






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 	lowercase_      (			SCREAMING_SNAKE_CASE__				,						unittest.TestCase     ):






      '''simple docstring'''




      __snake_case  						=						PaintByExamplePipeline
      __snake_case  						=						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      __snake_case  						=						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __snake_case  						=						frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







      def    __lowerCAmelCase      (   self   :		List[str]						)					->int:




       """simple docstring"""


       torch.manual_seed(0						)
       a							=   UNetaDConditionModel(
           block_out_channels=(32, 64)				,      layers_per_block=2				,      sample_size=32				,      in_channels=9				,      out_channels=4				,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,      cross_attention_dim=32				,      )
       a							=   PNDMScheduler(skip_prk_steps=__UpperCAmelCase						)
       torch.manual_seed(0						)
       a							=   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						)
       a							=   CLIPVisionConfig(
           hidden_size=32				,      projection_dim=32				,      intermediate_size=37				,      layer_norm_eps=1e-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      image_size=32				,      patch_size=4				,      )
       a							=   PaintByExampleImageEncoder(__UpperCAmelCase				,      proj_size=32						)
       a							=   CLIPImageProcessor(crop_size=32				,      size=32						)

       a							=   {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''image_encoder''': image_encoder,
           '''safety_checker''': None,
           '''feature_extractor''': feature_extractor,
       }
       return components







      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      __UpperCAmelCase   :		List[str]						)					->Dict:




       """simple docstring"""


       a							=   np.array(image.convert('''RGB'''						)						)
       a							=   image[None].transpose(0				,      3				,      1				,      2						)
       a							=   torch.from_numpy(__UpperCAmelCase						).to(dtype=torch.floataa						) / 127.5 - 1.0
       return image







      def    __lowerCAmelCase      (   self   :		Union[str, Any]				,      __UpperCAmelCase   :		str="cpu"				,      __UpperCAmelCase   :		str=0						)					->Optional[Any]:




       """simple docstring"""


       a							=   floats_tensor((1, 3, 32, 32)				,      rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
       a							=   image.cpu().permute(0				,      2				,      3				,      1						)[0]
       a							=   Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						).resize((64, 64)						)
       a							=   Image.fromarray(np.uinta(image + 4						)						).convert('''RGB'''						).resize((64, 64)						)
       a							=   Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						).resize((32, 32)						)

       if str(__UpperCAmelCase						).startswith('''mps'''						):
        a							=   torch.manual_seed(__UpperCAmelCase						)
       else:
        a							=   torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
       a							=   {
           '''example_image''': example_image,
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs







      def    __lowerCAmelCase      (   self   :		Any						)					->Tuple:




       """simple docstring"""


       a							=   self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       a							=   PaintByExamplePipeline(**__UpperCAmelCase						)
       a							=   pipe.to('''cpu'''						)
       pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

       a							=   self.get_dummy_inputs()
       a							=   pipe(**__UpperCAmelCase						)
       a							=   output.images

       a							=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       a							=   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2







      def    __lowerCAmelCase      (   self   :		int						)					->Optional[Any]:




       """simple docstring"""


       a							=   '''cpu'''
       a							=   self.get_dummy_inputs()
       inputs.pop('''mask_image'''						)
       a							=   self.convert_to_pt(inputs.pop('''image'''						)						)
       a							=   image.clamp(0				,      1						) / 2

       # make sure here that pndm scheduler skips prk
       a							=   PaintByExamplePipeline(**self.get_dummy_components()						)
       a							=   pipe.to(__UpperCAmelCase						)
       pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

       a							=   pipe(image=__UpperCAmelCase				,      mask_image=mask_image[:, 0]				,      **__UpperCAmelCase						)
       a							=   output.images

       a							=   image.cpu().permute(0				,      2				,      3				,      1						)[0]
       a							=   mask_image.cpu().permute(0				,      2				,      3				,      1						)[0]

       a							=   Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						)
       a							=   Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						)

       a							=   pipe(**self.get_dummy_inputs()						)
       a							=   output.images

       assert out_a.shape == (1, 64, 64, 3)
       assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5e-2







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




       """simple docstring"""


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






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






      '''simple docstring'''




      def    __lowerCAmelCase      (   self   :		List[str]						)					->List[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->List[Any]:




       """simple docstring"""


       a							=   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/dog_in_bucket.png'''						)
       a							=   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/mask.png'''						)
       a							=   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/panda.jpg'''						)

       a							=   PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''						)
       a							=   pipe.to(__UpperCAmelCase						)
       pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

       a							=   torch.manual_seed(321						)
       a							=   pipe(
           image=__UpperCAmelCase				,      mask_image=__UpperCAmelCase				,      example_image=__UpperCAmelCase				,      generator=__UpperCAmelCase				,      guidance_scale=5.0				,      num_inference_steps=50				,      output_type='''np'''				,      )

       a							=   output.images

       a							=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       a							=   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2










def 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''recursive_lucas_number accepts only integer arguments.'''				)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1				) + recursive_lucas_number(
     n_th_number - 2				)
def 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''				)
 a						,  a							=   2, 1
 for _ in range(a				):
  a						,  a							=   b, a + b
 return a


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCAmelCase__            =	int(input("Enter the number of terms in lucas series:\n").strip())
     print("Using recursive function to calculate lucas series:")
     print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
     print("\nUsing dynamic function to calculate lucas series:")
     print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))


