





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 A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = StableDiffusionLDMaDPipeline
   lowerCAmelCase__        = TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase__        = TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase__        = TEXT_TO_IMAGE_IMAGE_PARAMS




   def _lowerCAmelCase      (self	:Tuple )->					Union[str, Any]:
         torch.manual_seed(0 )
         __A          =				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          =				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 )
         __A          =				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          =				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          =				CLIPTextModel(_UpperCamelCase )
         __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 _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Tuple=0 )->					str:
         if str(_UpperCamelCase ).startswith('''mps''' ):
               __A          =				torch.manual_seed(_UpperCamelCase )
         else:
               __A          =				torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
         __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 _lowerCAmelCase      (self	:Union[str, Any] )->					List[str]:
         __A          =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __A          =				self.get_dummy_components()
         __A          =				StableDiffusionLDMaDPipeline(**_UpperCamelCase )
         __A          =				ldmad_pipe.to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				ldmad_pipe(**_UpperCamelCase )
         __A   ,						__A          =				output.rgb, output.depth

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

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

         __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 _lowerCAmelCase      (self	:List[Any] )->					Tuple:
         __A          =				self.get_dummy_components()
         __A          =				StableDiffusionLDMaDPipeline(**_UpperCamelCase )
         __A          =				ldmad_pipe.to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				3 * [inputs['''prompt''']]

         # forward
         __A          =				ldmad_pipe(**_UpperCamelCase )
         __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(_UpperCamelCase )
         __A          =				3 * [inputs.pop('''prompt''' )]

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

         __A          =				ldmad_pipe.text_encoder(_UpperCamelCase )[0]

         __A          =				prompt_embeds

         # forward
         __A          =				ldmad_pipe(**_UpperCamelCase )
         __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 _lowerCAmelCase      (self	:int )->					List[str]:
         __A          =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __A          =				self.get_dummy_components()
         __A          =				PNDMScheduler(skip_prk_steps=_UpperCamelCase )
         __A          =				StableDiffusionLDMaDPipeline(**_UpperCamelCase )
         __A          =				ldmad_pipe.to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				'''french fries'''
         __A          =				ldmad_pipe(**_UpperCamelCase    ,   negative_prompt=_UpperCamelCase )

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

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

         __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 A_					(			unittest.TestCase   ):




   def _lowerCAmelCase      (self	:str )->					Tuple:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Optional[Any]="cpu"    ,   _UpperCamelCase	:List[str]=torch.floataa    ,   _UpperCamelCase	:str=0 )->					List[str]:
         __A          =				torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
         __A          =				np.random.RandomState(_UpperCamelCase ).standard_normal((1, 4, 64, 64) )
         __A          =				torch.from_numpy(_UpperCamelCase ).to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )
         __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 _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
         __A          =				ldmad_pipe.to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

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

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

         __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 A_					(			unittest.TestCase   ):




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[int]="cpu"    ,   _UpperCamelCase	:Dict=torch.floataa    ,   _UpperCamelCase	:List[str]=0 )->					Tuple:
         __A          =				torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
         __A          =				np.random.RandomState(_UpperCamelCase ).standard_normal((1, 4, 64, 64) )
         __A          =				torch.from_numpy(_UpperCamelCase ).to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )
         __A          =				{
             '''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	:Optional[Any] )->					int:
         __A          =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_inputs(_UpperCamelCase )
         __A          =				ldmad_pipe(**_UpperCamelCase )
         __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 _lowerCAmelCase      (self	:str )->					Dict:
         __A          =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(_UpperCamelCase )
         ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_inputs(_UpperCamelCase )
         __A          =				ldmad_pipe(**_UpperCamelCase )
         __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, 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












from itertools import permutations



def _a				(	lowerCamelCase:   tuple )	->						bool:







      '''simple docstring'''






      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      __A          =				[7, 11, 13, 17]
      for i, test in enumerate(lowerCamelCase ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






      return sum(
          int(''''''.join(map(lowerCamelCase					,				lowerCamelCase ) ) )
          for num in permutations(range(lowerCamelCase ) )
          if is_substring_divisible(lowerCamelCase ) )


if __name__ == "__main__":
       print(f'{solution() = }')





