'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, 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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


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






   """simple docstring"""
   A							:       Optional[int]     =    StableDiffusionXLImgaImgPipeline
   A							:       int     =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   A							:       Optional[int]     =    PipelineTesterMixin.required_optional_params - {'''latents'''}
   A							:       Tuple     =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   A							:       str     =    IMAGE_TO_IMAGE_IMAGE_PARAMS
   A							:       int     =    IMAGE_TO_IMAGE_IMAGE_PARAMS
   def 		_lowerCamelCase (self				)    ->     str:
        torch.manual_seed(0				)
        lowercase_					:       List[Any]     				=					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')       ,				attention_head_dim=(2, 4)       ,				use_linear_projection=_a       ,				addition_embed_type='text_time'       ,				addition_time_embed_dim=8       ,				transformer_layers_per_block=(1, 2)       ,				projection_class_embeddings_input_dim=80       ,				cross_attention_dim=64       ,				)
        lowercase_					:       Union[str, Any]     				=					EulerDiscreteScheduler(
            beta_start=0.0_00_85       ,				beta_end=0.0_12       ,				steps_offset=1       ,				beta_schedule='scaled_linear'       ,				timestep_spacing='leading'       ,				)
        torch.manual_seed(0				)
        lowercase_					:       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				)
        lowercase_					:       List[str]     				=					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=32       ,				)
        lowercase_					:       List[str]     				=					CLIPTextModel(_a				)
        lowercase_					:       Tuple     				=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,				local_files_only=_a				)

        lowercase_					:       Tuple     				=					CLIPTextModelWithProjection(_a				)
        lowercase_					:       Dict     				=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,				local_files_only=_a				)

        lowercase_					:       Dict     				=					{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'text_encoder_2': text_encoder_a,
            'tokenizer_2': tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components
   def 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     Tuple:
        lowercase_					:       str     				=					floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					image / 2 + 0.5
        if str(_a				).startswith('mps'				):
             lowercase_					:       Any     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       Dict     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Optional[Any]     				=					{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 5.0,
            'output_type': 'numpy',
            'strength': 0.75,
        }
        return inputs
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Any     				=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase_					:       Optional[int]     				=					self.get_dummy_components()
        lowercase_					:       Union[str, Any]     				=					StableDiffusionXLImgaImgPipeline(**_a				)
        lowercase_					:       Any     				=					sd_pipe.to(_a				)
        sd_pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       int     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       Tuple     				=					sd_pipe(**_a				).images
        lowercase_					:       Optional[Any]     				=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)

        lowercase_					:       Tuple     				=					np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
   def 		_lowerCamelCase (self				)    ->     str:
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)
   def 		_lowerCamelCase (self				)    ->     int:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        pass





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Union[str, Any]     				=					self.get_dummy_components()
        lowercase_					:       Union[str, Any]     				=					StableDiffusionXLImgaImgPipeline(**_a				)
        lowercase_					:       List[Any]     				=					sd_pipe.to(_a				)
        lowercase_					:       Tuple     				=					sd_pipe.to(_a				)
        sd_pipe.set_progress_bar_config(disable=_a				)

        # forward without prompt embeds
        lowercase_					:       Optional[Any]     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       Optional[Any]     				=					3 * ['this is a negative prompt']
        lowercase_					:       Dict     				=					negative_prompt
        lowercase_					:       Dict     				=					3 * [inputs['prompt']]

        lowercase_					:       Tuple     				=					sd_pipe(**_a				)
        lowercase_					:       Any     				=					output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        lowercase_					:       Tuple     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       int     				=					3 * ['this is a negative prompt']
        lowercase_					:       str     				=					3 * [inputs.pop('prompt'				)]

        (
            (
            lowercase_
        )				,(
            lowercase_
        )				,(
            lowercase_
        )				,(
            lowercase_
        )				,
        )					:       Optional[Any]     				=					sd_pipe.encode_prompt(_a       ,				negative_prompt=_a				)

        lowercase_					:       int     				=					sd_pipe(
            **_a       ,				prompt_embeds=_a       ,				negative_prompt_embeds=_a       ,				pooled_prompt_embeds=_a       ,				negative_pooled_prompt_embeds=_a       ,				)
        lowercase_					:       Dict     				=					output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1e-4



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






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Tuple:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def 		_lowerCamelCase (self       ,				_a       ,				_a="cpu"       ,				_a=torch.floataa       ,				_a=0				)    ->     Optional[int]:
        lowercase_					:       int     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Union[str, Any]     				=					np.random.RandomState(_a				).standard_normal((1, 4, 64, 64)				)
        lowercase_					:       str     				=					torch.from_numpy(_a				).to(device=_a       ,				dtype=_a				)
        lowercase_					:       Tuple     				=					{
            'prompt': 'a photograph of an astronaut riding a horse',
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs





   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Tuple     				=					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'				)
        pipe.to(_a				)
        pipe.set_progress_bar_config(disable=_a				)

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

        assert image.shape == (1, 512, 512, 3)
        lowercase_					:       List[Any]     				=					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
        assert np.abs(image_slice - expected_slice				).max() < 7e-3





'''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() = }""")




