

"""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 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DiTPipeline
  _UpperCAmelCase          =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "latents",
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  _UpperCAmelCase          =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      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=_A    ,activation_fn='gelu-approximate'    ,num_embeds_ada_norm=1000    ,norm_type='ada_norm_zero'    ,norm_elementwise_affine=_A    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    AutoencoderKL()
      _lowerCAmelCase       :   Union[str, Any]  =    DDIMScheduler()
      _lowerCAmelCase       :   Tuple  =    {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    'cpu'

      _lowerCAmelCase       :   Optional[int]  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    self.pipeline_class(**_A      )
      pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Tuple  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   Dict  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Optional[int]  =    image[0, -3:, -3:, -1]

      self.assertEqual(image.shape    ,(1, 16, 16, 3)      )
      _lowerCAmelCase       :   List[str]  =    np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]      )
      _lowerCAmelCase       :   int  =    np.abs(image_slice.flatten() - expected_slice      ).max()
      self.assertLessEqual(_A    ,1E-3      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self._test_inference_batch_single_identical(relax_max_difference=_A    ,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      ):
      '''simple docstring'''

      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    torch.manual_seed(0      )

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

      _lowerCAmelCase       :   List[Any]  =    ['vase', 'umbrella', 'white shark', 'white wolf']
      _lowerCAmelCase       :   str  =    pipe.get_label_ids(_A      )

      _lowerCAmelCase       :   Dict  =    pipe(_A    ,generator=_A    ,num_inference_steps=40    ,output_type='np'      ).images

      for word, image in zip(_A    ,_A      ):
          _lowerCAmelCase       :   Any  =    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      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'      )
      _lowerCAmelCase       :   List[Any]  =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
      pipe.to('cuda'      )

      _lowerCAmelCase       :   str  =    ['vase', 'umbrella']
      _lowerCAmelCase       :   Any  =    pipe.get_label_ids(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   List[str]  =    pipe(_A    ,generator=_A    ,num_inference_steps=25    ,output_type='np'      ).images

      for word, image in zip(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    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 	__UpperCamelCase    :

  def __init__(		self    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   list  =    []
      # Stores indexes of each item for supporting updates and deletion.
      _lowerCAmelCase       :   dict  =    {}
      # Stores current size of heap.
      _lowerCAmelCase       :   Union[str, Any]  =    0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      _lowerCAmelCase       :   Union[str, Any]  =    key or (lambda _A      : x)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return int((i - 1) / 2      ) if i > 0 else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    int(2 * i + 1      )
      return left if 0 < left < self.size else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    int(2 * i + 2      )
      return right if 0 < right < self.size else None

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.arr[j], self.arr[i]

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return self.arr[i][1] < self.arr[j][1]

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._left(_A      )
      _lowerCAmelCase       :   str  =    self._right(_A      )
      _lowerCAmelCase       :   Tuple  =    i

      if left is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    left
      if right is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    right

      return valid_parent

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self._parent(_A      )
      while parent is not None and not self._cmp(_A    ,_A      ):
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    parent, self._parent(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self._get_valid_parent(_A      )
      while valid_parent != index:
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    valid_parent, self._get_valid_parent(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   int  =    self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    [item, self.key(_A      )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_A      )
      self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   List[str]  =    self.pos_map[item]
      del self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    self.arr[self.size - 1]
      _lowerCAmelCase       :   Optional[Any]  =    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(_A      )
          self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    len(self.arr      )
      if arr_len == self.size:
          self.arr.append([item, self.key(_A      )]      )
      else:
          _lowerCAmelCase       :   Any  =    [item, self.key(_A      )]
      _lowerCAmelCase       :   str  =    self.size
      self.size += 1
      self._heapify_up(self.size - 1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.arr[0] if self.size else None







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_top()
      if top_item_tuple:
          self.delete_item(top_item_tuple[0]      )
      return top_item_tuple





def 		lowerCamelCase__		(					):

    '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





