


'''simple docstring'''






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   __snake_case			(  _SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



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

    def __lowercase  (  self       :    Any						)  ->    Union[str, Any]:
      torch.manual_seed(0						)
      lowerCAmelCase_     : List[str]     =  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=lowerCamelCase       ,		activation_fn="""gelu-approximate"""       ,		num_embeds_ada_norm=10_00       ,		norm_type="""ada_norm_zero"""       ,		norm_elementwise_affine=lowerCamelCase       ,		)
      lowerCAmelCase_     : Optional[Any]     =  AutoencoderKL()
      lowerCAmelCase_     : int     =  DDIMScheduler()
      lowerCAmelCase_     : Optional[Any]     =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
      return components

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Any=0						)  ->    Optional[Any]:
      if str(lowerCamelCase						).startswith("""mps"""						):
        lowerCAmelCase_     : Optional[Any]     =  torch.manual_seed(lowerCamelCase						)
      else:
        lowerCAmelCase_     : int     =  torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  {
          """class_labels""": [1],
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs

    def __lowercase  (  self       :    Dict						)  ->    int:
      lowerCAmelCase_     : Union[str, Any]     =  """cpu"""

      lowerCAmelCase_     : List[str]     =  self.get_dummy_components()
      lowerCAmelCase_     : Optional[Any]     =  self.pipeline_class(**lowerCamelCase						)
      pipe.to(lowerCamelCase						)
      pipe.set_progress_bar_config(disable=lowerCamelCase						)

      lowerCAmelCase_     : int     =  self.get_dummy_inputs(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  pipe(**lowerCamelCase						).images
      lowerCAmelCase_     : int     =  image[0, -3:, -3:, -1]

      self.assertEqual(image.shape       ,		(1, 16, 16, 3)						)
      lowerCAmelCase_     : List[str]     =  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]						)
      lowerCAmelCase_     : str     =  np.abs(image_slice.flatten() - expected_slice						).max()
      self.assertLessEqual(lowerCamelCase       ,		1E-3						)

    def __lowercase  (  self       :    Any						)  ->    Tuple:
      self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase       ,		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 __lowercase  (  self       :    Dict						)  ->    Any:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







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







    """simple docstring"""



    def __lowercase  (  self       :    List[str]						)  ->    Optional[Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    def __lowercase  (  self       :    int						)  ->    Tuple:
      lowerCAmelCase_     : Tuple     =  torch.manual_seed(0						)

      lowerCAmelCase_     : Union[str, Any]     =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
      pipe.to("""cuda"""						)

      lowerCAmelCase_     : Tuple     =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
      lowerCAmelCase_     : List[str]     =  pipe.get_label_ids(lowerCamelCase						)

      lowerCAmelCase_     : List[str]     =  pipe(lowerCamelCase       ,		generator=lowerCamelCase       ,		num_inference_steps=40       ,		output_type="""np"""						).images

      for word, image in zip(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : str     =  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 __lowercase  (  self       :    Optional[Any]						)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
      lowerCAmelCase_     : str     =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
      pipe.to("""cuda"""						)

      lowerCAmelCase_     : List[str]     =  ["""vase""", """umbrella"""]
      lowerCAmelCase_     : List[str]     =  pipe.get_label_ids(lowerCamelCase						)

      lowerCAmelCase_     : int     =  torch.manual_seed(0						)
      lowerCAmelCase_     : Dict     =  pipe(lowerCamelCase       ,		generator=lowerCamelCase       ,		num_inference_steps=25       ,		output_type="""np"""						).images

      for word, image in zip(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Any     =  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







'''simple docstring'''






from collections.abc import Callable






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Callable | None = None						)  ->    None:
      # Stores actual heap items.
      lowerCAmelCase_     : list     =  []
      # Stores indexes of each item for supporting updates and deletion.
      lowerCAmelCase_     : dict     =  {}
      # Stores current size of heap.
      lowerCAmelCase_     : List[Any]     =  0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      lowerCAmelCase_     : Tuple     =  key or (lambda lowerCamelCase						: x)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      return int((i - 1) / 2						) if i > 0 else None

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[str]     =  int(2 * i + 1						)
      return left if 0 < left < self.size else None

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[Any]     =  int(2 * i + 2						)
      return right if 0 < right < self.size else None

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  self.arr[j], self.arr[i]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    bool:
      return self.arr[i][1] < self.arr[j][1]

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self._left(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  self._right(lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  i

      if left is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : int     =  left
      if right is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  right

      return valid_parent

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Tuple     =  self._parent(lowerCamelCase						)
      while parent is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : str     =  parent, self._parent(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Optional[Any]     =  self._get_valid_parent(lowerCamelCase						)
      while valid_parent != index:
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  valid_parent, self._get_valid_parent(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : Dict     =  self.pos_map[item]
      lowerCAmelCase_     : Dict     =  [item, self.key(lowerCamelCase						)]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(lowerCamelCase						)
      self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : List[str]     =  self.pos_map[item]
      del self.pos_map[item]
      lowerCAmelCase_     : Tuple     =  self.arr[self.size - 1]
      lowerCAmelCase_     : List[str]     =  index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
        self._heapify_up(lowerCamelCase						)
        self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Any     =  len(self.arr						)
      if arr_len == self.size:
        self.arr.append([item, self.key(lowerCamelCase						)]						)
      else:
        lowerCAmelCase_     : str     =  [item, self.key(lowerCamelCase						)]
      lowerCAmelCase_     : Optional[Any]     =  self.size
      self.size += 1
      self._heapify_up(self.size - 1						)

    def __lowercase  (  self       :    str						)  ->    tuple | None:
      return self.arr[0] if self.size else None





    def __lowercase  (  self       :    Optional[Any]						)  ->    tuple | None:
      lowerCAmelCase_     : int     =  self.get_top()
      if top_item_tuple:
        self.delete_item(top_item_tuple[0]						)
      return top_item_tuple







def 							UpperCamelCase_     (       ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



