

"""simple docstring"""






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()
class        _lowercase					(     unittest.TestCase     ):
     _lowerCamelCase     =       StableDiffusionLDMaDPipeline
     _lowerCamelCase     =       TEXT_TO_IMAGE_PARAMS
     _lowerCamelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
     _lowerCamelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      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 ,						)
      __magic_name__     =      DDIMScheduler(
          beta_start=0.0_0_0_8_5 ,						beta_end=0.0_1_2 ,						beta_schedule='''scaled_linear''' ,						clip_sample=UpperCamelCase_ ,						set_alpha_to_one=UpperCamelCase_ ,						)
      torch.manual_seed(0       )
      __magic_name__     =      AutoencoderKL(
          block_out_channels=[32, 64] ,						in_channels=6 ,						out_channels=6 ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,						latent_channels=4 ,						)
      torch.manual_seed(0       )
      __magic_name__     =      CLIPTextConfig(
          bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-0_5 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1000 ,						)
      __magic_name__     =      CLIPTextModel(UpperCamelCase_       )
      __magic_name__     =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0       ):
      if str(UpperCamelCase_       ).startswith('''mps'''       ):
       __magic_name__     =      torch.manual_seed(UpperCamelCase_       )
      else:
       __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      {
          '''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       ):
      __magic_name__     =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      StableDiffusionLDMaDPipeline(**UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe.to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth

      __magic_name__     =      rgb[0, -3:, -3:, -1]
      __magic_name__     =      depth[0, -3:, -1]

      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)

      __magic_name__     =      np.array(
          [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]       )
      __magic_name__     =      np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]       )

      assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb       ).max() < 1E-2
      assert np.abs(image_slice_depth.flatten() - expected_slice_depth       ).max() < 1E-2







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      StableDiffusionLDMaDPipeline(**UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe.to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      3 * [inputs['''prompt''']]

      # forward
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth
      __magic_name__     =      rgb_slice_a[0, -3:, -3:, -1]
      __magic_name__     =      depth_slice_a[0, -3:, -1]

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      3 * [inputs.pop('''prompt'''       )]

      __magic_name__     =      ldmad_pipe.tokenizer(
          UpperCamelCase_ ,						padding='''max_length''' ,						max_length=ldmad_pipe.tokenizer.model_max_length ,						truncation=UpperCamelCase_ ,						return_tensors='''pt''' ,						)
      __magic_name__     =      text_inputs['''input_ids'''].to(UpperCamelCase_       )

      __magic_name__     =      ldmad_pipe.text_encoder(UpperCamelCase_       )[0]

      __magic_name__     =      prompt_embeds

      # forward
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth
      __magic_name__     =      rgb_slice_a[0, -3:, -3:, -1]
      __magic_name__     =      depth_slice_a[0, -3:, -1]

      assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()       ).max() < 1E-4
      assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()       ).max() < 1E-4







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      PNDMScheduler(skip_prk_steps=UpperCamelCase_       )
      __magic_name__     =      StableDiffusionLDMaDPipeline(**UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe.to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      '''french fries'''
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_ ,						negative_prompt=UpperCamelCase_       )

      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth
      __magic_name__     =      rgb[0, -3:, -3:, -1]
      __magic_name__     =      depth[0, -3:, -1]

      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)

      __magic_name__     =      np.array(
          [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]       )
      __magic_name__     =      np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]       )
      assert np.abs(rgb_slice.flatten() - expected_slice_rgb       ).max() < 1E-2
      assert np.abs(depth_slice.flatten() - expected_slice_depth       ).max() < 1E-2


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







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_="cpu" ,						UpperCamelCase_=torch.floataa ,						UpperCamelCase_=0       ):
      __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      np.random.RandomState(UpperCamelCase_       ).standard_normal((1, 4, 64, 64)       )
      __magic_name__     =      torch.from_numpy(UpperCamelCase_       ).to(device=UpperCamelCase_ ,						dtype=UpperCamelCase_       )
      __magic_name__     =      {
          '''prompt''': '''a photograph of an astronaut riding a horse''',
          '''latents''': latents,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''       )
      __magic_name__     =      ldmad_pipe.to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_inputs(UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth
      __magic_name__     =      rgb[0, -3:, -3:, -1].flatten()
      __magic_name__     =      rgb[0, -3:, -1].flatten()

      assert rgb.shape == (1, 512, 512, 3)
      assert depth.shape == (1, 512, 512)

      __magic_name__     =      np.array(
          [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]       )
      __magic_name__     =      np.array(
          [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]       )
      assert np.abs(rgb_slice - expected_slice_rgb       ).max() < 3E-3
      assert np.abs(depth_slice - expected_slice_depth       ).max() < 3E-3


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







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_="cpu" ,						UpperCamelCase_=torch.floataa ,						UpperCamelCase_=0       ):
      __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      np.random.RandomState(UpperCamelCase_       ).standard_normal((1, 4, 64, 64)       )
      __magic_name__     =      torch.from_numpy(UpperCamelCase_       ).to(device=UpperCamelCase_ ,						dtype=UpperCamelCase_       )
      __magic_name__     =      {
          '''prompt''': '''a photograph of an astronaut riding a horse''',
          '''latents''': latents,
          '''generator''': generator,
          '''num_inference_steps''': 50,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''       ).to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_inputs(UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth

      __magic_name__     =      0.4_9_5_5_8_6
      __magic_name__     =      0.3_3_7_9_5_5_1_5
      __magic_name__     =      1_1_2.4_8_5_1_8
      __magic_name__     =      9_8.4_8_9_7_4_6
      assert np.abs(expected_rgb_mean - rgb.mean()       ) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()       ) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()       ) < 1E-3
      assert np.abs(expected_depth_std - depth.std()       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''       ).to(UpperCamelCase_       )
      ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_inputs(UpperCamelCase_       )
      __magic_name__     =      ldmad_pipe(**UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      output.rgb, output.depth

      __magic_name__     =      0.4_1_9_4_1_2_7
      __magic_name__     =      0.3_5_3_7_5_5_8_6
      __magic_name__     =      0.5_6_3_8_5_0_2
      __magic_name__     =      0.3_4_6_8_6_1_0_3

      assert rgb.shape == (1, 512, 512, 3)
      assert depth.shape == (1, 512, 512, 1)
      assert np.abs(expected_rgb_mean - rgb.mean()       ) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()       ) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()       ) < 1E-3
      assert np.abs(expected_depth_std - depth.std()       ) < 1E-3




"""simple docstring"""






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCamelCase										=		TypeVar("T")





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (position - 1) // 2





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (2 * position) + 1





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return (2 * position) + 2
class        _lowercase					(     Generic[T]     ):







     def __init__(      self       ):
      __magic_name__     =      []
      __magic_name__     =      {}
      __magic_name__     =      0







     def __len__(      self       ):
      return self.elements







     def __repr__(      self       ):
      return str(self.heap       )







     def 	lowerCAmelCase__    (      self       ):
      # Check if the priority queue is empty
      return self.elements == 0







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)       )
      __magic_name__     =      self.elements
      self.elements += 1
      self._bubble_up(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
       self._swap_nodes(0 ,						self.elements - 1       )
      __magic_name__   ,						__magic_name__     =      self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
       __magic_name__   ,						__magic_name__     =      self.heap[0]
       self._bubble_down(UpperCamelCase_       )
      return elem







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Update the weight of the given key
      __magic_name__     =      self.position_map[elem]
      __magic_name__     =      (elem, weight)
      if position > 0:
       __magic_name__     =      get_parent_position(UpperCamelCase_       )
       __magic_name__   ,						__magic_name__     =      self.heap[parent_position]
       if parent_weight > weight:
        self._bubble_up(UpperCamelCase_       )
       else:
        self._bubble_down(UpperCamelCase_       )
      else:
       self._bubble_down(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      __magic_name__     =      self.position_map[elem]
      if curr_pos == 0:
       return None
      __magic_name__     =      get_parent_position(UpperCamelCase_       )
      __magic_name__   ,						__magic_name__     =      self.heap[curr_pos]
      __magic_name__   ,						__magic_name__     =      self.heap[parent_position]
      if parent_weight > weight:
       self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
       return self._bubble_up(UpperCamelCase_       )
      return None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      __magic_name__     =      self.position_map[elem]
      __magic_name__   ,						__magic_name__     =      self.heap[curr_pos]
      __magic_name__     =      get_child_left_position(UpperCamelCase_       )
      __magic_name__     =      get_child_right_position(UpperCamelCase_       )
      if child_left_position < self.elements and child_right_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_left_position]
       __magic_name__   ,						__magic_name__     =      self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      if child_left_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_left_position]
       if child_left_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      else:
       return None
      if child_right_position < self.elements:
       __magic_name__   ,						__magic_name__     =      self.heap[child_right_position]
       if child_right_weight < weight:
        self._swap_nodes(UpperCamelCase_ ,						UpperCamelCase_       )
        return self._bubble_down(UpperCamelCase_       )
      return None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Swap the nodes at the given positions
      __magic_name__     =      self.heap[nodea_pos][0]
      __magic_name__     =      self.heap[nodea_pos][0]
      __magic_name__   ,						__magic_name__     =      (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      __magic_name__     =      nodea_pos
      __magic_name__     =      nodea_pos


class        _lowercase					(     Generic[T]     ):







     def __init__(      self       ):
      __magic_name__     =      {}
      __magic_name__     =      0







     def __repr__(      self       ):
      return str(self.connections       )







     def __len__(      self       ):
      return self.nodes







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
       __magic_name__     =      {}
       self.nodes += 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # Add an edge between 2 nodes in the graph
      self.add_node(UpperCamelCase_       )
      self.add_node(UpperCamelCase_       )
      __magic_name__     =      weight
      __magic_name__     =      weight





def 	lowercase      (   __UpperCamelCase      ,   ) ->      tuple[dict[T, int], dict[T, T | None]]:
 __magic_name__     =      {node: maxsize for node in graph.connections}
 __magic_name__     =      {node: None for node in graph.connections}

 __magic_name__     =      MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(__UpperCamelCase      ,   __UpperCamelCase						)

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 __magic_name__     =      priority_queue.extract_min()
 __magic_name__     =      0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   __magic_name__     =      dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(__UpperCamelCase      ,   dist[neighbour]						)
   __magic_name__     =      node

    # running prim's algorithm
 while not priority_queue.is_empty():
  __magic_name__     =      priority_queue.extract_min()
  for neighbour in graph.connections[node]:
   if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
    __magic_name__     =      dist[node] + graph.connections[node][neighbour]
    priority_queue.update_key(__UpperCamelCase      ,   dist[neighbour]						)
    __magic_name__     =      node
 return dist, parent

