






'''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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[Any]												=StableDiffusionLDMaDPipeline
  a_       :str												=TEXT_TO_IMAGE_PARAMS
  a_       :List[str]												=TEXT_TO_IMAGE_BATCH_PARAMS
  a_       :Any												=TEXT_TO_IMAGE_IMAGE_PARAMS

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       torch.manual_seed(0						)
       __a       =   UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)				,							layers_per_block=2				,							sample_size=3_2				,							in_channels=4				,							out_channels=4				,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,							cross_attention_dim=3_2				,							)
       __a       =   DDIMScheduler(
           beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule="""scaled_linear"""				,							clip_sample=SCREAMING_SNAKE_CASE__				,							set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							)
       torch.manual_seed(0						)
       __a       =   AutoencoderKL(
           block_out_channels=[3_2, 6_4]				,							in_channels=6				,							out_channels=6				,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,							latent_channels=4				,							)
       torch.manual_seed(0						)
       __a       =   CLIPTextConfig(
           bos_token_id=0				,							eos_token_id=2				,							hidden_size=3_2				,							intermediate_size=3_7				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_0_0_0				,							)
       __a       =   CLIPTextModel(SCREAMING_SNAKE_CASE__						)
       __a       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

       __a       =   {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       return components

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0						):





       '''simple docstring'''

       if str(SCREAMING_SNAKE_CASE__						).startswith("""mps"""						):
            __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   {
           """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 __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator

       __a       =   self.get_dummy_components()
       __a       =   StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe.to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe(**SCREAMING_SNAKE_CASE__						)
       __a ,						__a       =   output.rgb, output.depth

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

       assert rgb.shape == (1, 6_4, 6_4, 3)
       assert depth.shape == (1, 6_4, 6_4)

       __a       =   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]						)
       __a       =   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 __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.get_dummy_components()
       __a       =   StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe.to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   3 * [inputs["""prompt"""]]

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

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   3 * [inputs.pop("""prompt"""						)]

       __a       =   ldmad_pipe.tokenizer(
           SCREAMING_SNAKE_CASE__				,							padding="""max_length"""				,							max_length=ldmad_pipe.tokenizer.model_max_length				,							truncation=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""				,							)
       __a       =   text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE__						)

       __a       =   ldmad_pipe.text_encoder(SCREAMING_SNAKE_CASE__						)[0]

       __a       =   prompt_embeds

       # forward
       __a       =   ldmad_pipe(**SCREAMING_SNAKE_CASE__						)
       __a ,						__a       =   output.rgb, output.depth
       __a       =   rgb_slice_a[0, -3:, -3:, -1]
       __a       =   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 __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components()
       __a       =   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__						)
       __a       =   StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe.to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   """french fries"""
       __a       =   ldmad_pipe(**SCREAMING_SNAKE_CASE__				,							negative_prompt=SCREAMING_SNAKE_CASE__						)

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

       assert rgb.shape == (1, 6_4, 6_4, 3)
       assert depth.shape == (1, 6_4, 6_4)

       __a       =   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]						)
       __a       =   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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

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

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]="cpu"				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							Dict=0						):





       '''simple docstring'''

       __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   np.random.RandomState(SCREAMING_SNAKE_CASE__						).standard_normal((1, 4, 6_4, 6_4)						)
       __a       =   torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)
       __a       =   {
           """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 __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""						)
       __a       =   ldmad_pipe.to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

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

       assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
       assert depth.shape == (1, 5_1_2, 5_1_2)

       __a       =   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]						)
       __a       =   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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							int						):





       '''simple docstring'''

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

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Any="cpu"				,							SCREAMING_SNAKE_CASE__     :							int=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0						):





       '''simple docstring'''

       __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   np.random.RandomState(SCREAMING_SNAKE_CASE__						).standard_normal((1, 4, 6_4, 6_4)						)
       __a       =   torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)
       __a       =   {
           """prompt""": """a photograph of an astronaut riding a horse""",
           """latents""": latents,
           """generator""": generator,
           """num_inference_steps""": 5_0,
           """guidance_scale""": 7.5,
           """output_type""": """numpy""",
       }
       return inputs

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""						).to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe(**SCREAMING_SNAKE_CASE__						)
       __a ,						__a       =   output.rgb, output.depth

       __a       =   0.4_9_5_5_8_6
       __a       =   0.3_3_7_9_5_5_1_5
       __a       =   1_1_2.4_8_5_1_8
       __a       =   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 __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""						).to(SCREAMING_SNAKE_CASE__						)
       ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   ldmad_pipe(**SCREAMING_SNAKE_CASE__						)
       __a ,						__a       =   output.rgb, output.depth

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

       assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
       assert depth.shape == (1, 5_1_2, 5_1_2, 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 ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												=["""image_processor""", """feature_extractor"""]
  a_       :str												="""TvltImageProcessor"""
  a_       :str												="""TvltFeatureExtractor"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(image_processor=SCREAMING_SNAKE_CASE__				,							feature_extractor=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor
       __a       =   feature_extractor

  def __call__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=False				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]				,							):





       '''simple docstring'''

       if images is None and audio is None:
            raise ValueError("""You need to specify either an `images` or `audio` input to process."""						)

       __a       =   None
       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							mask_pixel=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if images_mixed is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							is_mixed=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if audio is not None:
            __a       =   self.feature_extractor(
                SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							mask_audio=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   {}
       if audio is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images_mixed_dict is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       return output_dict


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.image_processor.model_input_names
       __a       =   self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)






