


'''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'''
 __lowercase					:		Optional[Any]     				=    StableDiffusionLDMaDPipeline
 __lowercase					:		Union[str, Any]     				=    TEXT_TO_IMAGE_PARAMS
 __lowercase					:		List[Any]     				=    TEXT_TO_IMAGE_BATCH_PARAMS
 __lowercase					:		int     				=    TEXT_TO_IMAGE_IMAGE_PARAMS


 def 			snake_case__ (   self    ):

  """simple docstring"""







  torch.manual_seed(0    )
  __A	:		Any	  =      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	:		Union[str, Any]	  =      DDIMScheduler(
      beta_start=0.0_0_0_8_5   ,   beta_end=0.0_1_2   ,   beta_schedule='scaled_linear'   ,   clip_sample=__lowercase   ,   set_alpha_to_one=__lowercase   ,   )
  torch.manual_seed(0    )
  __A	:		Tuple	  =      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	:		Optional[int]	  =      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=1_000   ,   )
  __A	:		Tuple	  =      CLIPTextModel(__lowercase    )
  __A	:		int	  =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

  __A	:		Optional[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   ,   __lowercase   ,   __lowercase=0    ):

  """simple docstring"""







  if str(__lowercase    ).startswith('mps'    ):
   __A	:		List[Any]	  =      torch.manual_seed(__lowercase    )
  else:
   __A	:		Union[str, Any]	  =      torch.Generator(device=__lowercase    ).manual_seed(__lowercase    )
  __A	:		List[str]	  =      {
      '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    ):

  """simple docstring"""







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

  __A	:		Union[str, Any]	  =      self.get_dummy_components()
  __A	:		str	  =      StableDiffusionLDMaDPipeline(**__lowercase    )
  __A	:		Union[str, Any]	  =      ldmad_pipe.to(__lowercase    )
  ldmad_pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		List[str]	  =      self.get_dummy_inputs(__lowercase    )
  __A	:		Optional[Any]	  =      ldmad_pipe(**__lowercase    )
  __A  ,__A	:		Optional[Any]	  =      output.rgb, output.depth

  __A	:		List[str]	  =      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	:		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	:		Union[str, Any]	  =      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    ):

  """simple docstring"""







  __A	:		str	  =      self.get_dummy_components()
  __A	:		Dict	  =      StableDiffusionLDMaDPipeline(**__lowercase    )
  __A	:		Optional[Any]	  =      ldmad_pipe.to(__lowercase    )
  ldmad_pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		List[str]	  =      self.get_dummy_inputs(__lowercase    )
  __A	:		Tuple	  =      3 * [inputs['prompt']]

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

  __A	:		str	  =      self.get_dummy_inputs(__lowercase    )
  __A	:		str	  =      3 * [inputs.pop('prompt'    )]

  __A	:		List[str]	  =      ldmad_pipe.tokenizer(
      __lowercase   ,   padding='max_length'   ,   max_length=ldmad_pipe.tokenizer.model_max_length   ,   truncation=__lowercase   ,   return_tensors='pt'   ,   )
  __A	:		Any	  =      text_inputs['input_ids'].to(__lowercase    )

  __A	:		Dict	  =      ldmad_pipe.text_encoder(__lowercase    )[0]

  __A	:		Union[str, Any]	  =      prompt_embeds

  # forward
  __A	:		int	  =      ldmad_pipe(**__lowercase    )
  __A  ,__A	:		Optional[Any]	  =      output.rgb, output.depth
  __A	:		Dict	  =      rgb_slice_a[0, -3:, -3:, -1]
  __A	:		Optional[Any]	  =      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    ):

  """simple docstring"""







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

  __A	:		List[Any]	  =      self.get_dummy_inputs(__lowercase    )
  __A	:		List[Any]	  =      'french fries'
  __A	:		List[str]	  =      ldmad_pipe(**__lowercase   ,   negative_prompt=__lowercase    )

  __A  ,__A	:		Any	  =      output.rgb, output.depth
  __A	:		List[Any]	  =      rgb[0, -3:, -3:, -1]
  __A	:		List[Any]	  =      depth[0, -3:, -1]

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

  __A	:		Dict	  =      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	:		Dict	  =      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    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase="cpu"   ,   __lowercase=torch.floataa   ,   __lowercase=0    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      torch.Generator(device=__lowercase    ).manual_seed(__lowercase    )
  __A	:		str	  =      np.random.RandomState(__lowercase    ).standard_normal((1, 4, 64, 64)    )
  __A	:		Tuple	  =      torch.from_numpy(__lowercase    ).to(device=__lowercase   ,   dtype=__lowercase    )
  __A	:		int	  =      {
      '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    ):

  """simple docstring"""







  __A	:		str	  =      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'    )
  __A	:		List[str]	  =      ldmad_pipe.to(__lowercase    )
  ldmad_pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		Dict	  =      self.get_inputs(__lowercase    )
  __A	:		Optional[int]	  =      ldmad_pipe(**__lowercase    )
  __A  ,__A	:		Union[str, Any]	  =      output.rgb, output.depth
  __A	:		Optional[int]	  =      rgb[0, -3:, -3:, -1].flatten()
  __A	:		Union[str, Any]	  =      rgb[0, -3:, -1].flatten()

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

  __A	:		List[str]	  =      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	:		Union[str, Any]	  =      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    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase="cpu"   ,   __lowercase=torch.floataa   ,   __lowercase=0    ):

  """simple docstring"""







  __A	:		Dict	  =      torch.Generator(device=__lowercase    ).manual_seed(__lowercase    )
  __A	:		Any	  =      np.random.RandomState(__lowercase    ).standard_normal((1, 4, 64, 64)    )
  __A	:		List[str]	  =      torch.from_numpy(__lowercase    ).to(device=__lowercase   ,   dtype=__lowercase    )
  __A	:		Optional[int]	  =      {
      '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    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'    ).to(__lowercase    )
  ldmad_pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		Tuple	  =      self.get_inputs(__lowercase    )
  __A	:		Any	  =      ldmad_pipe(**__lowercase    )
  __A  ,__A	:		Tuple	  =      output.rgb, output.depth

  __A	:		Any	  =      0.4_9_5_5_8_6
  __A	:		Optional[Any]	  =      0.3_3_7_9_5_5_1_5
  __A	:		Optional[Any]	  =      1_1_2.4_8_5_1_8
  __A	:		Tuple	  =      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    ):

  """simple docstring"""







  __A	:		Any	  =      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'    ).to(__lowercase    )
  ldmad_pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		Dict	  =      self.get_inputs(__lowercase    )
  __A	:		List[str]	  =      ldmad_pipe(**__lowercase    )
  __A  ,__A	:		Tuple	  =      output.rgb, output.depth

  __A	:		Optional[int]	  =      0.4_1_9_4_1_2_7
  __A	:		str	  =      0.3_5_3_7_5_5_8_6
  __A	:		Dict	  =      0.5_6_3_8_5_0_2
  __A	:		int	  =      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'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






