def 							lowerCAmelCase__    (       a__      )	->list:




  '''simple docstring'''

  _UpperCamelCase				=  []
  _UpperCamelCase				=  1

  for i in range(len(a__      )      ):
    if i + 1 < len(a__      ) and text[i] == text[i + 1]:
      count += 1
    else:
      encoded.append((text[i], count)      )
      _UpperCamelCase				=  1

  return encoded


def 							lowerCAmelCase__    (       a__      )	->str:




  '''simple docstring'''

  return "".join(char * length for char, length in encoded      )


if __name__ == "__main__":
   from doctest import testmod

   testmod(name='''run_length_encode''', verbose=True)
   testmod(name='''run_length_decode''', verbose=True)





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       (			lowerCAmelCase,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = KandinskyVaaPriorEmbaEmbPipeline
 __A           = ['''prompt''', '''image''']
 __A           = ['''prompt''', '''image''']
 __A           = [
     '''num_images_per_prompt''',
     '''strength''',
     '''generator''',
     '''num_inference_steps''',
     '''latents''',
     '''negative_prompt''',
     '''guidance_scale''',
     '''output_type''',
     '''return_dict''',
 ]
 __A           = False







 @property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     List[str]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					Any)		->     Optional[int]:


   """simple docstring"""







   return self.time_input_dim







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     int:


   """simple docstring"""







   return self.time_input_dim * 4







 @property
 def 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







   return 100







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
   return tokenizer







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Optional[int]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  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-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
   return CLIPTextModelWithProjection(lowercase_)







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     List[str]:


   """simple docstring"""







   torch.manual_seed(0)

   _UpperCamelCase				=  {
       "num_attention_heads": 2,
       "attention_head_dim": 12,
       "embedding_dim": self.text_embedder_hidden_size,
       "num_layers": 1,
   }

   _UpperCamelCase				=  PriorTransformer(**lowercase_)
   # 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
   _UpperCamelCase				=  nn.Parameter(torch.ones(model.clip_std.shape))
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					str)		->     Union[str, Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  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				,      )

   _UpperCamelCase				=  CLIPVisionModelWithProjection(lowercase_)
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPImageProcessor(
       crop_size=224				,      do_center_crop=lowercase_				,      do_normalize=lowercase_				,      do_resize=lowercase_				,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,      resample=3				,      size=224				,      )

   return image_processor







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.dummy_prior
   _UpperCamelCase				=  self.dummy_image_encoder
   _UpperCamelCase				=  self.dummy_text_encoder
   _UpperCamelCase				=  self.dummy_tokenizer
   _UpperCamelCase				=  self.dummy_image_processor

   _UpperCamelCase				=  UnCLIPScheduler(
       variance_type="fixed_small_log"				,      prediction_type="sample"				,      num_train_timesteps=1000				,      clip_sample=lowercase_				,      clip_sample_range=10.0				,      )

   _UpperCamelCase				=  {
       "prior": prior,
       "image_encoder": image_encoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "scheduler": scheduler,
       "image_processor": image_processor,
   }

   return components







 def 						__UpperCAmelCase							(						self							:					List[str]				,      lowercase_							:					Optional[int]				,      lowercase_							:					Union[str, Any]=0)		->     Tuple:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase				=  torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase				=  torch.Generator(device=lowercase_).manual_seed(lowercase_)

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

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







 def 						__UpperCAmelCase							(						self							:					List[str])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  "cpu"

   _UpperCamelCase				=  self.get_dummy_components()

   _UpperCamelCase				=  self.pipeline_class(**lowercase_)
   _UpperCamelCase				=  pipe.to(lowercase_)

   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase				=  pipe(**self.get_dummy_inputs(lowercase_))
   _UpperCamelCase				=  output.image_embeds

   _UpperCamelCase				=  pipe(
       **self.get_dummy_inputs(lowercase_)				,      return_dict=lowercase_				,      )[0]

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

   assert image.shape == (1, 32)

   _UpperCamelCase				=  np.array(
       [
           0.1_07_12_84,
           1.3_33_02_71,
           0.61_26_02_23,
           -0.6_69_10_65,
           -0.3_84_68_52,
           -1.0_30_36_61,
           0.22_71_61_11,
           0.03_34_89_01,
           0.30_04_06_75,
           -0.24_80_50_29,
       ])

   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 						__UpperCAmelCase							(						self							:					Optional[int])		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  True
   _UpperCamelCase				=  False

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







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  False

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




