



"""simple docstring"""







import math

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> bool:






	"""simple docstring"""





	return math.sqrt(UpperCAmelCase_     ) * math.sqrt(UpperCAmelCase_     ) == num

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> bool:






	"""simple docstring"""





	A__											=					0
	A__											=					n
	while left <= right:
		A__											=					(left + right) // 2
		if mid**2 == n:
			return True
		elif mid**2 > n:
			A__											=					mid - 1
		else:
			A__											=					mid + 1
	return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()






"""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 						a__		( a_,     a_,     unittest.TestCase		):
   __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      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				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      ,       )
          lowercase       :							Optional[Any]									=				EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       steps_offset=1      ,       beta_schedule="scaled_linear"      ,       timestep_spacing="leading"      ,       )
          torch.manual_seed(0							)
          lowercase       :							Any									=				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							)
          lowercase       :							Dict									=				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=32      ,       )
          lowercase       :							str									=				CLIPTextModel(_a							)
          lowercase       :							List[str]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				CLIPTextModelWithProjection(_a							)
          lowercase       :							Union[str, Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							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      __magic_name__     (     self      ,       _a      ,       _a=0							):
          lowercase       :							Dict									=				floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(_a							)							).to(_a							)
          lowercase       :							List[Any]									=				image / 2 + 0.5
          if str(_a							).startswith("mps"							):
                 lowercase       :							Optional[int]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							str									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Optional[int]									=				{
              "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": 0.7_5,
          }
          return inputs
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Optional[int]									=				self.get_dummy_components()
          lowercase       :							str									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							List[Any]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Optional[Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							Tuple									=				sd_pipe(**_a							).images
          lowercase       :							Dict									=				image[0, -3:, -3:, -1]

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

          lowercase       :							Tuple									=				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]							)

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

   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.get_dummy_components()
          lowercase       :							Optional[Any]									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          # forward without prompt embeds
          lowercase       :							Union[str, Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							int									=				3 * ["this is a negative prompt"]
          lowercase       :							Any									=				negative_prompt
          lowercase       :							List[str]									=				3 * [inputs["prompt"]]

          lowercase       :							Optional[int]									=				sd_pipe(**_a							)
          lowercase       :							List[Any]									=				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          lowercase       :							Any									=				self.get_dummy_inputs(_a							)
          lowercase       :							List[str]									=				3 * ["this is a negative prompt"]
          lowercase       :							Optional[Any]									=				3 * [inputs.pop("prompt"							)]

          (
              (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       
          )       :							List[Any]									=				sd_pipe.encode_prompt(_a      ,       negative_prompt=_a							)

          lowercase       :							int									=				sd_pipe(
              **_a      ,       prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       pooled_prompt_embeds=_a      ,       negative_pooled_prompt_embeds=_a      ,       )
          lowercase       :							Dict									=				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 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self      ,       _a      ,       _a="cpu"      ,       _a=torch.floataa      ,       _a=0							):
          lowercase       :							Optional[int]									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Any									=				np.random.RandomState(_a							).standard_normal((1, 4, 64, 64)							)
          lowercase       :							List[str]									=				torch.from_numpy(_a							).to(device=_a      ,       dtype=_a							)
          lowercase       :							List[Any]									=				{
              "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      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Any									=				self.get_inputs(_a							)
          lowercase       :							int									=				pipe(**_a							).images
          lowercase       :							Any									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Optional[Any]									=				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]							)
          assert np.abs(image_slice - expected_slice							).max() < 7E-3


