




"""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       lowercase(  __UpperCAmelCase ,    __UpperCAmelCase ,    unittest.TestCase				):







 '''simple docstring'''





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


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



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

   _snake_case							:	Tuple			=						{
       """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 				UpperCamelCase_     (       self:	int,   a_:	List[Any],   a_:	Dict=0	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						floats_tensor((1, 16, 16),   rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
   _snake_case							:	List[Any]			=						floats_tensor((1, 2, 4, 16, 16),   rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
   if str(lowerCAmelCase_	).startswith("""mps"""	):
     _snake_case							:	int			=						torch.manual_seed(lowerCAmelCase_	)
   else:
     _snake_case							:	Any			=						torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
   _snake_case							:	Optional[int]			=						{
       """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 				UpperCamelCase_     (       self:	List[str],   a_:	List[Any],   a_:	str=0	):




   '''simple docstring'''



   _snake_case							:	Any			=						floats_tensor((1, 3, 32, 32),   rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
   _snake_case							:	List[str]			=						image.cpu().permute(0,   2,   3,   1	)[0]
   _snake_case							:	List[str]			=						Image.fromarray(np.uinta(lowerCAmelCase_	)	).convert("""RGB"""	)
   if str(lowerCAmelCase_	).startswith("""mps"""	):
     _snake_case							:	List[Any]			=						torch.manual_seed(lowerCAmelCase_	)
   else:
     _snake_case							:	Tuple			=						torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
   _snake_case							:	str			=						{
       """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 				UpperCamelCase_     (       self:	int,   a_:	Tuple,   a_:	Optional[Any]=0	):




   '''simple docstring'''



   _snake_case							:	int			=						floats_tensor((1, 3, 32, 32),   rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
   _snake_case							:	Optional[Any]			=						image.cpu().permute(0,   2,   3,   1	)[0]
   _snake_case							:	str			=						Image.fromarray(np.uinta(lowerCAmelCase_	)	).convert("""RGB"""	)
   if str(lowerCAmelCase_	).startswith("""mps"""	):
     _snake_case							:	Dict			=						torch.manual_seed(lowerCAmelCase_	)
   else:
     _snake_case							:	Union[str, Any]			=						torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
   _snake_case							:	Optional[int]			=						{
       """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 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



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

   _snake_case							:	Dict			=						self.get_dummy_components()
   _snake_case							:	Optional[Any]			=						self.pipeline_class(**lowerCAmelCase_	)
   pipe.to(lowerCAmelCase_	)
   pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

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

   _snake_case							:	Union[str, Any]			=						self.get_dummy_inputs(lowerCAmelCase_	)
   _snake_case							:	Optional[int]			=						pipe(**lowerCAmelCase_	)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(lowerCAmelCase_	)
     _snake_case							:	Dict			=						self.pipeline_class.from_pretrained(lowerCAmelCase_	)
     pipe_loaded.to(lowerCAmelCase_	)
     pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_	)

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

   _snake_case							:	Optional[Any]			=						self.get_dummy_inputs(lowerCAmelCase_	)
   _snake_case							:	Any			=						pipe_loaded(**lowerCAmelCase_	)[0]

   _snake_case							:	int			=						np.abs(output - output_loaded	).max()
   self.assertLess(lowerCAmelCase_,   1E-4	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						"""cpu"""

   _snake_case							:	Tuple			=						self.get_dummy_components()
   _snake_case							:	int			=						self.pipeline_class(**lowerCAmelCase_	)
   pipe.to(lowerCAmelCase_	)
   pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

   _snake_case							:	Optional[Any]			=						self.get_dummy_mask_inputs(lowerCAmelCase_	)
   _snake_case							:	Union[str, Any]			=						pipe.generate_mask(**lowerCAmelCase_	)
   _snake_case							:	Any			=						mask[0, -3:, -3:]

   self.assertEqual(mask.shape,   (1, 16, 16)	)
   _snake_case							:	Optional[Any]			=						np.array([0] * 9	)
   _snake_case							:	str			=						np.abs(mask_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(lowerCAmelCase_,   1E-3	)
   self.assertEqual(mask[0, -3, -4],   0	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						"""cpu"""

   _snake_case							:	Union[str, Any]			=						self.get_dummy_components()
   _snake_case							:	Dict			=						self.pipeline_class(**lowerCAmelCase_	)
   pipe.to(lowerCAmelCase_	)
   pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

   _snake_case							:	Optional[int]			=						self.get_dummy_inversion_inputs(lowerCAmelCase_	)
   _snake_case							:	Optional[int]			=						pipe.invert(**lowerCAmelCase_	).images
   _snake_case							:	Dict			=						image[0, -1, -3:, -3:]

   self.assertEqual(image.shape,   (2, 32, 32, 3)	)
   _snake_case							:	Optional[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],   )
   _snake_case							:	Dict			=						np.abs(image_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(lowerCAmelCase_,   1E-3	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						"""cpu"""

   _snake_case							:	List[str]			=						self.get_dummy_components()

   _snake_case							:	Optional[int]			=						{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
   _snake_case							:	int			=						DPMSolverMultistepScheduler(**lowerCAmelCase_	)
   _snake_case							:	str			=						DPMSolverMultistepInverseScheduler(**lowerCAmelCase_	)

   _snake_case							:	List[str]			=						self.pipeline_class(**lowerCAmelCase_	)
   pipe.to(lowerCAmelCase_	)
   pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

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

   self.assertEqual(image.shape,   (2, 32, 32, 3)	)
   _snake_case							:	int			=						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],   )
   _snake_case							:	Union[str, Any]			=						np.abs(image_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(lowerCAmelCase_,   1E-3	)







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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



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


 @classmethod
 def 				UpperCamelCase_     (       cls:	int	):




   '''simple docstring'''



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

   _snake_case							:	Dict			=						raw_image.convert("""RGB"""	).resize((768, 768)	)

   _snake_case							:	Any			=						raw_image


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	str			=						torch.manual_seed(0	)

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

   _snake_case							:	Optional[int]			=						"""a bowl of fruit"""
   _snake_case							:	List[str]			=						"""a bowl of pears"""

   _snake_case							:	Optional[Any]			=						pipe.generate_mask(
       image=self.raw_image,   source_prompt=lowerCAmelCase_,   target_prompt=lowerCAmelCase_,   generator=lowerCAmelCase_,   )

   _snake_case							:	Any			=						pipe.invert(
       prompt=lowerCAmelCase_,   image=self.raw_image,   inpaint_strength=0.7,   generator=lowerCAmelCase_	).latents

   _snake_case							:	List[str]			=						pipe(
       prompt=lowerCAmelCase_,   mask_image=lowerCAmelCase_,   image_latents=lowerCAmelCase_,   generator=lowerCAmelCase_,   negative_prompt=lowerCAmelCase_,   inpaint_strength=0.7,   output_type="""numpy""",   ).images[0]

   _snake_case							:	Tuple			=						(
       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 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						torch.manual_seed(0	)

   _snake_case							:	Any			=						StableDiffusionDiffEditPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2-1""",   safety_checker=lowerCAmelCase_,   torch_dtype=torch.floataa	)
   _snake_case							:	Any			=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
   _snake_case							:	Tuple			=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config	)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

   _snake_case							:	List[str]			=						"""a bowl of fruit"""
   _snake_case							:	Optional[Any]			=						"""a bowl of pears"""

   _snake_case							:	int			=						pipe.generate_mask(
       image=self.raw_image,   source_prompt=lowerCAmelCase_,   target_prompt=lowerCAmelCase_,   generator=lowerCAmelCase_,   )

   _snake_case							:	Optional[Any]			=						pipe.invert(
       prompt=lowerCAmelCase_,   image=self.raw_image,   inpaint_strength=0.7,   generator=lowerCAmelCase_,   num_inference_steps=25,   ).latents

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

   _snake_case							:	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












"""simple docstring"""


from __future__ import annotations

import math


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,     int(math.sqrt(snake_case__					) + 1					)	,     6					):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True


A_      			=       [num for num in range(3, 10_00_01, 2) if not is_prime(num)]


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if not isinstance(snake_case__	,     snake_case__					):
    raise ValueError("""n must be an integer"""					)
  if n <= 0:
    raise ValueError("""n must be >= 0"""					)

  _snake_case							:	Any			=						[]
  for num in range(len(snake_case__					)					):
    _snake_case							:	Optional[int]			=						0
    while 2 * i * i <= odd_composites[num]:
      _snake_case							:	Optional[int]			=						odd_composites[num] - 2 * i * i
      if is_prime(snake_case__					):
        break
      i += 1
    else:
      list_nums.append(odd_composites[num]					)
      if len(snake_case__					) == n:
        return list_nums

  return []


def       UpperCAmelCase__ ():
  """simple docstring"""

  return compute_nums(1					)[0]


if __name__ == "__main__":
     print(F'''{solution() = }''')


