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 (
    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 TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


UpperCamelCase__											=					logging.get_logger(__name__)




def    lowerCAmelCase_ (     __A							)     ->				List[List[ImageInput]]:







      '''simple docstring'''


      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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[Any]      =			['pixel_values']


   def __init__(self     :						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 / 2_5_5       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				None:
         """simple docstring"""



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

         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_center_crop
         UpperCAmelCase__								=  crop_size
         UpperCAmelCase__								=  resample
         UpperCAmelCase__								=  do_rescale
         UpperCAmelCase__								=  rescale_factor
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__								=  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Dict[str, int]       ,  __UpperCAmelCase     :						PILImageResampling = PILImageResampling.BILINEAR       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						int       ,  )						->				np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase       ,  default_to_square=__UpperCAmelCase		)
         if "shortest_edge" in size:
               UpperCAmelCase__								=  get_resize_output_image_size(__UpperCAmelCase       ,  size["shortest_edge"]       ,  default_to_square=__UpperCAmelCase		)
         elif "height" in size and "width" in size:
               UpperCAmelCase__								=  (size["height"], size["width"])
         else:
               raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
         return resize(__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase       ,  data_format=__UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Dict[str, int]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						Any       ,  )						->				np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)
         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(__UpperCAmelCase       ,  size=(size["height"], size["width"])       ,  data_format=__UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Union[int, float]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				str:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Union[float, List[float]]       ,  __UpperCAmelCase     :						Union[float, List[float]]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						Dict       ,  )						->				np.ndarray:
         """simple docstring"""



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


   def 			lowercase_      (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     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[ChannelDimension] = ChannelDimension.FIRST       ,  )						->				np.ndarray:
         """simple docstring"""



         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."		)

         # All transformations expect numpy arrays.
         UpperCAmelCase__								=  to_numpy_array(__UpperCAmelCase		)

         if do_resize:
               UpperCAmelCase__								=  self.resize(image=__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase		)

         if do_center_crop:
               UpperCAmelCase__								=  self.center_crop(__UpperCAmelCase       ,  size=__UpperCAmelCase		)

         if do_rescale:
               UpperCAmelCase__								=  self.rescale(image=__UpperCAmelCase       ,  scale=__UpperCAmelCase		)

         if do_normalize:
               UpperCAmelCase__								=  self.normalize(image=__UpperCAmelCase       ,  mean=__UpperCAmelCase       ,  std=__UpperCAmelCase		)

         UpperCAmelCase__								=  to_channel_dimension_format(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return image





   def 			lowercase_      (self     :						List[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     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[str, TensorType]] = None       ,  __UpperCAmelCase     :						ChannelDimension = ChannelDimension.FIRST       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				PIL.Image.Image:
         """simple docstring"""



         UpperCAmelCase__								=  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__								=  resample if resample is not None else self.resample
         UpperCAmelCase__								=  do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__								=  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__								=  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__								=  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__								=  image_std if image_std is not None else self.image_std

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

         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."		)

         UpperCAmelCase__								=  make_batched(__UpperCAmelCase		)

         UpperCAmelCase__								=  [
             [
                 self._preprocess_image(
                     image=__UpperCAmelCase       ,  do_resize=__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase       ,  do_center_crop=__UpperCAmelCase       ,  crop_size=__UpperCAmelCase       ,  do_rescale=__UpperCAmelCase       ,  rescale_factor=__UpperCAmelCase       ,  do_normalize=__UpperCAmelCase       ,  image_mean=__UpperCAmelCase       ,  image_std=__UpperCAmelCase       ,  data_format=__UpperCAmelCase       ,  )
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase__								=  {"pixel_values": videos}
         return BatchFeature(data=__UpperCAmelCase       ,  tensor_type=__UpperCAmelCase		)




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      UpperCamelCase__											=					np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      UpperCamelCase__											=					[0, 2_5, 5_0]
      UpperCamelCase__											=					[2_5, 5_0, 7_5]
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      UpperCamelCase__											=					np.ones(7_5)
      UpperCamelCase__											=					np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      UpperCamelCase__											=					fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      UpperCamelCase__											=					young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      UpperCamelCase__											=					young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title('Young')
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title('Middle aged')
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title('union')
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title('intersection')
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title('complement_a')
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title('difference a/b')
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title('alg_sum')
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title('alg_product')
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title('bdd_sum')
      plt.grid(True)

      plt.subplot(4, 3, 1_0)
      plt.plot(X, bdd_difference)
      plt.title('bdd_difference')
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()



