
import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase_				(  A					,       unittest.TestCase     ):




      '''simple docstring'''







      a		:Dict						 =      KandinskyVaaPriorEmbaEmbPipeline
      a		:str						 =      ['prompt', 'image']
      a		:List[str]						 =      ['prompt', 'image']
      a		:int						 =      [
          'num_images_per_prompt',
          'strength',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      a		:int						 =      False







      @property
      def lowercase__		( self):
            return 32







      @property
      def lowercase__		( self):
            return 32







      @property
      def lowercase__		( self):
            return self.time_input_dim







      @property
      def lowercase__		( self):
            return self.time_input_dim * 4







      @property
      def lowercase__		( self):
            return 100







      @property
      def lowercase__		( self):
            lowerCAmelCase_						=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
            return tokenizer







      @property
      def lowercase__		( self):
            torch.manual_seed(0)
            lowerCAmelCase_						=      CLIPTextConfig(
                bos_token_id=0					,					eos_token_id=2					,					hidden_size=self.text_embedder_hidden_size					,					projection_dim=self.text_embedder_hidden_size					,					intermediate_size=37					,					layer_norm_eps=1E-05					,					num_attention_heads=4					,					num_hidden_layers=5					,					pad_token_id=1					,					vocab_size=1_000					,					)
            return CLIPTextModelWithProjection(_UpperCAmelCase)







      @property
      def lowercase__		( self):
            torch.manual_seed(0)

            lowerCAmelCase_						=      {
                '''num_attention_heads''': 2,
                '''attention_head_dim''': 12,
                '''embedding_dim''': self.text_embedder_hidden_size,
                '''num_layers''': 1,
            }

            lowerCAmelCase_						=      PriorTransformer(**_UpperCAmelCase)
            # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
            lowerCAmelCase_						=      nn.Parameter(torch.ones(model.clip_std.shape))
            return model







      @property
      def lowercase__		( self):
            torch.manual_seed(0)
            lowerCAmelCase_						=      CLIPVisionConfig(
                hidden_size=self.text_embedder_hidden_size					,					image_size=224					,					projection_dim=self.text_embedder_hidden_size					,					intermediate_size=37					,					num_attention_heads=4					,					num_channels=3					,					num_hidden_layers=5					,					patch_size=14					,					)

            lowerCAmelCase_						=      CLIPVisionModelWithProjection(_UpperCAmelCase)
            return model







      @property
      def lowercase__		( self):
            lowerCAmelCase_						=      CLIPImageProcessor(
                crop_size=224					,					do_center_crop=_UpperCAmelCase					,					do_normalize=_UpperCAmelCase					,					do_resize=_UpperCAmelCase					,					image_mean=[0.48145466, 0.4578275, 0.40821073]					,					image_std=[0.26862954, 0.26130258, 0.27577711]					,					resample=3					,					size=224					,					)

            return image_processor







      def lowercase__		( self):
            lowerCAmelCase_						=      self.dummy_prior
            lowerCAmelCase_						=      self.dummy_image_encoder
            lowerCAmelCase_						=      self.dummy_text_encoder
            lowerCAmelCase_						=      self.dummy_tokenizer
            lowerCAmelCase_						=      self.dummy_image_processor

            lowerCAmelCase_						=      UnCLIPScheduler(
                variance_type='''fixed_small_log'''					,					prediction_type='''sample'''					,					num_train_timesteps=1_000					,					clip_sample=_UpperCAmelCase					,					clip_sample_range=10.0					,					)

            lowerCAmelCase_						=      {
                '''prior''': prior,
                '''image_encoder''': image_encoder,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''scheduler''': scheduler,
                '''image_processor''': image_processor,
            }

            return components







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=0):
            if str(_UpperCAmelCase).startswith('''mps'''):
                  lowerCAmelCase_						=      torch.manual_seed(_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)

            lowerCAmelCase_						=      floats_tensor((1, 3, 64, 64)					,					rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
            lowerCAmelCase_						=      image.cpu().permute(0					,					2					,					3					,					1)[0]
            lowerCAmelCase_						=      Image.fromarray(np.uinta(_UpperCAmelCase)).convert('''RGB''').resize((256, 256))

            lowerCAmelCase_						=      {
                '''prompt''': '''horse''',
                '''image''': init_image,
                '''strength''': 0.5,
                '''generator''': generator,
                '''guidance_scale''': 4.0,
                '''num_inference_steps''': 2,
                '''output_type''': '''np''',
            }
            return inputs







      def lowercase__		( self):
            lowerCAmelCase_						=      '''cpu'''

            lowerCAmelCase_						=      self.get_dummy_components()

            lowerCAmelCase_						=      self.pipeline_class(**_UpperCAmelCase)
            lowerCAmelCase_						=      pipe.to(_UpperCAmelCase)

            pipe.set_progress_bar_config(disable=_UpperCAmelCase)

            lowerCAmelCase_						=      pipe(**self.get_dummy_inputs(_UpperCAmelCase))
            lowerCAmelCase_						=      output.image_embeds

            lowerCAmelCase_						=      pipe(
                **self.get_dummy_inputs(_UpperCAmelCase)					,					return_dict=_UpperCAmelCase					,					)[0]

            lowerCAmelCase_						=      image[0, -10:]
            lowerCAmelCase_						=      image_from_tuple[0, -10:]

            assert image.shape == (1, 32)

            lowerCAmelCase_						=      np.array(
                [
                    0.1071284,
                    1.3330271,
                    0.61260223,
                    -0.6691065,
                    -0.3846852,
                    -1.0303661,
                    0.22716111,
                    0.03348901,
                    0.30040675,
                    -0.24805029,
                ])

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







      @skip_mps
      def lowercase__		( self):
            lowerCAmelCase_						=      torch_device == '''cpu'''
            lowerCAmelCase_						=      True
            lowerCAmelCase_						=      False

            self._test_inference_batch_single_identical(
                test_max_difference=_UpperCAmelCase					,					relax_max_difference=_UpperCAmelCase					,					test_mean_pixel_difference=_UpperCAmelCase					,					)







      @skip_mps
      def lowercase__		( self):
            lowerCAmelCase_						=      torch_device == '''cpu'''
            lowerCAmelCase_						=      False

            self._test_attention_slicing_forward_pass(
                test_max_difference=_UpperCAmelCase					,					test_mean_pixel_difference=_UpperCAmelCase					,					)





import math
def lowerCamelCase_       (			A			:      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(A						) + 1						)		,					6						):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


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



