

"""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()








"""simple docstring"""






import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		LDMTextToImagePipeline
  _UpperCAmelCase          =		TEXT_TO_IMAGE_PARAMS - {
      "negative_prompt",
      "negative_prompt_embeds",
      "cross_attention_kwargs",
      "prompt_embeds",
  }
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  _UpperCAmelCase          =		TEXT_TO_IMAGE_BATCH_PARAMS
  _UpperCAmelCase          =		False

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

      torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    UNetaDConditionModel(
          block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,cross_attention_dim=32    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    DDIMScheduler(
          beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=_A    ,set_alpha_to_one=_A    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Union[str, Any]  =    AutoencoderKL(
          block_out_channels=(32, 64)    ,in_channels=3    ,out_channels=3    ,down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')    ,up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')    ,latent_channels=4    ,)
      torch.manual_seed(0      )
      _lowerCAmelCase       :   Dict  =    CLIPTextConfig(
          bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1000    ,)
      _lowerCAmelCase       :   Tuple  =    CLIPTextModel(_A      )
      _lowerCAmelCase       :   Any  =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      _lowerCAmelCase       :   List[str]  =    {
          'unet': unet,
          'scheduler': scheduler,
          'vqvae': vae,
          'bert': text_encoder,
          'tokenizer': tokenizer,
      }
      return components

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

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   int  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   List[Any]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







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

      _lowerCAmelCase       :   Optional[int]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator

      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   str  =    LDMTextToImagePipeline(**_A      )
      pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

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

      assert image.shape == (1, 16, 16, 3)
      _lowerCAmelCase       :   Tuple  =    np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







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

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

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

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

      _lowerCAmelCase       :   Optional[Any]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   List[str]  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







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

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[Any]  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   List[Any]  =    pipe(**_A      ).images
      _lowerCAmelCase       :   Dict  =    image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 256, 256, 3)
      _lowerCAmelCase       :   str  =    np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]      )
      _lowerCAmelCase       :   Dict  =    np.abs(expected_slice - image_slice      ).max()
      assert max_diff < 1E-3







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

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

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

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

      _lowerCAmelCase       :   List[str]  =    torch.manual_seed(_A      )
      _lowerCAmelCase       :   Optional[int]  =    np.random.RandomState(_A      ).standard_normal((1, 4, 32, 32)      )
      _lowerCAmelCase       :   List[Any]  =    torch.from_numpy(_A      ).to(device=_A    ,dtype=_A      )
      _lowerCAmelCase       :   int  =    {
          'prompt': 'A painting of a squirrel eating a burger',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 50,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs







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

      _lowerCAmelCase       :   List[str]  =    LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   str  =    self.get_inputs(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    pipe(**_A      ).images[0]

      _lowerCAmelCase       :   int  =    load_numpy(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
      _lowerCAmelCase       :   List[str]  =    np.abs(expected_image - image      ).max()
      assert max_diff < 1E-3





