






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  DiTPipeline
      __snake_case       =  CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      __snake_case       =  PipelineTesterMixin.required_optional_params - {
          '''latents''',
          '''num_images_per_prompt''',
          '''callback''',
          '''callback_steps''',
      }
      __snake_case       =  CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Tuple:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      TransformeraDModel(
           sample_size=16 ,					num_layers=2 ,					patch_size=4 ,					attention_head_dim=8 ,					num_attention_heads=2 ,					in_channels=4 ,					out_channels=8 ,					attention_bias=__UpperCAmelCase ,					activation_fn='''gelu-approximate''' ,					num_embeds_ada_norm=1_000 ,					norm_type='''ada_norm_zero''' ,					norm_elementwise_affine=__UpperCAmelCase ,					)
       a		  =      AutoencoderKL()
       a		  =      DDIMScheduler()
       a		  =      {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
       return components







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Optional[Any]=0  )    ->Any:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''class_labels''': [1],
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->int:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()
       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       pipe.to(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      self.get_dummy_inputs(__UpperCAmelCase  )
       a		  =      pipe(**__UpperCAmelCase  ).images
       a		  =      image[0, -3:, -3:, -1]

       self.assertEqual(image.shape ,					(1, 16, 16, 3)  )
       a		  =      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
       a		  =      np.abs(image_slice.flatten() - expected_slice  ).max()
       self.assertLessEqual(__UpperCAmelCase ,					1e-3  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase ,					expected_max_diff=1e-3  )







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available() ,					reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,					)
      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )






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






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      torch.manual_seed(0  )

       a		  =      DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''  )
       pipe.to('''cuda'''  )

       a		  =      ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
       a		  =      pipe.get_label_ids(__UpperCAmelCase  )

       a		  =      pipe(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					num_inference_steps=40 ,					output_type='''np'''  ).images

       for word, image in zip(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      load_numpy(
            F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
        assert np.abs((expected_image - image).max()  ) < 1e-2







      def       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''  )
       a		  =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
       pipe.to('''cuda'''  )

       a		  =      ['''vase''', '''umbrella''']
       a		  =      pipe.get_label_ids(__UpperCAmelCase  )

       a		  =      torch.manual_seed(0  )
       a		  =      pipe(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					num_inference_steps=25 ,					output_type='''np'''  ).images

       for word, image in zip(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            F"""/dit/{word}_512.npy"""  )

        assert np.abs((expected_image - image).max()  ) < 1e-1






import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

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

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_A						)    ,					_A						)
            plt.xlabel("Iterations"						)
            plt.ylabel("Function values"						)
            plt.show()
      return best_state


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					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      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    int		=					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       lowercase_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




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

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					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      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					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()}'
    )


