





"""simple docstring"""





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = True	,  _SCREAMING_SNAKE_CASE = math.inf	,  _SCREAMING_SNAKE_CASE = -math.inf	,  _SCREAMING_SNAKE_CASE = math.inf	,  _SCREAMING_SNAKE_CASE = -math.inf	,  _SCREAMING_SNAKE_CASE = False	,  _SCREAMING_SNAKE_CASE = 100	,  _SCREAMING_SNAKE_CASE = 0.0_1	,  _SCREAMING_SNAKE_CASE = 1	,  )   ->Any:





  """simple docstring"""


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

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

  if visualization:
    from matplotlib import pyplot as plt

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


if __name__ == "__main__":
  def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





    """simple docstring"""


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

  # starting the problem with initial coordinates (12, 47)
  __A											=						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  __A											=						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)
  __A											=						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  __A											=						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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Dict:





    """simple docstring"""


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

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

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







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			super().__init__(*__lowerCamelCase					,      **__lowerCamelCase   )
			self.check_model_type(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

			if self.framework == "pt":
					__lowercase											=     model_outputs.logits.sigmoid()[0]
					__lowercase						,      __lowercase											=     probs.topk(__lowerCamelCase   )
			else:
					raise ValueError(F"Unsupported framework: {self.framework}"   )

			__lowercase											=     scores.tolist()
			__lowercase											=     ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase					,      __lowerCamelCase   )]




