
"""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	):
     __a						=					StableDiffusionLDMaDPipeline
     __a						=					TEXT_TO_IMAGE_PARAMS
     __a						=					TEXT_TO_IMAGE_BATCH_PARAMS
     __a						=					TEXT_TO_IMAGE_IMAGE_PARAMS







     def      lowercase	(							self			:			Dict			):
       torch.manual_seed(0			)
       _snake_case					     =       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    ,      )
       _snake_case					     =       DDIMScheduler(
           beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=_lowerCamelCase    ,      set_alpha_to_one=_lowerCamelCase    ,      )
       torch.manual_seed(0			)
       _snake_case					     =       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			)
       _snake_case					     =       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    ,      )
       _snake_case					     =       CLIPTextModel(_lowerCamelCase			)
       _snake_case					     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Any=0			):
       if str(_lowerCamelCase			).startswith('''mps'''			):
         _snake_case					     =       torch.manual_seed(_lowerCamelCase			)
       else:
         _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _snake_case					     =       {
           '''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      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

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

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

       _snake_case					     =       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]			)
       _snake_case					     =       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      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       3 * [inputs['''prompt''']]

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

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       3 * [inputs.pop('''prompt'''			)]

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

       _snake_case					     =       ldmad_pipe.text_encoder(_lowerCamelCase			)[0]

       _snake_case					     =       prompt_embeds

       # forward
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth
       _snake_case					     =       rgb_slice_a[0, -3:, -3:, -1]
       _snake_case					     =       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      lowercase	(							self			:			Any			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       PNDMScheduler(skip_prk_steps=_lowerCamelCase			)
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       '''french fries'''
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase    ,      negative_prompt=_lowerCamelCase			)

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

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

       _snake_case					     =       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]			)
       _snake_case					     =       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	):







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







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[str]="cpu"    ,      _lowerCamelCase			:			int=torch.floataa    ,      _lowerCamelCase			:			int=0			):
       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _snake_case					     =       np.random.RandomState(_lowerCamelCase			).standard_normal((1, 4, 64, 64)			)
       _snake_case					     =       torch.from_numpy(_lowerCamelCase			).to(device=_lowerCamelCase    ,      dtype=_lowerCamelCase			)
       _snake_case					     =       {
           '''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      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

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

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

       _snake_case					     =       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]			)
       _snake_case					     =       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	):







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







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			List[str]="cpu"    ,      _lowerCamelCase			:			int=torch.floataa    ,      _lowerCamelCase			:			List[Any]=0			):
       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _snake_case					     =       np.random.RandomState(_lowerCamelCase			).standard_normal((1, 4, 64, 64)			)
       _snake_case					     =       torch.from_numpy(_lowerCamelCase			).to(device=_lowerCamelCase    ,      dtype=_lowerCamelCase			)
       _snake_case					     =       {
           '''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      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			).to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

       _snake_case					     =       0.4_9_5_5_8_6
       _snake_case					     =       0.3_3_7_9_5_5_1_5
       _snake_case					     =       1_1_2.4_8_5_1_8
       _snake_case					     =       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      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''			).to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

       _snake_case					     =       0.4_1_9_4_1_2_7
       _snake_case					     =       0.3_5_3_7_5_5_8_6
       _snake_case					     =       0.5_6_3_8_5_0_2
       _snake_case					     =       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"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       list ,					__lowerCamelCase   :       int = 0		)     ->					list:
  _snake_case					     =       length or len(__lowerCamelCase		)
  _snake_case					     =       False
  for i in range(length - 1		):
    if list_data[i] > list_data[i + 1]:
      _snake_case     ,					_snake_case					     =       list_data[i + 1], list_data[i]
      _snake_case					     =       True

  return list_data if not swapped else bubble_sort(__lowerCamelCase ,					length - 1		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



