






"""simple docstring"""







def 					_lowerCAmelCase				(					lowerCAmelCase						):







       '''simple docstring'''
       return " ".join([word[::-1] for word in input_str.split()]						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""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_       (				a_      ,    a_      ,    unittest.TestCase     ):
  _A				:    str =		StableDiffusionParadigmsPipeline
  _A				:    Any =		TEXT_TO_IMAGE_PARAMS
  _A				:    List[Any] =		TEXT_TO_IMAGE_BATCH_PARAMS
  _A				:    str =		TEXT_TO_IMAGE_IMAGE_PARAMS
  _A				:    Tuple =		TEXT_TO_IMAGE_IMAGE_PARAMS



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         torch.manual_seed(0 )
         UpperCAmelCase					=	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=snake_case__       ,						)
         UpperCAmelCase					=	DDIMParallelScheduler(
             beta_start=0.00_085       ,						beta_end=0.012       ,						beta_schedule="""scaled_linear"""       ,						clip_sample=snake_case__       ,						set_alpha_to_one=snake_case__       ,						)
         torch.manual_seed(0 )
         UpperCAmelCase					=	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=1_28       ,						)
         torch.manual_seed(0 )
         UpperCAmelCase					=	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=10_00       ,						hidden_act="""gelu"""       ,						projection_dim=5_12       ,						)
         UpperCAmelCase					=	CLIPTextModel(snake_case__ )
         UpperCAmelCase					=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

         UpperCAmelCase					=	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__=0 )	->	Any:



         """simple docstring"""





         if str(snake_case__ ).startswith("""mps""" ):
                UpperCAmelCase					=	torch.manual_seed(snake_case__ )
         else:
                UpperCAmelCase					=	torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
         UpperCAmelCase					=	{
             """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 						UpperCamelCase_ (					self )	->	Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase					=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase					=	self.get_dummy_components()
         UpperCAmelCase					=	StableDiffusionParadigmsPipeline(**snake_case__ )
         UpperCAmelCase					=	sd_pipe.to(snake_case__ )
         sd_pipe.set_progress_bar_config(disable=snake_case__ )

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

         UpperCAmelCase					=	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 						UpperCamelCase_ (					self )	->	List[Any]:



         """simple docstring"""





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

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

         UpperCAmelCase					=	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 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





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



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



         """simple docstring"""





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



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



         """simple docstring"""





         UpperCAmelCase					=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase					=	self.get_dummy_components()
         UpperCAmelCase					=	StableDiffusionParadigmsPipeline(**snake_case__ )
         UpperCAmelCase					=	sd_pipe.to(snake_case__ )
         sd_pipe.set_progress_bar_config(disable=snake_case__ )

         UpperCAmelCase					=	self.get_dummy_inputs(snake_case__ )
         UpperCAmelCase					=	"""french fries"""
         UpperCAmelCase					=	sd_pipe(**snake_case__       ,						negative_prompt=snake_case__ )
         UpperCAmelCase					=	output.images
         UpperCAmelCase					=	image[0, -3:, -3:, -1]

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

         UpperCAmelCase					=	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 						UpperCamelCase_ (					self )	->	Union[str, Any]:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self       ,						snake_case__=0 )	->	int:



         """simple docstring"""





         UpperCAmelCase					=	torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
         UpperCAmelCase					=	{
             """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 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         UpperCAmelCase					=	"""stabilityai/stable-diffusion-2-base"""
         UpperCAmelCase					=	DDIMParallelScheduler.from_pretrained(snake_case__       ,						subfolder="""scheduler""" )
         UpperCAmelCase					=	StableDiffusionParadigmsPipeline.from_pretrained(snake_case__       ,						scheduler=snake_case__       ,						safety_checker=snake_case__ )
         pipe.to(snake_case__ )
         pipe.set_progress_bar_config(disable=snake_case__ )
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 5_12, 5_12, 3)

         UpperCAmelCase					=	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
