"""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 KandinskyPriorPipeline, 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 						UpperCamelCase     (       a__ ,      unittest.TestCase		):
				UpperCAmelCase							: List[str]       =						KandinskyPriorPipeline
				UpperCAmelCase							: Union[str, Any]       =						["""prompt"""]
				UpperCAmelCase							: List[Any]       =						["""prompt""", """negative_prompt"""]
				UpperCAmelCase							: int       =						[
				    """num_images_per_prompt""",
				    """generator""",
				    """num_inference_steps""",
				    """latents""",
				    """negative_prompt""",
				    """guidance_scale""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCAmelCase							: List[Any]       =						False



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



				@property
				def    _lowercase	(self							: int)       ->      Optional[int]:
					return 32



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



				@property
				def    _lowercase	(self							: List[Any])       ->      Union[str, Any]:
					return self.time_input_dim * 4



				@property
				def    _lowercase	(self							: Tuple)       ->      Union[str, Any]:
					return 1_00



				@property
				def    _lowercase	(self							: List[Any])       ->      List[Any]:
					__snake_case     :				Optional[int] 						=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def    _lowercase	(self							: Optional[int])       ->      int:
					torch.manual_seed(0)
					__snake_case     :				Union[str, 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=10_00      ,    )
					return CLIPTextModelWithProjection(lowercase__)



				@property
				def    _lowercase	(self							: Union[str, Any])       ->      List[str]:
					torch.manual_seed(0)

					__snake_case     :				Optional[Any] 						=					{
					    'num_attention_heads': 2,
					    'attention_head_dim': 12,
					    'embedding_dim': self.text_embedder_hidden_size,
					    'num_layers': 1,
					}

					__snake_case     :				Tuple 						=					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
					__snake_case     :				Optional[int] 						=					nn.Parameter(torch.ones(model.clip_std.shape))
					return model



				@property
				def    _lowercase	(self							: Any)       ->      Any:
					torch.manual_seed(0)
					__snake_case     :				Dict 						=					CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size      ,    image_size=2_24      ,    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     :				str 						=					CLIPVisionModelWithProjection(lowercase__)
					return model



				@property
				def    _lowercase	(self							: int)       ->      str:
					__snake_case     :				List[str] 						=					CLIPImageProcessor(
					    crop_size=2_24      ,    do_center_crop=lowercase__      ,    do_normalize=lowercase__      ,    do_resize=lowercase__      ,    image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]      ,    image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]      ,    resample=3      ,    size=2_24      ,    )

					return image_processor



				def    _lowercase	(self							: Optional[int])       ->      Optional[int]:
					__snake_case     :				List[Any] 						=					self.dummy_prior
					__snake_case     :				Union[str, Any] 						=					self.dummy_image_encoder
					__snake_case     :				List[Any] 						=					self.dummy_text_encoder
					__snake_case     :				Dict 						=					self.dummy_tokenizer
					__snake_case     :				List[str] 						=					self.dummy_image_processor

					__snake_case     :				List[str] 						=					UnCLIPScheduler(
					    variance_type='fixed_small_log'      ,    prediction_type='sample'      ,    num_train_timesteps=10_00      ,    clip_sample=lowercase__      ,    clip_sample_range=10.0      ,    )

					__snake_case     :				List[str] 						=					{
					    'prior': prior,
					    'image_encoder': image_encoder,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'scheduler': scheduler,
					    'image_processor': image_processor,
					}

					return components



				def    _lowercase	(self							: List[Any]      ,    _A							: Optional[int]      ,    _A							: List[str]=0)       ->      Optional[int]:
					if str(lowercase__).startswith('mps'):
						__snake_case     :				Optional[Any] 						=					torch.manual_seed(lowercase__)
					else:
						__snake_case     :				Optional[int] 						=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
					__snake_case     :				Dict 						=					{
					    'prompt': 'horse',
					    'generator': generator,
					    'guidance_scale': 4.0,
					    'num_inference_steps': 2,
					    'output_type': 'np',
					}
					return inputs



				def    _lowercase	(self							: Tuple)       ->      str:
					__snake_case     :				List[str] 						=					'cpu'

					__snake_case     :				Any 						=					self.get_dummy_components()

					__snake_case     :				Tuple 						=					self.pipeline_class(**lowercase__)
					__snake_case     :				Optional[Any] 						=					pipe.to(lowercase__)

					pipe.set_progress_bar_config(disable=lowercase__)

					__snake_case     :				Tuple 						=					pipe(**self.get_dummy_inputs(lowercase__))
					__snake_case     :				Optional[int] 						=					output.image_embeds

					__snake_case     :				int 						=					pipe(
					    **self.get_dummy_inputs(lowercase__)      ,    return_dict=lowercase__      ,    )[0]

					__snake_case     :				List[str] 						=					image[0, -10:]
					__snake_case     :				List[Any] 						=					image_from_tuple[0, -10:]

					assert image.shape == (1, 32)

					__snake_case     :				Dict 						=					np.array(
					    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156])

					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							: Dict)       ->      Dict:
					__snake_case     :				Any 						=					torch_device == 'cpu'
					__snake_case     :				str 						=					True
					__snake_case     :				Dict 						=					False

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






				@skip_mps
				def    _lowercase	(self							: int)       ->      int:
					__snake_case     :				Optional[int] 						=					torch_device == 'cpu'
					__snake_case     :				Any 						=					False

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











def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
