




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 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

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



   @property
   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      return self.time_input_dim



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      return self.time_input_dim * 4



   @property
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      return 1_0_0



   @property
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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



   @property
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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=3_7						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_0_0_0						,   )
      return CLIPTextModelWithProjection(__lowerCamelCase					)



   @property
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      torch.manual_seed(0					)

      _SCREAMING_SNAKE_CASE =   {
          "num_attention_heads": 2,
          "attention_head_dim": 1_2,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }

      _SCREAMING_SNAKE_CASE =   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
      _SCREAMING_SNAKE_CASE =   nn.Parameter(torch.ones(model.clip_std.shape					)					)
      return model



   @property
   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size						,   image_size=2_2_4						,   projection_dim=self.text_embedder_hidden_size						,   intermediate_size=3_7						,   num_attention_heads=4						,   num_channels=3						,   num_hidden_layers=5						,   patch_size=1_4						,   )

      _SCREAMING_SNAKE_CASE =   CLIPVisionModelWithProjection(__lowerCamelCase					)
      return model



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(
          crop_size=2_2_4						,   do_center_crop=__lowerCamelCase						,   do_normalize=__lowerCamelCase						,   do_resize=__lowerCamelCase						,   image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]						,   image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]						,   resample=3						,   size=2_2_4						,   )

      return image_processor



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.dummy_prior
      _SCREAMING_SNAKE_CASE =   self.dummy_image_encoder
      _SCREAMING_SNAKE_CASE =   self.dummy_text_encoder
      _SCREAMING_SNAKE_CASE =   self.dummy_tokenizer
      _SCREAMING_SNAKE_CASE =   self.dummy_image_processor

      _SCREAMING_SNAKE_CASE =   UnCLIPScheduler(
          variance_type="fixed_small_log"						,   prediction_type="sample"						,   num_train_timesteps=1_0_0_0						,   clip_sample=__lowerCamelCase						,   clip_sample_range=1_0.0						,   )

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

      return components



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str=0					):

      """simple docstring"""
      if str(__lowerCamelCase					).startswith("mps"					):
         _SCREAMING_SNAKE_CASE =   torch.manual_seed(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 6_4, 6_4)						,   rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((2_5_6, 2_5_6)					)

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



   def 							lowerCAmelCase_    (					self      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "cpu"

      _SCREAMING_SNAKE_CASE =   self.get_dummy_components()

      _SCREAMING_SNAKE_CASE =   self.pipeline_class(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)

      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   pipe(**self.get_dummy_inputs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   output.image_embeds

      _SCREAMING_SNAKE_CASE =   pipe(
          **self.get_dummy_inputs(__lowerCamelCase					)						,   return_dict=__lowerCamelCase						,   )[0]

      _SCREAMING_SNAKE_CASE =   image[0, -1_0:]
      _SCREAMING_SNAKE_CASE =   image_from_tuple[0, -1_0:]

      assert image.shape == (1, 3_2)

      _SCREAMING_SNAKE_CASE =   np.array(
          [
              0.1_0_7_1_2_8_4,
              1.3_3_3_0_2_7_1,
              0.6_1_2_6_0_2_2_3,
              -0.6_6_9_1_0_6_5,
              -0.3_8_4_6_8_5_2,
              -1.0_3_0_3_6_6_1,
              0.2_2_7_1_6_1_1_1,
              0.0_3_3_4_8_9_0_1,
              0.3_0_0_4_0_6_7_5,
              -0.2_4_8_0_5_0_2_9,
          ]					)

      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 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   torch_device == "cpu"
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   False

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



   @skip_mps
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   torch_device == "cpu"
      _SCREAMING_SNAKE_CASE =   False

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







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   len(node.outbound						)

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

   for ri, row in enumerate(__A						):
      for ci, col in enumerate(__A						):
         if col == 1:
            nodes[ci].add_inbound(names[ri]						)
            nodes[ri].add_outbound(names[ci]						)

   print("======= Nodes ======="						)
   for node in nodes:
      print(__A						)

   page_rank(__A						)


if __name__ == "__main__":
 main()

