


'''simple docstring'''
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_ :     Optional[int]								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
 if isinstance(UpperCamelCase__,				(list, tuple)			) and isinstance(videos[0],				(list, tuple)			) and is_valid_image(videos[0][0]			):
  return videos

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

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

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


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						['''pixel_values''']


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

  """simple docstring"""







  super().__init__(**__lowercase						)
  __A  :		Union[str, Any]       =			size if size is not None else {'shortest_edge': 224}
  __A  :		Tuple       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
  __A  :		List[Any]       =			get_size_dict(__lowercase      ,      param_name='crop_size'						)

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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = PILImageResampling.BILINEAR      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		str       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  if "shortest_edge" in size:
   __A  :		Union[str, Any]       =			get_resize_output_image_size(__lowercase      ,      size['shortest_edge']      ,      default_to_square=__lowercase						)
  elif "height" in size and "width" in size:
   __A  :		List[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(__lowercase      ,      size=__lowercase      ,      resample=__lowercase      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Tuple       =			get_size_dict(__lowercase						)
  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(__lowercase      ,      size=(size['height'], size['width'])      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      ):

  """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.
  __A  :		Tuple       =			to_numpy_array(__lowercase						)

  if do_resize:
   __A  :		List[Any]       =			self.resize(image=__lowercase      ,      size=__lowercase      ,      resample=__lowercase						)

  if do_center_crop:
   __A  :		List[Any]       =			self.center_crop(__lowercase      ,      size=__lowercase						)

  if do_rescale:
   __A  :		Any       =			self.rescale(image=__lowercase      ,      scale=__lowercase						)

  if do_normalize:
   __A  :		str       =			self.normalize(image=__lowercase      ,      mean=__lowercase      ,      std=__lowercase						)

  __A  :		Dict       =			to_channel_dimension_format(__lowercase      ,      __lowercase						)
  return image







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			do_resize if do_resize is not None else self.do_resize
  __A  :		str       =			resample if resample is not None else self.resample
  __A  :		Optional[int]       =			do_center_crop if do_center_crop is not None else self.do_center_crop
  __A  :		Tuple       =			do_rescale if do_rescale is not None else self.do_rescale
  __A  :		str       =			rescale_factor if rescale_factor is not None else self.rescale_factor
  __A  :		int       =			do_normalize if do_normalize is not None else self.do_normalize
  __A  :		List[Any]       =			image_mean if image_mean is not None else self.image_mean
  __A  :		List[Any]       =			image_std if image_std is not None else self.image_std

  __A  :		Optional[Any]       =			size if size is not None else self.size
  __A  :		int       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else self.crop_size
  __A  :		Any       =			get_size_dict(__lowercase      ,      param_name='crop_size'						)

  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.'						)

  __A  :		List[Any]       =			make_batched(__lowercase						)

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

  __A  :		List[str]       =			{'pixel_values': videos}
  return BatchFeature(data=__lowercase      ,      tensor_type=__lowercase						)










'''simple docstring'''
import math

class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			0.0
  __A  :		Optional[int]       =			0.0
  for i in range(len(__lowercase						)						):
   da += math.pow((sample[i] - weights[0][i])      ,      2						)
   da += math.pow((sample[i] - weights[1][i])      ,      2						)
   return 0 if da > da else 1
  return 0







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  for i in range(len(__lowercase						)						):
   weights[j][i] += alpha * (sample[i] - weights[j][i])
  return weights


def 			_lowercase       (				):
 # Training Examples ( m, n )
 __A  :		Union[str, Any]       =			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

 # weight initialization ( n, C )
 __A  :		Tuple       =			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

 # training
 __A  :		Optional[int]       =			SelfOrganizingMap()
 __A  :		int       =			3
 __A  :		Optional[Any]       =			0.5

 for _ in range(UpperCamelCase__			):
  for j in range(len(UpperCamelCase__			)			):
   # training sample
   __A  :		Dict       =			training_samples[j]

   # Compute the winning vector
   __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

   # Update the winning vector
   __A  :		Union[str, Any]       =			self_organizing_map.update(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

    # classify test sample
 __A  :		int       =			[0, 0, 0, 1]
 __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

 # results
 print(f"""Clusters that the test sample belongs to : {winner}"""			)
 print(f"""Weights that have been trained : {weights}"""			)


# running the main() function
if __name__ == "__main__":
 main()






