





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
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        _lowerCamelCase      (       _lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			KandinskyVaaPipeline
						UpperCAmelCase_ =			[
						    "image_embeds",
						    "negative_image_embeds",
						]
						UpperCAmelCase_ =			["image_embeds", "negative_image_embeds"]
						UpperCAmelCase_ =			[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase_ =			False


						@property
						def     snake_case_   (self		) ->   Tuple:
								return 32


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


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


						@property
						def     snake_case_   (self		) ->   int:
								return self.time_input_dim * 4


						@property
						def     snake_case_   (self		) ->   Optional[int]:
								return 1_00


						@property
						def     snake_case_   (self		) ->   Any:
								torch.manual_seed(0		)

								UpperCamelCase   =	{
								    "in_channels": 4,
								    # Out channels is double in channels because predicts mean and variance
								    "out_channels": 8,
								    "addition_embed_type": "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": "image_proj",
								    "cross_attention_dim": self.cross_attention_dim,
								    "attention_head_dim": 4,
								    "resnet_time_scale_shift": "scale_shift",
								    "class_embed_type": None,
								}

								UpperCamelCase   =	UNetaDConditionModel(**__a		)
								return model


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


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.dummy_unet
								UpperCamelCase   =	self.dummy_movq

								UpperCamelCase   =	DDIMScheduler(
								    num_train_timesteps=10_00   ,							beta_schedule="linear"   ,							beta_start=0.00085   ,							beta_end=0.012   ,							clip_sample=__a   ,							set_alpha_to_one=__a   ,							steps_offset=1   ,							prediction_type="epsilon"   ,							thresholding=__a   ,							)

								UpperCamelCase   =	{
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}
								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   List[Any]:
								UpperCamelCase   =	floats_tensor((1, self.text_embedder_hidden_size)   ,							rng=random.Random(__a		)		).to(__a		)
								UpperCamelCase   =	floats_tensor((1, self.text_embedder_hidden_size)   ,							rng=random.Random(seed + 1		)		).to(
								    __a		)
								if str(__a		).startswith("mps"		):
										UpperCamelCase   =	torch.manual_seed(__a		)
								else:
										UpperCamelCase   =	torch.Generator(device=__a		).manual_seed(__a		)
								UpperCamelCase   =	{
								    "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		) ->   List[str]:
								UpperCamelCase   =	"cpu"

								UpperCamelCase   =	self.get_dummy_components()

								UpperCamelCase   =	self.pipeline_class(**__a		)
								UpperCamelCase   =	pipe.to(__a		)

								pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	pipe(**self.get_dummy_inputs(__a		)		)
								UpperCamelCase   =	output.images

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

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

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

								UpperCamelCase   =	np.array(
								    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]		)

								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        _lowerCamelCase      (       unittest.TestCase ):


						def     snake_case_   (self		) ->   List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"		)

								UpperCamelCase   =	KandinskyVaaPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-prior"   ,							torch_dtype=torch.floataa		)
								pipe_prior.to(__a		)

								UpperCamelCase   =	KandinskyVaaPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-decoder"   ,							torch_dtype=torch.floataa		)
								UpperCamelCase   =	pipeline.to(__a		)
								pipeline.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	"red cat, 4k photo"

								UpperCamelCase   =	torch.Generator(device="cuda"		).manual_seed(0		)
								UpperCamelCase   ,				UpperCamelCase   =	pipe_prior(
								    __a   ,							generator=__a   ,							num_inference_steps=5   ,							negative_prompt=""   ,							).to_tuple()

								UpperCamelCase   =	torch.Generator(device="cuda"		).manual_seed(0		)
								UpperCamelCase   =	pipeline(
								    image_embeds=__a   ,							negative_image_embeds=__a   ,							generator=__a   ,							num_inference_steps=1_00   ,							output_type="np"   ,							)

								UpperCamelCase   =	output.images[0]

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

								assert_mean_pixel_difference(__a   ,							__a		)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__     			=			{'''tokenization_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






