



import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   bool = True	,					snake_case__	:   float = math.inf	,					snake_case__	:   float = -math.inf	,					snake_case__	:   float = math.inf	,					snake_case__	:   float = -math.inf	,					snake_case__	:   bool = False	,					snake_case__	:   float = 100	,					snake_case__	:   float = 0.01	,					snake_case__	:   float = 1	,					)     ->					Any:
   UpperCamelCase   :  Dict						     =     False
   UpperCamelCase   :  Union[str, Any]						     =     search_prob
   UpperCamelCase   :  str						     =     start_temperate
   UpperCamelCase   :  Optional[int]						     =     []
   UpperCamelCase   :  Any						     =     0
   UpperCamelCase   :  Optional[int]						     =     None

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

   if visualization:
      from matplotlib import pyplot as plt

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


if __name__ == "__main__":
    def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   str     )     ->					Dict:
       return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    __UpperCAmelCase	       =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __UpperCAmelCase	       =	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)
    __UpperCAmelCase	       =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __UpperCAmelCase	       =	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 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Dict:
       return (3 * x**2) - (6 * y)

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

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








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

   # load LoRA weight from .safetensors
   UpperCamelCase   :  Union[str, Any]						     =     load_file(snake_case__     )

   UpperCamelCase   :  int						     =     []

   # directly update weight in diffusers model
   for key in state_dict:
      # it is suggested to print out the key, it usually will be something like below
      # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

      # as we have set the alpha beforehand, so just skip
      if ".alpha" in key or key in visited:
         continue

      if "text" in key:
         UpperCamelCase   :  Optional[Any]						     =     key.split('.'     )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'     )[-1].split('_'     )
         UpperCamelCase   :  Optional[Any]						     =     pipeline.text_encoder
      else:
         UpperCamelCase   :  Tuple						     =     key.split('.'     )[0].split(LORA_PREFIX_UNET + '_'     )[-1].split('_'     )
         UpperCamelCase   :  List[str]						     =     pipeline.unet

      # find the target layer
      UpperCamelCase   :  Optional[Any]						     =     layer_infos.pop(0     )
      while len(snake_case__     ) > -1:
         try:
            UpperCamelCase   :  Dict						     =     curr_layer.__getattr__(snake_case__     )
            if len(snake_case__     ) > 0:
               UpperCamelCase   :  Dict						     =     layer_infos.pop(0     )
            elif len(snake_case__     ) == 0:
               break
         except Exception:
            if len(snake_case__     ) > 0:
               temp_name += "_" + layer_infos.pop(0     )
            else:
               UpperCamelCase   :  Tuple						     =     layer_infos.pop(0     )

      UpperCamelCase   :  List[Any]						     =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'	,					'lora_up'     )     )
         pair_keys.append(snake_case__     )
      else:
         pair_keys.append(snake_case__     )
         pair_keys.append(key.replace('lora_up'	,					'lora_down'     )     )

      # update weight
      if len(state_dict[pair_keys[0]].shape     ) == 4:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         UpperCamelCase   :  Union[str, Any]						     =     state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     ).unsqueeze(2     ).unsqueeze(3     )
      else:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].to(torch.floataa     )
         UpperCamelCase   :  List[Any]						     =     state_dict[pair_keys[1]].to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     )

      # update visited list
      for item in pair_keys:
         visited.append(snake_case__     )

   return pipeline


if __name__ == "__main__":
    __UpperCAmelCase	       =	argparse.ArgumentParser()

    parser.add_argument(
        '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
    )
    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )
    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
    )
    parser.add_argument(
        '''--lora_prefix_text_encoder''',
        default='''lora_te''',
        type=str,
        help='''The prefix of text encoder weight in safetensors''',
    )
    parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
    parser.add_argument(
        '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
    )
    parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

    __UpperCAmelCase	       =	parser.parse_args()

    __UpperCAmelCase	       =	args.base_model_path
    __UpperCAmelCase	       =	args.checkpoint_path
    __UpperCAmelCase	       =	args.dump_path
    __UpperCAmelCase	       =	args.lora_prefix_unet
    __UpperCAmelCase	       =	args.lora_prefix_text_encoder
    __UpperCAmelCase	       =	args.alpha

    __UpperCAmelCase	       =	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

    __UpperCAmelCase	       =	pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



