





'''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 					_UpperCAmelCase							(		lowerCAmelCase_			,      unittest.TestCase     ):
		a     :     List[Any]     						=KandinskyVaaPriorEmbaEmbPipeline
		a     :     str     						=["""prompt""", """image"""]
		a     :     List[str]     						=["""prompt""", """image"""]
		a     :     Any     						=[
		    """num_images_per_prompt""",
		    """strength""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		a     :     Dict     						=False
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return 32
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return 32
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return self.time_input_dim
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return self.time_input_dim * 4
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return 1_00
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )
					return tokenizer
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					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=10_00,)
					return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE    )
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					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(**__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
					__lowerCAmelCase      		=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
					return model
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					torch.manual_seed(0    )
					__lowerCAmelCase      		=       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,)

					__lowerCAmelCase      		=       CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE    )
					return model
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       CLIPImageProcessor(
					    crop_size=2_24,do_center_crop=__SCREAMING_SNAKE_CASE,do_normalize=__SCREAMING_SNAKE_CASE,do_resize=__SCREAMING_SNAKE_CASE,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073],image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711],resample=3,size=2_24,)

					return image_processor
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__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=10_00,clip_sample=__SCREAMING_SNAKE_CASE,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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0    ):



					'''simple docstring'''

					if str(__SCREAMING_SNAKE_CASE    ).startswith("""mps"""    ):
								__lowerCAmelCase      		=       torch.manual_seed(__SCREAMING_SNAKE_CASE    )
					else:
								__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       floats_tensor((1, 3, 64, 64),rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((2_56, 2_56)    )

					__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 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       """cpu"""

					__lowerCAmelCase      		=       self.get_dummy_components()

					__lowerCAmelCase      		=       self.pipeline_class(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    )
					__lowerCAmelCase      		=       output.image_embeds

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

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

					assert image.shape == (1, 32)

					__lowerCAmelCase      		=       np.array(
					    [
					        0.107_1284,
					        1.333_0271,
					        0.6126_0223,
					        -0.669_1065,
					        -0.384_6852,
					        -1.030_3661,
					        0.2271_6111,
					        0.0334_8901,
					        0.3004_0675,
					        -0.2480_5029,
					    ]    )

					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    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       torch_device == """cpu"""
					__lowerCAmelCase      		=       True
					__lowerCAmelCase      		=       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 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       torch_device == """cpu"""
					__lowerCAmelCase      		=       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.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_a      :				Dict											=					TypeVar("""T""", bound=bool)





class 					_UpperCAmelCase							(		Generic[T]     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       value
					__lowerCAmelCase      		=       None
					__lowerCAmelCase      		=       None
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return self._value







		@staticmethod
		def 						lowerCamelCase__    (						__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )

					return result





class 					_UpperCAmelCase							(		Generic[T]     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE = ()    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       None
					if data:
								for item in data:
											self.insert(__SCREAMING_SNAKE_CASE    )
		def __bool__(						self    ):



					'''simple docstring'''

					return self._root is not None
		def __iter__(						self    ):



					'''simple docstring'''

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

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

					return iter(__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       SkewNode.merge(self._root,SkewNode(__SCREAMING_SNAKE_CASE    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

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

					return result
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

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







		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

