


import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       KandinskyVaaPriorPipeline
 SCREAMING_SNAKE_CASE__      =       ['prompt']
 SCREAMING_SNAKE_CASE__      =       ['prompt', 'negative_prompt']
 SCREAMING_SNAKE_CASE__      =       [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 SCREAMING_SNAKE_CASE__      =       False




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 32




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 32




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




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




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 100




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
       return tokenizer




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Any 							=       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=1000					,  )
       return CLIPTextModelWithProjection(_lowerCamelCase    )




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

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

       a						:str 							=       PriorTransformer(**_lowerCamelCase    )
       # 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
       a						:str 							=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
       return model




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Tuple 							=       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					,  )

       a						:Tuple 							=       CLIPVisionModelWithProjection(_lowerCamelCase    )
       return model




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       CLIPImageProcessor(
           crop_size=224					,  do_center_crop=_lowerCamelCase					,  do_normalize=_lowerCamelCase					,  do_resize=_lowerCamelCase					,  image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]					,  image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,  resample=3					,  size=224					,  )

       return image_processor




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.dummy_prior
       a						:int 							=       self.dummy_image_encoder
       a						:Any 							=       self.dummy_text_encoder
       a						:List[str] 							=       self.dummy_tokenizer
       a						:Union[str, Any] 							=       self.dummy_image_processor

       a						:List[Any] 							=       UnCLIPScheduler(
           variance_type='''fixed_small_log'''					,  prediction_type='''sample'''					,  num_train_timesteps=1000					,  clip_sample=_lowerCamelCase					,  clip_sample_range=10.0					,  )

       a						:Optional[Any] 							=       {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:str 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:List[str] 							=       {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       '''cpu'''

       a						:Tuple 							=       self.get_dummy_components()

       a						:Optional[int] 							=       self.pipeline_class(**_lowerCamelCase    )
       a						:Dict 							=       pipe.to(_lowerCamelCase    )

       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Optional[Any] 							=       pipe(**self.get_dummy_inputs(_lowerCamelCase    )    )
       a						:Optional[Any] 							=       output.image_embeds

       a						:Union[str, Any] 							=       pipe(
           **self.get_dummy_inputs(_lowerCamelCase    )					,  return_dict=_lowerCamelCase					,  )[0]

       a						:Tuple 							=       image[0, -10:]
       a						:int 							=       image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       a						:Optional[int] 							=       np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]    )

       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       torch_device == '''cpu'''
       a						:Union[str, Any] 							=       True
       a						:int 							=       False

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




 @skip_mps
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       torch_device == '''cpu'''
       a						:Union[str, Any] 							=       False

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









def 				__lowerCamelCase						( UpperCAmelCase_						:		int = 100_0000						):




      """simple docstring"""





      a						:Any 							=       set(range(3 ,  UpperCAmelCase_ ,  2						)						)
      primes.add(2						)
      for p in range(3 ,  UpperCAmelCase_ ,  2						):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,  UpperCAmelCase_ ,  UpperCAmelCase_						)						)						)

      a						:Union[str, Any] 							=       [float(UpperCAmelCase_						) for n in range(limit + 1						)]

      for p in primes:
            for n in range(UpperCAmelCase_ ,  limit + 1 ,  UpperCAmelCase_						):
                  phi[n] *= 1 - 1 / p

      return int(sum(phi[2:]						)						)


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





