
'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase__	(     lowerCamelCase_   ,  lowerCamelCase_   ,  unittest.TestCase):
 UpperCAmelCase__     :     Optional[Any]    					=       StableDiffusionParadigmsPipeline
 UpperCAmelCase__     :     Union[str, Any]    					=       TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase__     :     Optional[int]    					=       TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase__     :     List[Any]    					=       TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase__     :     Optional[Any]    					=       TEXT_TO_IMAGE_IMAGE_PARAMS






 def 						lowercase_    (      self	:int      )					->      Tuple:







       '''simple docstring'''



       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						,					attention_head_dim=(2, 4)						,					use_linear_projection=lowerCAmelCase_						,					)
       __A													=      DDIMParallelScheduler(
           beta_start=0.00_085						,					beta_end=0.012						,					beta_schedule='scaled_linear'						,					clip_sample=lowerCAmelCase_						,					set_alpha_to_one=lowerCAmelCase_						,					)
       torch.manual_seed(0      )
       __A													=      AutoencoderKL(
           block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,					latent_channels=4						,					sample_size=128						,					)
       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=1_000						,					hidden_act='gelu'						,					projection_dim=512						,					)
       __A													=      CLIPTextModel(lowerCAmelCase_      )
       __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 						lowercase_    (      self	:Dict						,					_A	:Tuple						,					_A	:Tuple=0      )					->      Union[str, Any]:







       '''simple docstring'''



       if str(lowerCAmelCase_      ).startswith('mps'      ):
             __A													=      torch.manual_seed(lowerCAmelCase_      )
       else:
             __A													=      torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )
       __A													=      {
           'prompt': 'a photograph of an astronaut riding a horse',
           'generator': generator,
           'num_inference_steps': 10,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'parallel': 3,
           'debug': True,
       }
       return inputs






 def 						lowercase_    (      self	:List[Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      StableDiffusionParadigmsPipeline(**lowerCAmelCase_      )
       __A													=      sd_pipe.to(lowerCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

       __A													=      self.get_dummy_inputs(lowerCAmelCase_      )
       __A													=      sd_pipe(**lowerCAmelCase_      ).images
       __A													=      image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       __A													=      np.array([0.4_773, 0.5_417, 0.4_723, 0.4_925, 0.5_631, 0.4_752, 0.5_240, 0.4_935, 0.5_023]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2






 def 						lowercase_    (      self	:Optional[int]      )					->      Tuple:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       torch.manual_seed(0      )
       __A													=      DDPMParallelScheduler()
       torch.manual_seed(0      )
       __A													=      StableDiffusionParadigmsPipeline(**lowerCAmelCase_      )
       __A													=      sd_pipe.to(lowerCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

       __A													=      self.get_dummy_inputs(lowerCAmelCase_      )
       __A													=      sd_pipe(**lowerCAmelCase_      ).images
       __A													=      image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       __A													=      np.array([0.3_573, 0.4_420, 0.4_960, 0.4_799, 0.3_796, 0.3_879, 0.4_819, 0.4_365, 0.4_468]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2






 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       super().test_inference_batch_consistent(batch_sizes=[1, 2]      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      int:







       '''simple docstring'''



       super().test_inference_batch_single_identical(batch_size=2						,					expected_max_diff=3E-3      )






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      StableDiffusionParadigmsPipeline(**lowerCAmelCase_      )
       __A													=      sd_pipe.to(lowerCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

       __A													=      self.get_dummy_inputs(lowerCAmelCase_      )
       __A													=      'french fries'
       __A													=      sd_pipe(**lowerCAmelCase_						,					negative_prompt=lowerCAmelCase_      )
       __A													=      output.images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       __A													=      np.array([0.4_771, 0.5_420, 0.4_683, 0.4_918, 0.5_636, 0.4_725, 0.5_230, 0.4_923, 0.5_015]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2


@slow
@require_torch_gpu
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Optional[int]      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:List[str]						,					_A	:Union[str, Any]=0      )					->      Any:







       '''simple docstring'''



       __A													=      torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )
       __A													=      {
           'prompt': 'a photograph of an astronaut riding a horse',
           'generator': generator,
           'num_inference_steps': 10,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
           'parallel': 3,
           'debug': True,
       }
       return inputs






 def 						lowercase_    (      self	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      'stabilityai/stable-diffusion-2-base'
       __A													=      DDIMParallelScheduler.from_pretrained(lowerCAmelCase_						,					subfolder='scheduler'      )
       __A													=      StableDiffusionParadigmsPipeline.from_pretrained(lowerCAmelCase_						,					scheduler=lowerCAmelCase_						,					safety_checker=lowerCAmelCase_      )
       pipe.to(lowerCAmelCase_      )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_      )
       pipe.enable_attention_slicing()

       __A													=      self.get_inputs()
       __A													=      pipe(**lowerCAmelCase_      ).images
       __A													=      image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       __A													=      np.array([0.9_622, 0.9_602, 0.9_748, 0.9_591, 0.9_630, 0.9_691, 0.9_661, 0.9_631, 0.9_741]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-2







def 	__lowerCamelCase							(				)      ->       Dict:
	_lowercase   =1

	while True:
		if (
		    sorted(str(__a		)		)
		    == sorted(str(2 * i		)		)
		    == sorted(str(3 * i		)		)
		    == sorted(str(4 * i		)		)
		    == sorted(str(5 * i		)		)
		    == sorted(str(6 * i		)		)
		):
			return i

		i += 1


if __name__ == "__main__":
						print(solution())




