
'''simple docstring'''





from __future__ import annotations

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

a__		: Optional[Any]  							=							TypeVar("T", bound=bool)


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:List[Any]						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      value
       __A													=      None
       __A													=      None






 @property
 def 						lowercase_    (      self	:Tuple      )					->      T:







       '''simple docstring'''



       return self._value






 @staticmethod
 def 						lowercase_    (      _A	:SkewNode[T] | None						,					_A	:SkewNode[T] | None      )					->      SkewNode[T] | None:







       '''simple docstring'''



       if not roota:
             return roota

       if not roota:
             return roota

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

       __A													=      roota
       __A													=      roota.right
       __A													=      roota.left
       __A													=      SkewNode.merge(_A						,					_A      )

       return result


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:Optional[int]						,					_A	:Iterable[T] | None = ()      )					->      None:







       '''simple docstring'''



       __A													=      None
       if data:
             for item in data:
                   self.insert(_A      )






 def __bool__(      self	:List[str]      )					->      bool:







       '''simple docstring'''



       return self._root is not None






 def __iter__(      self	:Dict      )					->      Iterator[T]:







       '''simple docstring'''



       __A													=      []
       while self:
             result.append(self.pop()      )

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

       return iter(_A      )






 def 						lowercase_    (      self	:Tuple						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      SkewNode.merge(self._root						,					SkewNode(_A      )      )






 def 						lowercase_    (      self	:Dict      )					->      T | None:







       '''simple docstring'''



       __A													=      self.top()
       __A													=      (
           SkewNode.merge(self._root.left						,					self._root.right      ) if self._root else None
       )

       return result






 def 						lowercase_    (      self	:int      )					->      T:







       '''simple docstring'''



       if not self._root:
             raise IndexError('Can\'t get top element for the empty heap.'      )
       return self._root.value






 def 						lowercase_    (      self	:int      )					->      None:







       '''simple docstring'''



       __A													=      None


if __name__ == "__main__":
 import doctest

 doctest.testmod()







"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class      UpperCamelCase__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





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





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





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				MCLIPConfig(
													    numDims=self.cross_attention_dim		,transformerDimensions=self.text_embedder_hidden_size		,hidden_size=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_hidden_layers=5		,vocab_size=10_05		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													torch.manual_seed(0  )

													A__						  =				{
													    'in_channels': 4,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 8,
													    'addition_embed_type': 'text_image',
													    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
													    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
													    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
													    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    'layers_per_block': 1,
													    'encoder_hid_dim': self.text_embedder_hidden_size,
													    'encoder_hid_dim_type': 'text_image_proj',
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': None,
													}

													A__						  =				UNetaDConditionModel(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}





						@property
						def 					snake_case__						(							self  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

													A__						  =				{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

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

													A__						  =				image[0, -3:, -3:, -1]
													A__						  =				image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													A__						  =				np.array(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]  )

													assert (
													    np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class      UpperCamelCase__(      unittest.TestCase			):





						def 					snake_case__						(							self  )       ->					Tuple:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





