




'''simple docstring'''





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        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						KandinskyVaaPriorPipeline
     SCREAMING_SNAKE_CASE_							:   Dict       =						["""prompt"""]
     SCREAMING_SNAKE_CASE_							:   int       =						["""prompt""", """negative_prompt"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						[
         """num_images_per_prompt""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						False






     @property
     def     _lowercase     (			self   )							->      Dict:
          return 32






     @property
     def     _lowercase     (			self   )							->      str:
          return 32






     @property
     def     _lowercase     (			self   )							->      str:
          return self.time_input_dim






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return self.time_input_dim * 4






     @property
     def     _lowercase     (			self   )							->      Any:
          return 100






     @property
     def     _lowercase     (			self   )							->      str:
          _snake_case							=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
          return tokenizer






     @property
     def     _lowercase     (			self   )							->      Dict:
          torch.manual_seed(0   )
          _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=37 ,layer_norm_eps=1e-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
          return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Tuple:
          torch.manual_seed(0   )

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

          _snake_case							=				PriorTransformer(**_SCREAMING_SNAKE_CASE   )
          # 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
          _snake_case							=				nn.Parameter(torch.ones(model.clip_std.shape   )   )
          return model






     @property
     def     _lowercase     (			self   )							->      Any:
          torch.manual_seed(0   )
          _snake_case							=				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 ,)

          _snake_case							=				CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE   )
          return model






     @property
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				CLIPImageProcessor(
              crop_size=224 ,do_center_crop=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_resize=_SCREAMING_SNAKE_CASE ,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=224 ,)

          return image_processor






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				self.dummy_prior
          _snake_case							=				self.dummy_image_encoder
          _snake_case							=				self.dummy_text_encoder
          _snake_case							=				self.dummy_tokenizer
          _snake_case							=				self.dummy_image_processor

          _snake_case							=				UnCLIPScheduler(
              variance_type="fixed_small_log" ,prediction_type="sample" ,num_train_timesteps=1_000 ,clip_sample=_SCREAMING_SNAKE_CASE ,clip_sample_range=1_0.0 ,)

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

          return components






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0   )							->      Optional[int]:
          if str(_SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
               _snake_case							=				torch.manual_seed(_SCREAMING_SNAKE_CASE   )
          else:
               _snake_case							=				torch.Generator(device=_SCREAMING_SNAKE_CASE   ).manual_seed(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				{
              "prompt": "horse",
              "generator": generator,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				"cpu"

          _snake_case							=				self.get_dummy_components()

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

          pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

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

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

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

          assert image.shape == (1, 32)

          _snake_case							=				np.array(
              [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]   )

          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   )							->      Any:
          _snake_case							=				torch_device == "cpu"
          _snake_case							=				True
          _snake_case							=				False

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






     @skip_mps
     def     _lowercase     (			self   )							->      int:
          _snake_case							=				torch_device == "cpu"
          _snake_case							=				False

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








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

     # Validation
     def count_nodes(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

     def count_coins(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_coins(node.left   ) + count_coins(node.right   ) + node.data

     if count_nodes(_UpperCamelCase   ) != count_coins(_UpperCamelCase   ):
          raise ValueError("The nodes number should be same as the number of coins"   )

     # Main calculation
     def get_distrib(_UpperCamelCase:   TreeNode | None   ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0 ,						1   )

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


