




"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__			(  UpperCamelCase_,			UpperCamelCase_,			unittest.TestCase		):






   '''simple docstring'''



   lowerCamelCase__   =    StableDiffusionXLImgaImgPipeline
   lowerCamelCase__   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
   lowerCamelCase__   =    PipelineTesterMixin.required_optional_params - {"""latents"""}
   lowerCamelCase__   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   lowerCamelCase__   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
   lowerCamelCase__   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
   def       A_					(						self						):
       torch.manual_seed(0						)
       _lowerCamelCase  :    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')						,   attention_head_dim=(2, 4)						,   use_linear_projection=__a						,   addition_embed_type='text_time'						,   addition_time_embed_dim=8						,   transformer_layers_per_block=(1, 2)						,   projection_class_embeddings_input_dim=80						,   cross_attention_dim=64						,   )
       _lowerCamelCase  :    Any					=					EulerDiscreteScheduler(
           beta_start=0.0_00_85						,   beta_end=0.0_12						,   steps_offset=1						,   beta_schedule='scaled_linear'						,   timestep_spacing='leading'						,   )
       torch.manual_seed(0						)
       _lowerCamelCase  :    List[str]					=					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						)
       _lowerCamelCase  :    str					=					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						,   hidden_act='gelu'						,   projection_dim=32						,   )
       _lowerCamelCase  :    Any					=					CLIPTextModel(__a						)
       _lowerCamelCase  :    List[Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,   local_files_only=__a						)

       _lowerCamelCase  :    int					=					CLIPTextModelWithProjection(__a						)
       _lowerCamelCase  :    Tuple					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,   local_files_only=__a						)

       _lowerCamelCase  :    Union[str, Any]					=					{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'text_encoder_2': text_encoder_a,
           'tokenizer_2': tokenizer_a,
           # "safety_checker": None,
           # "feature_extractor": None,
       }
       return components
   def       A_					(						self						,   lowercase						,   lowercase=0						):
       _lowerCamelCase  :    Optional[Any]					=					floats_tensor((1, 3, 32, 32)						,   rng=random.Random(__a						)						).to(__a						)
       _lowerCamelCase  :    Optional[int]					=					image / 2 + 0.5
       if str(__a						).startswith('mps'						):
           _lowerCamelCase  :    Dict					=					torch.manual_seed(__a						)
       else:
           _lowerCamelCase  :    Dict					=					torch.Generator(device=__a						).manual_seed(__a						)
       _lowerCamelCase  :    List[Any]					=					{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 5.0,
           'output_type': 'numpy',
           'strength': 0.75,
       }
       return inputs
   def       A_					(						self						):
       _lowerCamelCase  :    str					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
       _lowerCamelCase  :    Optional[Any]					=					self.get_dummy_components()
       _lowerCamelCase  :    int					=					StableDiffusionXLImgaImgPipeline(**__a						)
       _lowerCamelCase  :    Optional[int]					=					sd_pipe.to(__a						)
       sd_pipe.set_progress_bar_config(disable=__a						)

       _lowerCamelCase  :    List[Any]					=					self.get_dummy_inputs(__a						)
       _lowerCamelCase  :    List[Any]					=					sd_pipe(**__a						).images
       _lowerCamelCase  :    Any					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)

       _lowerCamelCase  :    Optional[int]					=					np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
   def       A_					(						self						):
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)
   def       A_					(						self						):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
   def       A_					(						self						):
       pass






   def       A_					(						self						):
       _lowerCamelCase  :    Optional[int]					=					self.get_dummy_components()
       _lowerCamelCase  :    Tuple					=					StableDiffusionXLImgaImgPipeline(**__a						)
       _lowerCamelCase  :    Optional[int]					=					sd_pipe.to(__a						)
       _lowerCamelCase  :    Optional[Any]					=					sd_pipe.to(__a						)
       sd_pipe.set_progress_bar_config(disable=__a						)

       # forward without prompt embeds
       _lowerCamelCase  :    Union[str, Any]					=					self.get_dummy_inputs(__a						)
       _lowerCamelCase  :    int					=					3 * ['this is a negative prompt']
       _lowerCamelCase  :    Optional[Any]					=					negative_prompt
       _lowerCamelCase  :    str					=					3 * [inputs['prompt']]

       _lowerCamelCase  :    Dict					=					sd_pipe(**__a						)
       _lowerCamelCase  :    Union[str, Any]					=					output.images[0, -3:, -3:, -1]

       # forward with prompt embeds
       _lowerCamelCase  :    int					=					self.get_dummy_inputs(__a						)
       _lowerCamelCase  :    Tuple					=					3 * ['this is a negative prompt']
       _lowerCamelCase  :    Union[str, Any]					=					3 * [inputs.pop('prompt'						)]

       (
           _lowerCamelCase
       )  :    Tuple					=					sd_pipe.encode_prompt(__a						,   negative_prompt=__a						)

       _lowerCamelCase  :    str					=					sd_pipe(
           **__a						,   prompt_embeds=__a						,   negative_prompt_embeds=__a						,   pooled_prompt_embeds=__a						,   negative_pooled_prompt_embeds=__a						,   )
       _lowerCamelCase  :    str					=					output.images[0, -3:, -3:, -1]

       # make sure that it's equal
       assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1E-4






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






   '''simple docstring'''



   def       A_					(						self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
   def       A_					(						self						,   lowercase						,   lowercase="cpu"						,   lowercase=torch.floataa						,   lowercase=0						):
       _lowerCamelCase  :    int					=					torch.Generator(device=__a						).manual_seed(__a						)
       _lowerCamelCase  :    Tuple					=					np.random.RandomState(__a						).standard_normal((1, 4, 64, 64)						)
       _lowerCamelCase  :    Tuple					=					torch.from_numpy(__a						).to(device=__a						,   dtype=__a						)
       _lowerCamelCase  :    Tuple					=					{
           '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       A_					(						self						):
       _lowerCamelCase  :    Optional[int]					=					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'						)
       pipe.to(__a						)
       pipe.set_progress_bar_config(disable=__a						)

       _lowerCamelCase  :    Tuple					=					self.get_inputs(__a						)
       _lowerCamelCase  :    Any					=					pipe(**__a						).images
       _lowerCamelCase  :    Any					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       _lowerCamelCase  :    Optional[int]					=					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]						)
       assert np.abs(image_slice - expected_slice						).max() < 7E-3





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				_lowerCamelCase  :    Optional[int]					=					0
				while n > 0:
								res += n % 10
								n //= 10
				return res







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				return n if n < 10 else n % 10 + sum_of_digits(n // 10	)







def   _snake_case    (       lowercase__	):
				return sum(int(lowercase__	) for c in str(abs(lowercase__	)	)	)







def   _snake_case    (       ):
				from collections.abc import Callable
				from timeit import timeit

				def benchmark_a_function(lowercase__							,					lowercase__	) -> None:
								_lowerCamelCase  :    int					=					f'''{func.__name__}({value})'''
								_lowerCamelCase  :    Optional[Any]					=					timeit(f'''__main__.{call}'''							,					setup='import __main__'	)
								print(f'''{call:56} = {func(lowercase__	)} -- {timing:.4f} seconds'''	)

				for value in (262144, 1125899906842624, 1267650600228229401496703205376):
								for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
												benchmark_a_function(lowercase__							,					lowercase__	)
								print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()