





'''simple docstring'''


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 				lowerCAmelCase_						(						snake_case__  ,unittest.TestCase						):
	__lowerCamelCase  :							Union[str, Any]    					=   KandinskyVaaPriorEmbaEmbPipeline
	__lowerCamelCase  :							str    					=   ["prompt", "image"]
	__lowerCamelCase  :							Any    					=   ["prompt", "image"]
	__lowerCamelCase  :							Tuple    					=   [
	    "num_images_per_prompt",
	    "strength",
	    "generator",
	    "num_inference_steps",
	    "latents",
	    "negative_prompt",
	    "guidance_scale",
	    "output_type",
	    "return_dict",
	]
	__lowerCamelCase  :							Any    					=   False




	@property
	def 							_snake_case				(		self						)      ->							Optional[Any]:
					return 32




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




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




	@property
	def 							_snake_case				(		self						)      ->							List[str]:
					return self.time_input_dim * 4




	@property
	def 							_snake_case				(		self						)      ->							Union[str, Any]:
					return 100




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




	@property
	def 							_snake_case				(		self						)      ->							List[str]:
					torch.manual_seed(0						)
					_lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Optional[Any]:
					torch.manual_seed(0						)

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

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




	@property
	def 							_snake_case				(		self						)      ->							Optional[int]:
					torch.manual_seed(0						)
					_lowerCAmelCase		     = 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     ,       )

					_lowerCAmelCase		     = CLIPVisionModelWithProjection(_lowerCAmelCase						)
					return model




	@property
	def 							_snake_case				(		self						)      ->							Any:
					_lowerCAmelCase		     = CLIPImageProcessor(
					    crop_size=224     ,       do_center_crop=_lowerCAmelCase     ,       do_normalize=_lowerCAmelCase     ,       do_resize=_lowerCAmelCase     ,       image_mean=[0.48145466, 0.4578275, 0.40821073]     ,       image_std=[0.26862954, 0.26130258, 0.27577711]     ,       resample=3     ,       size=224     ,       )

					return image_processor




	def 							_snake_case				(		self						)      ->							List[Any]:
					_lowerCAmelCase		     = self.dummy_prior
					_lowerCAmelCase		     = self.dummy_image_encoder
					_lowerCAmelCase		     = self.dummy_text_encoder
					_lowerCAmelCase		     = self.dummy_tokenizer
					_lowerCAmelCase		     = self.dummy_image_processor

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

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

					return components




	def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=0						)      ->							List[str]:
					if str(_lowerCAmelCase						).startswith("mps"						):
									_lowerCAmelCase		     = torch.manual_seed(_lowerCAmelCase						)
					else:
									_lowerCAmelCase		     = torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)

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

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




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

					_lowerCAmelCase		     = self.get_dummy_components()

					_lowerCAmelCase		     = self.pipeline_class(**_lowerCAmelCase						)
					_lowerCAmelCase		     = pipe.to(_lowerCAmelCase						)

					pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

					_lowerCAmelCase		     = pipe(**self.get_dummy_inputs(_lowerCAmelCase						)						)
					_lowerCAmelCase		     = output.image_embeds

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

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

					assert image.shape == (1, 32)

					_lowerCAmelCase		     = np.array(
					    [
					        0.1071284,
					        1.3330271,
					        0.61260223,
					        -0.6691065,
					        -0.3846852,
					        -1.0303661,
					        0.22716111,
					        0.03348901,
					        0.30040675,
					        -0.24805029,
					    ]						)

					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 							_snake_case				(		self						)      ->							Optional[Any]:
					_lowerCAmelCase		     = torch_device == "cpu"
					_lowerCAmelCase		     = True
					_lowerCAmelCase		     = False

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





	@skip_mps
	def 							_snake_case				(		self						)      ->							Optional[int]:
					_lowerCAmelCase		     = torch_device == "cpu"
					_lowerCAmelCase		     = False

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









'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE			=							TypeVar("T", bound=bool)

class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = value
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = None




 @property
 def 							_snake_case				(		self						)      ->							T:
     return self._value





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							SkewNode[T] | None:
     if not roota:
         return roota

     if not roota:
         return roota

     if roota.value > roota.value:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = roota, roota

     _lowerCAmelCase		     = roota
     _lowerCAmelCase		     = roota.right
     _lowerCAmelCase		     = roota.left
     _lowerCAmelCase		     = SkewNode.merge(_lowerCAmelCase     ,       _lowerCAmelCase						)

     return result







class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase = ()						)      ->							None:
     _lowerCAmelCase		     = None
     if data:
         for item in data:
             self.insert(_lowerCAmelCase						)




 def __bool__(		self						)      ->							bool:
     return self._root is not None




 def __iter__(		self						)      ->							Iterator[T]:
     _lowerCAmelCase		     = []
     while self:
         result.append(self.pop()						)

     # Pushing items back to the heap not to clear it.
     for item in result:
         self.insert(_lowerCAmelCase						)

     return iter(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = SkewNode.merge(self._root     ,       SkewNode(_lowerCAmelCase						)						)




 def 							_snake_case				(		self						)      ->							T | None:
     _lowerCAmelCase		     = self.top()
     _lowerCAmelCase		     = (
         SkewNode.merge(self._root.left     ,       self._root.right						) if self._root else None
     )

     return result




 def 							_snake_case				(		self						)      ->							T:
     if not self._root:
         raise IndexError("Can't get top element for the empty heap."						)
     return self._root.value





 def 							_snake_case				(		self						)      ->							None:
     _lowerCAmelCase		     = None


if __name__ == "__main__":
       import doctest

       doctest.testmod()


