


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"""
       snake_case      =    StableDiffusionLDMaDPipeline
       snake_case      =    TEXT_TO_IMAGE_PARAMS
       snake_case      =    TEXT_TO_IMAGE_BATCH_PARAMS
       snake_case      =    TEXT_TO_IMAGE_IMAGE_PARAMS



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				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				,       )
         A_      :				int		     =					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_      :				Optional[Any]		     =					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		)
         A_      :				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				,       )
         A_      :				Any		     =					CLIPTextModel(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

         A_      :				Union[str, Any]		     =					{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Dict:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Optional[int]		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Any		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					{
             '''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        _snake_case       (      self		)->Any:




         '''simple docstring'''





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

         A_      :				int		     =					self.get_dummy_components()
         A_      :				Dict		     =					StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				Union[str, Any]		     =					output.rgb, output.depth

         A_      :				str		     =					rgb[0, -3:, -3:, -1]
         A_      :				Tuple		     =					depth[0, -3:, -1]

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

         A_      :				List[str]		     =					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_      :				Tuple		     =					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        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					self.get_dummy_components()
         A_      :				Any		     =					StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					3 * [inputs['''prompt''']]

         # forward
         A_      :				Any		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				Any		     =					output.rgb, output.depth
         A_      :				int		     =					rgb_slice_a[0, -3:, -3:, -1]
         A_      :				List[str]		     =					depth_slice_a[0, -3:, -1]

         A_      :				Union[str, Any]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					3 * [inputs.pop('''prompt'''		)]

         A_      :				Optional[Any]		     =					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_      :				Tuple		     =					text_inputs['''input_ids'''].to(_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					ldmad_pipe.text_encoder(_SCREAMING_SNAKE_CASE		)[0]

         A_      :				int		     =					prompt_embeds

         # forward
         A_      :				str		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				Optional[int]		     =					output.rgb, output.depth
         A_      :				List[str]		     =					rgb_slice_a[0, -3:, -3:, -1]
         A_      :				Dict		     =					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        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				int		     =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         A_      :				List[Any]		     =					self.get_dummy_components()
         A_      :				Tuple		     =					PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					'''french fries'''
         A_      :				Optional[Any]		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE				,       negative_prompt=_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				int		     =					output.rgb, output.depth
         A_      :				Tuple		     =					rgb[0, -3:, -3:, -1]
         A_      :				int		     =					depth[0, -3:, -1]

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

         A_      :				Any		     =					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_      :				Any		     =					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        _snake_case       (      self		)->Dict:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE="cpu"				,       _SCREAMING_SNAKE_CASE=torch.floataa				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Any		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
         A_      :				Dict		     =					torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					{
             '''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        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''		)
         A_      :				int		     =					ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				List[str]		     =					self.get_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				List[Any]		     =					output.rgb, output.depth
         A_      :				List[str]		     =					rgb[0, -3:, -3:, -1].flatten()
         A_      :				Any		     =					rgb[0, -3:, -1].flatten()

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

         A_      :				int		     =					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_      :				Optional[int]		     =					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        _snake_case       (      self		)->Any:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE="cpu"				,       _SCREAMING_SNAKE_CASE=torch.floataa				,       _SCREAMING_SNAKE_CASE=0		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Tuple		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
         A_      :				Optional[Any]		     =					torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					{
             '''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        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				Any		     =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''		).to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					self.get_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				Optional[Any]		     =					output.rgb, output.depth

         A_      :				int		     =					0.4_9_5_5_8_6
         A_      :				Optional[int]		     =					0.3_3_7_9_5_5_1_5
         A_      :				str		     =					1_1_2.4_8_5_1_8
         A_      :				Optional[int]		     =					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        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				str		     =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''		).to(_SCREAMING_SNAKE_CASE		)
         ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					self.get_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
         A_     ,       A_      :				List[Any]		     =					output.rgb, output.depth

         A_      :				List[str]		     =					0.4_1_9_4_1_2_7
         A_      :				Dict		     =					0.3_5_3_7_5_5_8_6
         A_      :				Dict		     =					0.5_6_3_8_5_0_2
         A_      :				Any		     =					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








import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse(SCREAMING_SNAKE_CASE       ):
      raise ValueError(
          f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
          f''' version {__version__} is >= {version_name}'''       )

    A_      :				List[Any]		     =					None
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE       ),)
      A_      :				Optional[Any]		     =					f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
    elif hasattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      values += (getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ),)
      A_      :				int		     =					f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
    elif deprecated_kwargs is None:
      A_      :				List[Any]		     =					f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and len(SCREAMING_SNAKE_CASE       ) > 0:
    A_      :				Dict		     =					inspect.getouterframes(inspect.currentframe()       )[1]
    A_      :				Optional[int]		     =					call_frame.filename
    A_      :				Optional[int]		     =					call_frame.lineno
    A_      :				str		     =					call_frame.function
    A_     ,       A_      :				List[str]		     =					next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''       )

  if len(SCREAMING_SNAKE_CASE       ) == 0:
    return
  elif len(SCREAMING_SNAKE_CASE       ) == 1:
    return values[0]
  return values




