






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 _lowercase     (    UpperCamelCase__    ,				unittest.TestCase		):
							a    				=	KandinskyInpaintPipeline
							a    				=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							a    				=	[
							    """prompt""",
							    """negative_prompt""",
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							a    				=	[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """negative_prompt""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							a    				=	False




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														return 32




							@property
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														return 32




							@property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														return self.time_input_dim




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




							@property
							def 		lowerCamelCase_    (							self:    str					):
														return 100




							@property
							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Any	  =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
														return tokenizer




							@property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     int	  =	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=1_005      ,							)

														lowerCamelCase__ :     int	  =	MultilingualCLIP(__a					)
														lowerCamelCase__ :     int	  =	text_encoder.eval()

														return text_encoder




							@property
							def 		lowerCamelCase_    (							self:    Tuple					):
														torch.manual_seed(0					)

														lowerCamelCase__ :     Optional[Any]	  =	{
														    """in_channels""": 9,
														    # 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,
														}

														lowerCamelCase__ :     Union[str, Any]	  =	UNetaDConditionModel(**__a					)
														return model




							@property
							def 		lowerCamelCase_    (							self:    str					):
														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 		lowerCamelCase_    (							self:    int					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	VQModel(**self.dummy_movq_kwargs					)
														return model




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_text_encoder
														lowerCamelCase__ :     List[str]	  =	self.dummy_tokenizer
														lowerCamelCase__ :     List[str]	  =	self.dummy_unet
														lowerCamelCase__ :     Optional[int]	  =	self.dummy_movq

														lowerCamelCase__ :     Optional[Any]	  =	DDIMScheduler(
														    num_train_timesteps=1_000      ,							beta_schedule="""linear"""      ,							beta_start=0.00_085      ,							beta_end=0.012      ,							clip_sample=__a      ,							set_alpha_to_one=__a      ,							steps_offset=1      ,							prediction_type="""epsilon"""      ,							thresholding=__a      ,							)

														lowerCamelCase__ :     Union[str, Any]	  =	{
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}

														return components




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Any=0					):
														lowerCamelCase__ :     List[str]	  =	floats_tensor((1, self.cross_attention_dim)      ,							rng=random.Random(__a					)					).to(__a					)
														lowerCamelCase__ :     Union[str, Any]	  =	floats_tensor((1, self.cross_attention_dim)      ,							rng=random.Random(seed + 1					)					).to(__a					)
														# create init_image
														lowerCamelCase__ :     Union[str, Any]	  =	floats_tensor((1, 3, 64, 64)      ,							rng=random.Random(__a					)					).to(__a					)
														lowerCamelCase__ :     List[Any]	  =	image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
														lowerCamelCase__ :     Any	  =	Image.fromarray(np.uinta(__a					)					).convert("""RGB"""					).resize((256, 256)					)
														# create mask
														lowerCamelCase__ :     Any	  =	np.ones((64, 64)      ,							dtype=np.floataa					)
														lowerCamelCase__ :     List[Any]	  =	0

														if str(__a					).startswith("""mps"""					):
																					lowerCamelCase__ :     Union[str, Any]	  =	torch.manual_seed(__a					)
														else:
																					lowerCamelCase__ :     Tuple	  =	torch.Generator(device=__a					).manual_seed(__a					)
														lowerCamelCase__ :     Dict	  =	{
														    """prompt""": """horse""",
														    """image""": init_image,
														    """mask_image""": mask,
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """num_inference_steps""": 2,
														    """guidance_scale""": 4.0,
														    """output_type""": """np""",
														}
														return inputs




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	"""cpu"""

														lowerCamelCase__ :     Tuple	  =	self.get_dummy_components()

														lowerCamelCase__ :     List[str]	  =	self.pipeline_class(**__a					)
														lowerCamelCase__ :     Tuple	  =	pipe.to(__a					)

														pipe.set_progress_bar_config(disable=__a					)

														lowerCamelCase__ :     Tuple	  =	pipe(**self.get_dummy_inputs(__a					)					)
														lowerCamelCase__ :     str	  =	output.images

														lowerCamelCase__ :     Dict	  =	pipe(
														    **self.get_dummy_inputs(__a					)      ,							return_dict=__a      ,							)[0]

														lowerCamelCase__ :     Optional[Any]	  =	image[0, -3:, -3:, -1]
														lowerCamelCase__ :     List[Any]	  =	image_from_tuple[0, -3:, -3:, -1]

														print(F'''image.shape {image.shape}'''					)

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

														lowerCamelCase__ :     Any	  =	np.array(
														    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

														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()}'''




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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




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




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Any	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

														lowerCamelCase__ :     Optional[int]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
														lowerCamelCase__ :     List[str]	  =	np.ones((768, 768)      ,							dtype=np.floataa					)
														lowerCamelCase__ :     str	  =	0

														lowerCamelCase__ :     Tuple	  =	"""a hat"""

														lowerCamelCase__ :     Union[str, Any]	  =	KandinskyPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-1-prior"""      ,							torch_dtype=torch.floataa					)
														pipe_prior.to(__a					)

														lowerCamelCase__ :     List[Any]	  =	KandinskyInpaintPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-1-inpaint"""      ,							torch_dtype=torch.floataa					)
														lowerCamelCase__ :     Any	  =	pipeline.to(__a					)
														pipeline.set_progress_bar_config(disable=__a					)

														lowerCamelCase__ :     Optional[Any]	  =	torch.Generator(device="""cpu"""					).manual_seed(0					)
														lowerCamelCase__  ,		lowerCamelCase__ :     int	  =	pipe_prior(
														    __a      ,							generator=__a      ,							num_inference_steps=5      ,							negative_prompt=""""""      ,							).to_tuple()

														lowerCamelCase__ :     Optional[Any]	  =	pipeline(
														    __a      ,							image=__a      ,							mask_image=__a      ,							image_embeds=__a      ,							negative_image_embeds=__a      ,							generator=__a      ,							num_inference_steps=100      ,							height=768      ,							width=768      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     List[Any]	  =	output.images[0]

														assert image.shape == (768, 768, 3)

														assert_mean_pixel_difference(__a      ,							__a					)













'''simple docstring'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

