





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					int = 16      ,				UpperCamelCase      :					int = 88      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					int = 1      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					int = 32      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					str = "geglu"      ,				UpperCamelCase      :					Optional[int] = None      ,				):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				Dict														=	nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=UpperCamelCase      ,				attention_head_dim=UpperCamelCase      ,				in_channels=UpperCamelCase      ,				num_layers=UpperCamelCase      ,				dropout=UpperCamelCase      ,				norm_num_groups=UpperCamelCase      ,				cross_attention_dim=UpperCamelCase      ,				attention_bias=UpperCamelCase      ,				sample_size=UpperCamelCase      ,				num_vector_embeds=UpperCamelCase      ,				activation_fn=UpperCamelCase      ,				num_embeds_ada_norm=UpperCamelCase      ,				)
            for _ in range(2							)
        ]							)

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    _snake_case   :				int														=	0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    _snake_case   :				Optional[int]														=	[77, 2_57]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    _snake_case   :				Optional[Any]														=	[1, 0]







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					str=None      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					bool = True      ,				):


    '''simple docstring'''





    _snake_case   :				str														=	hidden_states

    _snake_case   :				Tuple														=	[]
    _snake_case   :				Optional[int]														=	0
    # attention_mask is not used yet
    for i in range(2							):
      # for each of the two transformers, pass the corresponding condition tokens
      _snake_case   :				List[str]														=	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      _snake_case   :				str														=	self.transformer_index_for_condition[i]
      _snake_case   :				int														=	self.transformers[transformer_index](
          UpperCamelCase      ,				encoder_hidden_states=UpperCamelCase      ,				timestep=UpperCamelCase      ,				cross_attention_kwargs=UpperCamelCase      ,				return_dict=UpperCamelCase      ,				)[0]
      encoded_states.append(encoded_state - input_states							)
      tokens_start += self.condition_lengths[i]

    _snake_case   :				str														=	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    _snake_case   :				Optional[Any]														=	output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=UpperCamelCase							)











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()}"""
      )




