

"""simple docstring"""
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	lowerCamelCase     (    __lowercase , __lowercase , unittest.TestCase					):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE 	=   StableDiffusionDiffEditPipeline
  SCREAMING_SNAKE_CASE 	=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
  SCREAMING_SNAKE_CASE 	=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
  SCREAMING_SNAKE_CASE 	=   frozenset(
      []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE 	=   frozenset([]					)



  def       _a	(self     ):


        """simple docstring"""

        torch.manual_seed(0     )
        UpperCAmelCase__       :				List[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""")				,    cross_attention_dim=32				,    attention_head_dim=(2, 4)				,    use_linear_projection=_a				,    )
        UpperCAmelCase__       :				Optional[Any]						 =  DDIMScheduler(
            beta_start=0.00_085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""				,    clip_sample=_a				,    set_alpha_to_one=_a				,    )
        UpperCAmelCase__       :				Optional[Any]						 =  DDIMInverseScheduler(
            beta_start=0.00_085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""				,    clip_sample=_a				,    set_alpha_to_zero=_a				,    )
        torch.manual_seed(0     )
        UpperCAmelCase__       :				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     )
        UpperCAmelCase__       :				Optional[Any]						 =  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=512				,    )
        UpperCAmelCase__       :				Dict						 =  CLIPTextModel(_a     )
        UpperCAmelCase__       :				Optional[int]						 =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

        UpperCAmelCase__       :				Optional[Any]						 =  {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''inverse_scheduler''': inverse_scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components



  def       _a	(self				,    _lowerCamelCase				,    _lowerCamelCase=0     ):


        """simple docstring"""

        UpperCAmelCase__       :				Union[str, Any]						 =  floats_tensor((1, 16, 16)				,    rng=random.Random(_a     )     ).to(_a     )
        UpperCAmelCase__       :				Union[str, Any]						 =  floats_tensor((1, 2, 4, 16, 16)				,    rng=random.Random(_a     )     ).to(_a     )
        if str(_a     ).startswith("""mps"""     ):
              UpperCAmelCase__       :				Dict						 =  torch.manual_seed(_a     )
        else:
              UpperCAmelCase__       :				Optional[int]						 =  torch.Generator(device=_a     ).manual_seed(_a     )
        UpperCAmelCase__       :				Any						 =  {
            '''prompt''': '''a dog and a newt''',
            '''mask_image''': mask,
            '''image_latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



  def       _a	(self				,    _lowerCamelCase				,    _lowerCamelCase=0     ):


        """simple docstring"""

        UpperCAmelCase__       :				List[str]						 =  floats_tensor((1, 3, 32, 32)				,    rng=random.Random(_a     )     ).to(_a     )
        UpperCAmelCase__       :				int						 =  image.cpu().permute(0				,    2				,    3				,    1     )[0]
        UpperCAmelCase__       :				Any						 =  Image.fromarray(np.uinta(_a     )     ).convert("""RGB"""     )
        if str(_a     ).startswith("""mps"""     ):
              UpperCAmelCase__       :				Optional[Any]						 =  torch.manual_seed(_a     )
        else:
              UpperCAmelCase__       :				str						 =  torch.Generator(device=_a     ).manual_seed(_a     )
        UpperCAmelCase__       :				Dict						 =  {
            '''image''': image,
            '''source_prompt''': '''a cat and a frog''',
            '''target_prompt''': '''a dog and a newt''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''num_maps_per_mask''': 2,
            '''mask_encode_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



  def       _a	(self				,    _lowerCamelCase				,    _lowerCamelCase=0     ):


        """simple docstring"""

        UpperCAmelCase__       :				Optional[Any]						 =  floats_tensor((1, 3, 32, 32)				,    rng=random.Random(_a     )     ).to(_a     )
        UpperCAmelCase__       :				Any						 =  image.cpu().permute(0				,    2				,    3				,    1     )[0]
        UpperCAmelCase__       :				Union[str, Any]						 =  Image.fromarray(np.uinta(_a     )     ).convert("""RGB"""     )
        if str(_a     ).startswith("""mps"""     ):
              UpperCAmelCase__       :				Dict						 =  torch.manual_seed(_a     )
        else:
              UpperCAmelCase__       :				str						 =  torch.Generator(device=_a     ).manual_seed(_a     )
        UpperCAmelCase__       :				Any						 =  {
            '''image''': image,
            '''prompt''': '''a cat and a frog''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''decode_latents''': True,
            '''output_type''': '''numpy''',
        }
        return inputs



  def       _a	(self     ):


        """simple docstring"""

        if not hasattr(self.pipeline_class				,    """_optional_components"""     ):
              return

        UpperCAmelCase__       :				List[str]						 =  self.get_dummy_components()
        UpperCAmelCase__       :				List[Any]						 =  self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
              setattr(_a				,    _a				,    _a     )
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}     )

        UpperCAmelCase__       :				int						 =  self.get_dummy_inputs(_a     )
        UpperCAmelCase__       :				Optional[int]						 =  pipe(**_a     )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_a     )
              UpperCAmelCase__       :				List[Any]						 =  self.pipeline_class.from_pretrained(_a     )
              pipe_loaded.to(_a     )
              pipe_loaded.set_progress_bar_config(disable=_a     )

        for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_a				,    _a     ) is None				,    F"""`{optional_component}` did not stay set to None after loading."""				,    )

        UpperCAmelCase__       :				List[Any]						 =  self.get_dummy_inputs(_a     )
        UpperCAmelCase__       :				List[Any]						 =  pipe_loaded(**_a     )[0]

        UpperCAmelCase__       :				Any						 =  np.abs(output - output_loaded     ).max()
        self.assertLess(_a				,    1e-4     )



  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				Union[str, Any]						 =  '''cpu'''

        UpperCAmelCase__       :				str						 =  self.get_dummy_components()
        UpperCAmelCase__       :				Optional[int]						 =  self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        UpperCAmelCase__       :				Optional[Any]						 =  self.get_dummy_mask_inputs(_a     )
        UpperCAmelCase__       :				List[Any]						 =  pipe.generate_mask(**_a     )
        UpperCAmelCase__       :				List[str]						 =  mask[0, -3:, -3:]

        self.assertEqual(mask.shape				,    (1, 16, 16)     )
        UpperCAmelCase__       :				Optional[Any]						 =  np.array([0] * 9     )
        UpperCAmelCase__       :				List[Any]						 =  np.abs(mask_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(_a				,    1e-3     )
        self.assertEqual(mask[0, -3, -4]				,    0     )



  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				Dict						 =  '''cpu'''

        UpperCAmelCase__       :				Union[str, Any]						 =  self.get_dummy_components()
        UpperCAmelCase__       :				Any						 =  self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        UpperCAmelCase__       :				Union[str, Any]						 =  self.get_dummy_inversion_inputs(_a     )
        UpperCAmelCase__       :				List[str]						 =  pipe.invert(**_a     ).images
        UpperCAmelCase__       :				Optional[int]						 =  image[0, -1, -3:, -3:]

        self.assertEqual(image.shape				,    (2, 32, 32, 3)     )
        UpperCAmelCase__       :				Any						 =  np.array(
            [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]				,    )
        UpperCAmelCase__       :				Optional[Any]						 =  np.abs(image_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(_a				,    1e-3     )



  def       _a	(self     ):


        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=5e-3     )




  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				Any						 =  '''cpu'''

        UpperCAmelCase__       :				int						 =  self.get_dummy_components()

        UpperCAmelCase__       :				str						 =  {'''beta_start''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
        UpperCAmelCase__       :				Any						 =  DPMSolverMultistepScheduler(**_a     )
        UpperCAmelCase__       :				int						 =  DPMSolverMultistepInverseScheduler(**_a     )

        UpperCAmelCase__       :				Optional[int]						 =  self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        UpperCAmelCase__       :				List[str]						 =  self.get_dummy_inversion_inputs(_a     )
        UpperCAmelCase__       :				List[Any]						 =  pipe.invert(**_a     ).images
        UpperCAmelCase__       :				Any						 =  image[0, -1, -3:, -3:]

        self.assertEqual(image.shape				,    (2, 32, 32, 3)     )
        UpperCAmelCase__       :				List[str]						 =  np.array(
            [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]				,    )
        UpperCAmelCase__       :				int						 =  np.abs(image_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(_a				,    1e-3     )




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


  '''simple docstring'''







  def       _a	(self     ):


        """simple docstring"""

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



  @classmethod
  def       _a	(cls     ):


        """simple docstring"""

        UpperCAmelCase__       :				Optional[int]						 =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""     )

        UpperCAmelCase__       :				int						 =  raw_image.convert("""RGB"""     ).resize((768, 768)     )

        UpperCAmelCase__       :				List[Any]						 =  raw_image



  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				List[str]						 =  torch.manual_seed(0     )

        UpperCAmelCase__       :				List[str]						 =  StableDiffusionDiffEditPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-1"""				,    safety_checker=_a				,    torch_dtype=torch.floataa     )
        UpperCAmelCase__       :				List[Any]						 =  DDIMScheduler.from_config(pipe.scheduler.config     )
        UpperCAmelCase__       :				Any						 =  DDIMInverseScheduler.from_config(pipe.scheduler.config     )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_a     )

        UpperCAmelCase__       :				int						 =  '''a bowl of fruit'''
        UpperCAmelCase__       :				Tuple						 =  '''a bowl of pears'''

        UpperCAmelCase__       :				Optional[int]						 =  pipe.generate_mask(
            image=self.raw_image				,    source_prompt=_a				,    target_prompt=_a				,    generator=_a				,    )

        UpperCAmelCase__       :				Optional[Any]						 =  pipe.invert(
            prompt=_a				,    image=self.raw_image				,    inpaint_strength=0.7				,    generator=_a     ).latents

        UpperCAmelCase__       :				Union[str, Any]						 =  pipe(
            prompt=_a				,    mask_image=_a				,    image_latents=_a				,    generator=_a				,    negative_prompt=_a				,    inpaint_strength=0.7				,    output_type="""numpy"""				,    ).images[0]

        UpperCAmelCase__       :				Dict						 =  (
            np.array(
                load_image(
                    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                    """/diffedit/pears.png"""     ).resize((768, 768)     )     )
            / 255
        )
        assert np.abs((expected_image - image).max()     ) < 5e-1




  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				Dict						 =  torch.manual_seed(0     )

        UpperCAmelCase__       :				List[str]						 =  StableDiffusionDiffEditPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-1"""				,    safety_checker=_a				,    torch_dtype=torch.floataa     )
        UpperCAmelCase__       :				List[str]						 =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
        UpperCAmelCase__       :				Any						 =  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_a     )

        UpperCAmelCase__       :				str						 =  '''a bowl of fruit'''
        UpperCAmelCase__       :				Tuple						 =  '''a bowl of pears'''

        UpperCAmelCase__       :				List[Any]						 =  pipe.generate_mask(
            image=self.raw_image				,    source_prompt=_a				,    target_prompt=_a				,    generator=_a				,    )

        UpperCAmelCase__       :				List[str]						 =  pipe.invert(
            prompt=_a				,    image=self.raw_image				,    inpaint_strength=0.7				,    generator=_a				,    num_inference_steps=25				,    ).latents

        UpperCAmelCase__       :				int						 =  pipe(
            prompt=_a				,    mask_image=_a				,    image_latents=_a				,    generator=_a				,    negative_prompt=_a				,    inpaint_strength=0.7				,    num_inference_steps=25				,    output_type="""numpy"""				,    ).images[0]

        UpperCAmelCase__       :				str						 =  (
            np.array(
                load_image(
                    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                    """/diffedit/pears.png"""     ).resize((768, 768)     )     )
            / 255
        )
        assert np.abs((expected_image - image).max()     ) < 5e-1




from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



