


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Any      					="""pegasus"""
  a_							:       List[Any]      					=["""past_key_values"""]
  a_							:       Any      					={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







  def __init__(      self      :					Tuple      ,				UpperCamelCase      :					Optional[int]=5_02_65      ,				UpperCamelCase      :					Optional[int]=10_24      ,				UpperCamelCase      :					Any=12      ,				UpperCamelCase      :					Tuple=40_96      ,				UpperCamelCase      :					Tuple=16      ,				UpperCamelCase      :					int=12      ,				UpperCamelCase      :					Optional[int]=40_96      ,				UpperCamelCase      :					Tuple=16      ,				UpperCamelCase      :					str=0.0      ,				UpperCamelCase      :					Union[str, Any]=0.0      ,				UpperCamelCase      :					List[Any]=True      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					Dict="gelu"      ,				UpperCamelCase      :					Optional[int]=10_24      ,				UpperCamelCase      :					Optional[int]=0.1      ,				UpperCamelCase      :					str=0.0      ,				UpperCamelCase      :					Union[str, Any]=0.0      ,				UpperCamelCase      :					Dict=0.02      ,				UpperCamelCase      :					str=0      ,				UpperCamelCase      :					Any=False      ,				UpperCamelCase      :					List[str]=0      ,				UpperCamelCase      :					Optional[int]=1      ,				UpperCamelCase      :					int=1      ,				**UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    _snake_case   :				int														=	vocab_size
    _snake_case   :				Optional[int]														=	max_position_embeddings
    _snake_case   :				Dict														=	d_model
    _snake_case   :				List[str]														=	encoder_ffn_dim
    _snake_case   :				int														=	encoder_layers
    _snake_case   :				Optional[Any]														=	encoder_attention_heads
    _snake_case   :				Optional[Any]														=	decoder_ffn_dim
    _snake_case   :				Optional[Any]														=	decoder_layers
    _snake_case   :				List[str]														=	decoder_attention_heads
    _snake_case   :				str														=	dropout
    _snake_case   :				Union[str, Any]														=	attention_dropout
    _snake_case   :				List[str]														=	activation_dropout
    _snake_case   :				Optional[Any]														=	activation_function
    _snake_case   :				Dict														=	init_std
    _snake_case   :				str														=	encoder_layerdrop
    _snake_case   :				Optional[int]														=	decoder_layerdrop
    _snake_case   :				Optional[int]														=	use_cache
    _snake_case   :				Union[str, Any]														=	encoder_layers
    _snake_case   :				List[Any]														=	scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				is_encoder_decoder=UpperCamelCase      ,				decoder_start_token_id=UpperCamelCase      ,				forced_eos_token_id=UpperCamelCase      ,				**UpperCamelCase      ,				)







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    return self.encoder_attention_heads







  @property
  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    return self.d_model











import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							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    ,							)->		Any:
  _snake_case   :				int														=	False
  _snake_case   :				Any														=	search_prob
  _snake_case   :				Tuple														=	start_temperate
  _snake_case   :				Any														=	[]
  _snake_case   :				List[str]														=	0
  _snake_case   :				Optional[Any]														=	None

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




