		green / 2_5_5
      UpperCAmelCase_	:						int					   =		blue / 2_5_5
      UpperCAmelCase_	:						Any					   =		max(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Union[str, Any]					   =		value - min(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Optional[Any]					   =		0 if value == 0 else chroma / value

      if chroma == 0:
            UpperCAmelCase_	:						Tuple					   =		0.0
      elif value == float_red:
            UpperCAmelCase_	:						List[str]					   =		6_0 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
            UpperCAmelCase_	:						Union[str, Any]					   =		6_0 * (2 + (float_blue - float_red) / chroma)
      else:
            UpperCAmelCase_	:						Dict					   =		6_0 * (4 + (float_red - float_green) / chroma)

      UpperCAmelCase_	:						int					   =		(hue + 3_6_0) % 3_6_0

      return [hue, saturation, value]







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  bool:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		abs(hsv_a[0] - hsv_a[0]     ) < 0.2
      UpperCAmelCase_	:						Optional[Any]					   =		abs(hsv_a[1] - hsv_a[1]     ) < 0.002
      UpperCAmelCase_	:						Tuple					   =		abs(hsv_a[2] - hsv_a[2]     ) < 0.002

      return check_hue and check_saturation and check_value
