


"""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

__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

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

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

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

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


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	int    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 256}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				param_name='''crop_size'''							)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          if "shortest_edge" in size:
               _a    	=				get_resize_output_image_size(__UpperCAmelCase    ,				size['''shortest_edge''']    ,				default_to_square=__UpperCAmelCase							)
          elif "height" in size and "width" in size:
               _a    	=				(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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							str:
          _a    	=				image.astype(np.floataa							)
          if offset:
               _a    	=				image - (scale / 2)
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				)       ->							np.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_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.
          _a    	=				to_numpy_array(__UpperCAmelCase							)

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

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

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

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

          _a    	=				to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase							)
          return image





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							PIL.Image.Image:
          _a    	=				do_resize if do_resize is not None else self.do_resize
          _a    	=				resample if resample is not None else self.resample
          _a    	=				do_center_crop if do_center_crop is not None else self.do_center_crop
          _a    	=				do_rescale if do_rescale is not None else self.do_rescale
          _a    	=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _a    	=				offset if offset is not None else self.offset
          _a    	=				do_normalize if do_normalize is not None else self.do_normalize
          _a    	=				image_mean if image_mean is not None else self.image_mean
          _a    	=				image_std if image_std is not None else self.image_std

          _a    	=				size if size is not None else self.size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				crop_size if crop_size is not None else self.crop_size
          _a    	=				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.'''							)

          _a    	=				make_batched(__UpperCAmelCase							)

          _a    	=				[
              [
                  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    ,				offset=__UpperCAmelCase    ,				do_normalize=__UpperCAmelCase    ,				image_mean=__UpperCAmelCase    ,				image_std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				)
                  for img in video
              ]
              for video in videos
          ]

          _a    	=				{'''pixel_values''': videos}
          return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase							)



"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					bool = True,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					float = 1_00,     _lowerCAmelCase     :					float = 0.0_1,     _lowerCAmelCase     :					float = 1,     ):






     """simple docstring"""

     _a    	=				False
     _a    	=				search_prob
     _a    	=				start_temperate
     _a    	=				[]
     _a    	=				0
     _a    	=				None

     while not search_end:
          _a    	=				current_state.score()
          if best_state is None or current_score > best_state.score():
               _a    	=				current_state
          scores.append(_lowerCAmelCase       )
          iterations += 1
          _a    	=				None
          _a    	=				current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               _a    	=				random.randint(0,     len(_lowerCAmelCase       ) - 1       )  # picking a random neighbor
               _a    	=				neighbors.pop(_lowerCAmelCase       )
               _a    	=				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:
                    _a    	=				change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    _a    	=				picked_neighbor
               else:
                    _a    	=				(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         _a    	=				picked_neighbor
          _a    	=				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
               _a    	=				True
          else:
               _a    	=				next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(_lowerCAmelCase       ),     _lowerCAmelCase       )
          plt.xlabel('''Iterations'''       )
          plt.ylabel('''Function values'''       )
          plt.show()
     return best_state


if __name__ == "__main__":
   def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict       ):






        """simple docstring"""

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

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(
       prob, find_max=False, max_x=100, min_x=5, max_y=50, 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)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       simulated_annealing(
       prob, find_max=True, max_x=100, min_x=5, max_y=50, 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       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]       ):






        """simple docstring"""

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

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )