
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 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_vision_available, logging


if is_vision_available():
     import PIL


lowerCamelCase__    :		List[Any]          =   logging.get_logger(__name__)

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   str    		=      b.T
  lowercase__  :   Tuple    		=      np.sum(np.square(lowercase_   ) , axis=1   )
  lowercase__  :   int    		=      np.sum(np.square(lowercase_   ) , axis=0   )
  lowercase__  :   Optional[Any]    		=      np.matmul(lowercase_ , lowercase_   )
  lowercase__  :   Dict    		=      aa[:, None] - 2 * ab + ba[None, :]
  return d

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   int    		=      x.reshape(-1 , 3   )
  lowercase__  :   Dict    		=      squared_euclidean_distance(lowercase_ , lowercase_   )
  return np.argmin(lowercase_ , axis=1   )



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			['pixel_values']


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = True					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      size if size is not None else {"""height""": 2_56, """width""": 2_56}
   lowercase__  :   Tuple    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      np.array(SCREAMING_SNAKE_CASE_) if clusters is not None else None
   lowercase__  :   Union[str, Any]    		=      do_resize
   lowercase__  :   Tuple    		=      size
   lowercase__  :   Optional[Any]    		=      resample
   lowercase__  :   int    		=      do_normalize
   lowercase__  :   List[str]    		=      do_color_quantize


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   if "height" not in size or "width" not in size:
     raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}')
   return resize(
       SCREAMING_SNAKE_CASE_					,  size=(size["""height"""], size["""width"""])					,  resample=SCREAMING_SNAKE_CASE_					,  data_format=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  ):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      rescale(image=SCREAMING_SNAKE_CASE_					,  scale=1 / 1_2_7.5					,  data_format=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      image - 1
   return image



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      do_resize if do_resize is not None else self.do_resize
   lowercase__  :   List[str]    		=      size if size is not None else self.size
   lowercase__  :   Optional[int]    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      resample if resample is not None else self.resample
   lowercase__  :   Dict    		=      do_normalize if do_normalize is not None else self.do_normalize
   lowercase__  :   List[str]    		=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
   lowercase__  :   Union[str, Any]    		=      clusters if clusters is not None else self.clusters
   lowercase__  :   int    		=      np.array(SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     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 or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True.""")

   if do_color_quantize and clusters is None:
     raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

   # All transformations expect numpy arrays.
   lowercase__  :   Dict    		=      [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_resize:
     lowercase__  :   Tuple    		=      [self.resize(image=SCREAMING_SNAKE_CASE_					,  size=SCREAMING_SNAKE_CASE_					,  resample=SCREAMING_SNAKE_CASE_) for image in images]

   if do_normalize:
     lowercase__  :   List[Any]    		=      [self.normalize(image=SCREAMING_SNAKE_CASE_) for image in images]

   if do_color_quantize:
     lowercase__  :   Dict    		=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,  ChannelDimension.LAST) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     lowercase__  :   int    		=      np.array(SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[int]    		=      color_quantize(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_).reshape(images.shape[:-1])

     # flatten to (batch_size, height*width)
     lowercase__  :   Any    		=      images.shape[0]
     lowercase__  :   Dict    		=      images.reshape(SCREAMING_SNAKE_CASE_					,  -1)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     lowercase__  :   Any    		=      list(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   Tuple    		=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__  :   List[Any]    		=      {"""input_ids""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_					,  tensor_type=SCREAMING_SNAKE_CASE_)








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 		UpperCamelCase	(      lowercase_ , lowercase_ = True , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = False , lowercase_ = 1_00 , lowercase_ = 0.01 , lowercase_ = 1 , )		->	Any:


  '''simple docstring'''






  lowercase__  :   Any    		=      False
  lowercase__  :   Union[str, Any]    		=      search_prob
  lowercase__  :   int    		=      start_temperate
  lowercase__  :   Union[str, Any]    		=      []
  lowercase__  :   List[str]    		=      0
  lowercase__  :   Optional[int]    		=      None

  while not search_end:
    lowercase__  :   int    		=      current_state.score()
    if best_state is None or current_score > best_state.score():
      lowercase__  :   str    		=      current_state
    scores.append(lowercase_   )
    iterations += 1
    lowercase__  :   Union[str, Any]    		=      None
    lowercase__  :   int    		=      current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      lowercase__  :   List[str]    		=      random.randint(0 , len(lowercase_   ) - 1   )  # picking a random neighbor
      lowercase__  :   Optional[int]    		=      neighbors.pop(lowercase_   )
      lowercase__  :   Dict    		=      picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        lowercase__  :   Any    		=      change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        lowercase__  :   Tuple    		=      picked_neighbor
      else:
        lowercase__  :   Union[str, Any]    		=      (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          lowercase__  :   Optional[int]    		=      picked_neighbor
    lowercase__  :   Dict    		=      current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      lowercase__  :   Optional[Any]    		=      True
    else:
      lowercase__  :   List[Any]    		=      next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowercase_   ) , lowercase_   )
    plt.xlabel("""Iterations"""   )
    plt.ylabel("""Function values"""   )
    plt.show()
  return best_state


if __name__ == "__main__":

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


       '''simple docstring'''






       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		str          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		List[Any]          =   simulated_annealing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		List[Any]          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Optional[int]          =   simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


       '''simple docstring'''






       return (3 * x**2) - (6 * y)

     lowerCamelCase__    :		Tuple          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Union[str, Any]          =   simulated_annealing(prob, find_max=False, visualization=True)
     print(
         """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )

     lowerCamelCase__    :		Dict          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Tuple          =   simulated_annealing(prob, find_max=True, visualization=True)



     print(
         """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )






