'''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       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    StableDiffusionDiffEditPipeline
   A							:       Dict     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
   A							:       Tuple     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
   A							:       str     =    frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   A							:       int     =    frozenset([]			)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        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')       ,				cross_attention_dim=32       ,				attention_head_dim=(2, 4)       ,				use_linear_projection=_a       ,				)
        lowercase_					:       Any     				=					DDIMScheduler(
            beta_start=0.0_00_85       ,				beta_end=0.0_12       ,				beta_schedule='scaled_linear'       ,				clip_sample=_a       ,				set_alpha_to_one=_a       ,				)
        lowercase_					:       Union[str, Any]     				=					DDIMInverseScheduler(
            beta_start=0.0_00_85       ,				beta_end=0.0_12       ,				beta_schedule='scaled_linear'       ,				clip_sample=_a       ,				set_alpha_to_zero=_a       ,				)
        torch.manual_seed(0				)
        lowercase_					:       List[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_					:       Union[str, 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=1_000       ,				hidden_act='gelu'       ,				projection_dim=512       ,				)
        lowercase_					:       List[str]     				=					CLIPTextModel(_a				)
        lowercase_					:       List[str]     				=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

        lowercase_					:       Union[str, 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 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     str:
        lowercase_					:       Optional[Any]     				=					floats_tensor((1, 16, 16)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					floats_tensor((1, 2, 4, 16, 16)       ,				rng=random.Random(_a				)				).to(_a				)
        if str(_a				).startswith('mps'				):
             lowercase_					:       List[str]     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       Dict     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Union[str, 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 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     int:
        lowercase_					:       Optional[Any]     				=					floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					image.cpu().permute(0       ,				2       ,				3       ,				1				)[0]
        lowercase_					:       Dict     				=					Image.fromarray(np.uinta(_a				)				).convert('RGB'				)
        if str(_a				).startswith('mps'				):
             lowercase_					:       Dict     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       str     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Optional[Any]     				=					{
            '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 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     int:
        lowercase_					:       List[str]     				=					floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					image.cpu().permute(0       ,				2       ,				3       ,				1				)[0]
        lowercase_					:       Any     				=					Image.fromarray(np.uinta(_a				)				).convert('RGB'				)
        if str(_a				).startswith('mps'				):
             lowercase_					:       List[str]     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       Any     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       List[str]     				=					{
            '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 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        if not hasattr(self.pipeline_class       ,				'_optional_components'				):
             return

        lowercase_					:       Any     				=					self.get_dummy_components()
        lowercase_					:       Dict     				=					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}				)

        lowercase_					:       List[str]     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       Any     				=					pipe(**_a				)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_a				)
             lowercase_					:       Optional[int]     				=					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.'''       ,				)

        lowercase_					:       List[str]     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       Tuple     				=					pipe_loaded(**_a				)[0]

        lowercase_					:       Optional[int]     				=					np.abs(output - output_loaded				).max()
        self.assertLess(_a       ,				1e-4				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Optional[int]     				=					'cpu'

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

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

        self.assertEqual(mask.shape       ,				(1, 16, 16)				)
        lowercase_					:       Dict     				=					np.array([0] * 9				)
        lowercase_					:       int     				=					np.abs(mask_slice.flatten() - expected_slice				).max()
        self.assertLessEqual(_a       ,				1e-3				)
        self.assertEqual(mask[0, -3, -4]       ,				0				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					'cpu'

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

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

        self.assertEqual(image.shape       ,				(2, 32, 32, 3)				)
        lowercase_					:       Optional[int]     				=					np.array(
            [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]       ,				)
        lowercase_					:       int     				=					np.abs(image_slice.flatten() - expected_slice				).max()
        self.assertLessEqual(_a       ,				1e-3				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3				)





   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Tuple     				=					'cpu'

        lowercase_					:       Any     				=					self.get_dummy_components()

        lowercase_					:       Optional[int]     				=					{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
        lowercase_					:       str     				=					DPMSolverMultistepScheduler(**_a				)
        lowercase_					:       Optional[Any]     				=					DPMSolverMultistepInverseScheduler(**_a				)

        lowercase_					:       str     				=					self.pipeline_class(**_a				)
        pipe.to(_a				)
        pipe.set_progress_bar_config(disable=_a				)

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

        self.assertEqual(image.shape       ,				(2, 32, 32, 3)				)
        lowercase_					:       Union[str, Any]     				=					np.array(
            [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]       ,				)
        lowercase_					:       str     				=					np.abs(image_slice.flatten() - expected_slice				).max()
        self.assertLessEqual(_a       ,				1e-3				)



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






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @classmethod
   def 		_lowerCamelCase (cls				)    ->     Union[str, Any]:
        lowercase_					:       List[Any]     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'				)

        lowercase_					:       List[Any]     				=					raw_image.convert('RGB'				).resize((768, 768)				)

        lowercase_					:       Union[str, Any]     				=					raw_image
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					torch.manual_seed(0				)

        lowercase_					:       str     				=					StableDiffusionDiffEditPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-1'       ,				safety_checker=_a       ,				torch_dtype=torch.floataa				)
        lowercase_					:       Tuple     				=					DDIMScheduler.from_config(pipe.scheduler.config				)
        lowercase_					:       Dict     				=					DDIMInverseScheduler.from_config(pipe.scheduler.config				)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       List[str]     				=					'a bowl of fruit'
        lowercase_					:       str     				=					'a bowl of pears'

        lowercase_					:       int     				=					pipe.generate_mask(
            image=self.raw_image       ,				source_prompt=_a       ,				target_prompt=_a       ,				generator=_a       ,				)

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

        lowercase_					:       int     				=					pipe(
            prompt=_a       ,				mask_image=_a       ,				image_latents=_a       ,				generator=_a       ,				negative_prompt=_a       ,				inpaint_strength=0.7       ,				output_type='numpy'       ,				).images[0]

        lowercase_					:       List[Any]     				=					(
            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 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       List[Any]     				=					torch.manual_seed(0				)

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

        lowercase_					:       int     				=					'a bowl of fruit'
        lowercase_					:       Dict     				=					'a bowl of pears'

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

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

        lowercase_					:       List[Any]     				=					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]

        lowercase_					:       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





'''simple docstring'''




import math

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     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(SCREAMING_SNAKE_CASE_							) + 1							)  ,       6							):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_ = 10_001							):

     try:
          lowercase_					:       List[Any]     				=					int(SCREAMING_SNAKE_CASE_							)
     except (TypeError, ValueError):
          raise TypeError('Parameter nth must be int or castable to int.'							) from None
     if nth <= 0:
          raise ValueError('Parameter nth must be greater than or equal to one.'							)
     lowercase_					:       list[int]     				=					[]
     lowercase_					:       Optional[int]     				=					2
     while len(SCREAMING_SNAKE_CASE_							) < nth:
          if is_prime(SCREAMING_SNAKE_CASE_							):
               primes.append(SCREAMING_SNAKE_CASE_							)
               num += 1
          else:
               num += 1
     return primes[len(SCREAMING_SNAKE_CASE_							) - 1]


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




