


'''simple docstring'''





import math




class 							UpperCAmelCase :


 def __init__(			self       :  Tuple ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  List[str]   ):
    UpperCAmelCase__  :List[Any]            =			x
    UpperCAmelCase__  :Optional[int]            =			y
    UpperCAmelCase__  :Optional[int]            =			step_size
    UpperCAmelCase__  :List[Any]            =			function_to_optimize


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    return self.function(self.x ,					self.y   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :Tuple            =			self.step_size
    return [
        SearchProblem(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					self.function   )
        for x, y in (
            (self.x - step_size, self.y - step_size),
            (self.x - step_size, self.y),
            (self.x - step_size, self.y + step_size),
            (self.x, self.y - step_size),
            (self.x, self.y + step_size),
            (self.x + step_size, self.y - step_size),
            (self.x + step_size, self.y),
            (self.x + step_size, self.y + step_size),
        )
    ]


 def __hash__(			self       :  Dict   ):
    return hash(str(self   )   )


 def __eq__(			self       :  Dict ,					__lowerCamelCase       :  Optional[int]   ):
    if isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
       return hash(str(self   )   ) == hash(str(__lowerCamelCase   )   )
    return False





 def __str__(			self       :  List[Any]   ):
    return f'''x: {self.x} y: {self.y}'''


def 					a__    (				UpperCamelCase_	:			int,      UpperCamelCase_	:			bool = True,      UpperCamelCase_	:			float = math.inf,      UpperCamelCase_	:			float = -math.inf,      UpperCamelCase_	:			float = math.inf,      UpperCamelCase_	:			float = -math.inf,      UpperCamelCase_	:			bool = False,      UpperCamelCase_	:			int = 10_000,      ):
   UpperCAmelCase__  :List[Any]            =			search_prob
   UpperCAmelCase__  :str            =			[]  # list to store the current score at each iteration
   UpperCAmelCase__  :Union[str, Any]            =			0
   UpperCAmelCase__  :int            =			False
   UpperCAmelCase__  :List[str]            =			set()
   while not solution_found and iterations < max_iter:
      visited.add(UpperCamelCase_	)
      iterations += 1
      UpperCAmelCase__  :Optional[Any]            =			current_state.score()
      scores.append(UpperCamelCase_	)
      UpperCAmelCase__  :str            =			current_state.get_neighbors()
      UpperCAmelCase__  :int            =			-math.inf
      UpperCAmelCase__  :Any            =			math.inf
      UpperCAmelCase__  :Optional[Any]            =			None  # to hold the next best neighbor
      for neighbor in neighbors:
         if neighbor in visited:
            continue  # do not want to visit the same state again
         if (
             neighbor.x > max_x
             or neighbor.x < min_x
             or neighbor.y > max_y
             or neighbor.y < min_y
         ):
            continue  # neighbor outside our bounds
         UpperCAmelCase__  :Optional[int]            =			neighbor.score() - current_score
         if find_max:  # finding max
            # going to direction with greatest ascent
            if change > max_change and change > 0:
               UpperCAmelCase__  :List[Any]            =			change
               UpperCAmelCase__  :Dict            =			neighbor
         else:  # finding min
            # to direction with greatest descent
            if change < min_change and change < 0:
               UpperCAmelCase__  :int            =			change
               UpperCAmelCase__  :List[str]            =			neighbor
      if next_state is not None:
         # we found at least one neighbor which improved the current state
         UpperCAmelCase__  :Optional[Any]            =			next_state
      else:
         # since we have no neighbor that improves the solution we stop the search
         UpperCAmelCase__  :Union[str, Any]            =			True

   if visualization:
      from matplotlib import pyplot as plt

      plt.plot(range(UpperCamelCase_	),      UpperCamelCase_	)
      plt.xlabel('''Iterations'''	)
      plt.ylabel('''Function values'''	)
      plt.show()

   return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()


  def 					a__    (				UpperCamelCase_	:			List[Any],      UpperCamelCase_	:			List[str]	):
     return (x**2) + (y**2)

  # starting the problem with initial coordinates (3, 4)
  __lowerCamelCase       				=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  __lowerCamelCase       				=	hill_climbing(prob, find_max=False)
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      F'''{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       				=	hill_climbing(
      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()}'''
  )


  def 					a__    (				UpperCamelCase_	:			List[Any],      UpperCamelCase_	:			Optional[int]	):
     return (3 * x**2) - (6 * y)

  __lowerCamelCase       				=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  __lowerCamelCase       				=	hill_climbing(prob, find_max=True)


  print(
      '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      F'''{local_min.score()}'''
  )







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				StableDiffusionParadigmsPipeline
 UpperCAmelCase				=				TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    torch.manual_seed(0   )
    UpperCAmelCase__  :Optional[int]            =			UNetaDConditionModel(
        block_out_channels=(3_2, 6_4) ,					layers_per_block=2 ,					sample_size=3_2 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,					cross_attention_dim=3_2 ,					attention_head_dim=(2, 4) ,					use_linear_projection=__lowerCamelCase ,					)
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler(
        beta_start=0.0_00_85 ,					beta_end=0.0_12 ,					beta_schedule='''scaled_linear''' ,					clip_sample=__lowerCamelCase ,					set_alpha_to_one=__lowerCamelCase ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			AutoencoderKL(
        block_out_channels=[3_2, 6_4] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,					latent_channels=4 ,					sample_size=1_2_8 ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			CLIPTextConfig(
        bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=3_2 ,					intermediate_size=3_7 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_0_0_0 ,					hidden_act='''gelu''' ,					projection_dim=5_1_2 ,					)
    UpperCAmelCase__  :List[Any]            =			CLIPTextModel(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

    UpperCAmelCase__  :int            =			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  Any=0   ):
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       UpperCAmelCase__  :str            =			torch.manual_seed(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :Union[str, Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Dict            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    UpperCAmelCase__  :str            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Union[str, Any]            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[Any]            =			np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Any            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			DDPMParallelScheduler()
    torch.manual_seed(0   )
    UpperCAmelCase__  :List[str]            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Dict            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[str]            =			np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().test_inference_batch_consistent(batch_sizes=[1, 2]   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3e-3   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :int            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :str            =			self.get_dummy_components()
    UpperCAmelCase__  :Any            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Tuple            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			'''french fries'''
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase ,					negative_prompt=__lowerCamelCase   )
    UpperCAmelCase__  :int            =			output.images
    UpperCAmelCase__  :Tuple            =			image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :Tuple            =			np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch_gpu
class 							UpperCAmelCase ( unittest.TestCase	):


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Tuple=0   ):
    UpperCAmelCase__  :List[Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :Dict            =			'''stabilityai/stable-diffusion-2-base'''
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler.from_pretrained(__lowerCamelCase ,					subfolder='''scheduler'''   )
    UpperCAmelCase__  :Dict            =			StableDiffusionParadigmsPipeline.from_pretrained(__lowerCamelCase ,					scheduler=__lowerCamelCase ,					safety_checker=__lowerCamelCase   )
    pipe.to(__lowerCamelCase   )
    pipe.set_progress_bar_config(disable=__lowerCamelCase   )
    pipe.enable_attention_slicing()

    UpperCAmelCase__  :Dict            =			self.get_inputs()
    UpperCAmelCase__  :int            =			pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :List[str]            =			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase__  :Optional[int]            =			np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

    assert np.abs(expected_slice - image_slice   ).max() < 1e-2



