

'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class       __snake_case  (    UpperCAmelCase__     , UpperCAmelCase__     , UpperCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Optional[Any]						 =  StableDiffusionInstructPixaPixPipeline
  __lowerCAmelCase     :	Union[str, Any]						 =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  __lowerCAmelCase     :	List[str]						 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowerCAmelCase     :	List[str]						 =  IMAGE_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase     :	Tuple						 =  IMAGE_TO_IMAGE_IMAGE_PARAMS

  def 						lowerCAmelCase__     (					self):
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						UNetaDConditionModel(
          block_out_channels=(32, 64)						,		layers_per_block=2						,		sample_size=32						,		in_channels=8						,		out_channels=4						,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,		cross_attention_dim=32						,		)
      SCREAMING_SNAKE_CASE_									=						PNDMScheduler(skip_prk_steps=_A)
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						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						,		)
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						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						,		)
      SCREAMING_SNAKE_CASE_									=						CLIPTextModel(_A)
      SCREAMING_SNAKE_CASE_									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=0):
      SCREAMING_SNAKE_CASE_									=						floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_A)).to(_A)
      SCREAMING_SNAKE_CASE_									=						image.cpu().permute(0						,		2						,		3						,		1)[0]
      SCREAMING_SNAKE_CASE_									=						Image.fromarray(np.uinta(_A)).convert('RGB')
      if str(_A).startswith('mps'):
          SCREAMING_SNAKE_CASE_									=						torch.manual_seed(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						torch.Generator(device=_A).manual_seed(_A)
      SCREAMING_SNAKE_CASE_									=						{
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """image_guidance_scale""": 1,
          """output_type""": """numpy""",
      }
      return inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe.to(_A)
      sd_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe(**_A).images
      SCREAMING_SNAKE_CASE_									=						image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe.to(_A)
      sd_pipe.set_progress_bar_config(disable=_A)

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

      assert image.shape == (1, 32, 32, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe.to(_A)
      sd_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						[inputs["""prompt"""]] * 2

      SCREAMING_SNAKE_CASE_									=						np.array(inputs['image']).astype(np.floataa) / 255.0
      SCREAMING_SNAKE_CASE_									=						torch.from_numpy(_A).unsqueeze(0).to(_A)
      SCREAMING_SNAKE_CASE_									=						image / 2 + 0.5
      SCREAMING_SNAKE_CASE_									=						image.permute(0						,		3						,		1						,		2)
      SCREAMING_SNAKE_CASE_									=						image.repeat(2						,		1						,		1						,		1)

      SCREAMING_SNAKE_CASE_									=						sd_pipe(**_A).images
      SCREAMING_SNAKE_CASE_									=						image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						EulerAncestralDiscreteScheduler(
          beta_start=0.0_0_0_8_5						,		beta_end=0.0_1_2						,		beta_schedule='scaled_linear')
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe.to(_A)
      sd_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						sd_pipe(**_A).images
      SCREAMING_SNAKE_CASE_									=						image[0, -3:, -3:, -1]

      SCREAMING_SNAKE_CASE_									=						[round(_A						,		4) for x in image_slice.flatten().tolist()]
      print(','.join([str(_A) for x in slice]))

      assert image.shape == (1, 32, 32, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						VaeImageProcessor(do_resize=_A						,		do_normalize=_A)
      SCREAMING_SNAKE_CASE_									=						pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						pipe(**self.get_dummy_inputs_by_type(_A						,		input_image_type='pt'))[0]

      SCREAMING_SNAKE_CASE_									=						components["""vae"""]
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs_by_type(_A						,		input_image_type='pt')

      for image_param in self.image_latents_params:
          if image_param in inputs.keys():
              SCREAMING_SNAKE_CASE_									=						vae.encode(inputs[image_param]).latent_dist.mode()

      SCREAMING_SNAKE_CASE_									=						pipe(**_A)[0]

      SCREAMING_SNAKE_CASE_									=						np.abs(out - out_latents_inputs).max()
      self.assertLess(_A						,		1E-4						,		'passing latents as image input generate different result from passing image')






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

  def 						lowerCAmelCase__     (					self):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def 						lowerCAmelCase__     (					self						,		_A=0):
      SCREAMING_SNAKE_CASE_									=						torch.manual_seed(_A)
      SCREAMING_SNAKE_CASE_									=						load_image(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg')
      SCREAMING_SNAKE_CASE_									=						{
          """prompt""": """turn him into a cyborg""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """image_guidance_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          'timbrooks/instruct-pix2pix'						,		safety_checker=_A)
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5])

      assert np.abs(expected_slice - image_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          'timbrooks/instruct-pix2pix'						,		safety_checker=_A)
      SCREAMING_SNAKE_CASE_									=						LMSDiscreteScheduler.from_config(pipe.scheduler.config)
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1])

      assert np.abs(expected_slice - image_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          'timbrooks/instruct-pix2pix'						,		safety_checker=_A)
      SCREAMING_SNAKE_CASE_									=						DDIMScheduler.from_config(pipe.scheduler.config)
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3])

      assert np.abs(expected_slice - image_slice).max() < 1E-3

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						0

      def callback_fn(_A						,		_A						,		_A) -> None:
          SCREAMING_SNAKE_CASE_									=						True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 1:
              SCREAMING_SNAKE_CASE_									=						latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              SCREAMING_SNAKE_CASE_									=						latents[0, -3:, -3:, -1]
              SCREAMING_SNAKE_CASE_									=						np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3])

              assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
          elif step == 2:
              SCREAMING_SNAKE_CASE_									=						latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              SCREAMING_SNAKE_CASE_									=						latents[0, -3:, -3:, -1]
              SCREAMING_SNAKE_CASE_									=						np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5])

              assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

      SCREAMING_SNAKE_CASE_									=						False

      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          'timbrooks/instruct-pix2pix'						,		safety_checker=_A						,		torch_dtype=torch.floataa)
      SCREAMING_SNAKE_CASE_									=						pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing()

      SCREAMING_SNAKE_CASE_									=						self.get_inputs()
      pipe(**_A						,		callback=_A						,		callback_steps=1)
      assert callback_fn.has_been_called
      assert number_of_steps == 3

  def 						lowerCAmelCase__     (					self):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          'timbrooks/instruct-pix2pix'						,		safety_checker=_A						,		torch_dtype=torch.floataa)
      SCREAMING_SNAKE_CASE_									=						pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing(1)
      pipe.enable_sequential_cpu_offload()

      SCREAMING_SNAKE_CASE_									=						self.get_inputs()
      SCREAMING_SNAKE_CASE_									=						pipe(**_A)

      SCREAMING_SNAKE_CASE_									=						torch.cuda.max_memory_allocated()
      # make sure that less than 2.2 GB is allocated
      assert mem_bytes < 2.2 * 10**9


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_inputs()
      # resize to resolution that is divisible by 8 but not 16 or 32
      SCREAMING_SNAKE_CASE_									=						inputs["""image"""].resize((504, 504))

      SCREAMING_SNAKE_CASE_									=						"""timbrooks/instruct-pix2pix"""
      SCREAMING_SNAKE_CASE_									=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
          _A						,		safety_checker=_A						,		)
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      pipe.enable_attention_slicing()

      SCREAMING_SNAKE_CASE_									=						pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.images[0]

      SCREAMING_SNAKE_CASE_									=						image[255:258, 383:386, -1]

      assert image.shape == (504, 504, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3





def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    for n in range(1 ,   1_000_000       ):
        yield n * (n + 1) // 2


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						1
    SCREAMING_SNAKE_CASE_									=						2
    while i * i <= n:
        SCREAMING_SNAKE_CASE_									=						0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    return next(i for i in triangle_number_generator() if count_divisors(_SCREAMING_SNAKE_CASE       ) > 500       )


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


