



'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''image_processor''', '''tokenizer''']
  __lowercase							:				Dict           =				'''LayoutLMv2ImageProcessor'''
  __lowercase							:				int           =				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		str:
      if "feature_extractor" in kwargs:
          warnings.warn(
              """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
              """ instead."""  ,__UpperCAmelCase  ,)
          lowerCAmelCase__			:  List[Any]											= kwargs.pop("""feature_extractor"""				)

      lowerCAmelCase__			:  Tuple											= image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
          raise ValueError("""You need to specify an `image_processor`."""				)
      if tokenizer is None:
          raise ValueError("""You need to specify a `tokenizer`."""				)

      super().__init__(__UpperCAmelCase  ,__UpperCAmelCase				)




  def __call__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = True  ,__UpperCAmelCase = False  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = True  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)   ->		BatchEncoding:
      # verify input
      if self.image_processor.apply_ocr and (boxes is not None):
          raise ValueError(
              """You cannot provide bounding boxes """
              """if you initialized the image processor with apply_ocr set to True."""				)

      if self.image_processor.apply_ocr and (word_labels is not None):
          raise ValueError(
              """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
          raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""				)

      # first, apply the image processor
      lowerCAmelCase__			:  Optional[Any]											= self.image_processor(images=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase				)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
          if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
              lowerCAmelCase__			:  Any											= [text]  # add batch dimension (as the image processor always adds a batch dimension)
          lowerCAmelCase__			:  Optional[Any]											= features["""words"""]

      lowerCAmelCase__			:  List[Any]											= self.tokenizer(
          text=text if text is not None else features["""words"""]  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features["""boxes"""]  ,word_labels=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,max_length=__UpperCAmelCase  ,stride=__UpperCAmelCase  ,pad_to_multiple_of=__UpperCAmelCase  ,return_token_type_ids=__UpperCAmelCase  ,return_attention_mask=__UpperCAmelCase  ,return_overflowing_tokens=__UpperCAmelCase  ,return_special_tokens_mask=__UpperCAmelCase  ,return_offsets_mapping=__UpperCAmelCase  ,return_length=__UpperCAmelCase  ,verbose=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase  ,**__UpperCAmelCase  ,)

      # add pixel values
      lowerCAmelCase__			:  Optional[Any]											= features.pop("""pixel_values"""				)
      if return_overflowing_tokens is True:
          lowerCAmelCase__			:  List[Any]											= self.get_overflowing_images(__UpperCAmelCase  ,encoded_inputs["""overflow_to_sample_mapping"""]				)
      lowerCAmelCase__			:  Any											= images

      return encoded_inputs




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      lowerCAmelCase__			:  List[str]											= []
      for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]				)

      if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
          raise ValueError(
              """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
              F""" {len(__UpperCAmelCase				)} and {len(__UpperCAmelCase				)}"""				)

      return images_with_overflow




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Union[str, Any]:
      return self.tokenizer.batch_decode(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      return self.tokenizer.decode(*__UpperCAmelCase  ,**__UpperCAmelCase				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return ["input_ids", "bbox", "attention_mask", "image"]




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,__UpperCAmelCase  ,)
      return self.image_processor_class






  @property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,__UpperCAmelCase  ,)
      return self.image_processor







'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = True							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = False							,   UpperCamelCase = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

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

    if visualization:
        from matplotlib import pyplot as plt

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


if __name__ == "__main__":
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





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

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     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)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





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

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     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										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     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()}"""
 )


