






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

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




							@property
							def 		lowerCamelCase_    (							self:    List[str]					):
														return 32




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return 32




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




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return self.time_input_dim * 4




							@property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														return 100




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

														lowerCamelCase__ :     Dict	  =	{
														    """in_channels""": 9,
														    # 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,
														}

														lowerCamelCase__ :     Union[str, Any]	  =	UNetaDConditionModel(**UpperCamelCase__					)
														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:    Any					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     List[Any]	  =	VQModel(**self.dummy_movq_kwargs					)
														return model




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Any	  =	self.dummy_unet
														lowerCamelCase__ :     List[Any]	  =	self.dummy_movq

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

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

														return components




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

														if str(UpperCamelCase__					).startswith("""mps"""					):
																					lowerCamelCase__ :     str	  =	torch.manual_seed(UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     List[Any]	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	{
														    """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[int]					):
														lowerCamelCase__ :     Any	  =	"""cpu"""

														lowerCamelCase__ :     Any	  =	self.get_dummy_components()

														lowerCamelCase__ :     Optional[Any]	  =	self.pipeline_class(**UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	pipe.to(UpperCamelCase__					)

														pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	pipe(**self.get_dummy_inputs(UpperCamelCase__					)					)
														lowerCamelCase__ :     Optional[Any]	  =	output.images

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

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

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

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

														lowerCamelCase__ :     int	  =	np.array(
														    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]					)

														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:    Any					):
														super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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




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




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Any	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""					)

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

														lowerCamelCase__ :     Dict	  =	"""a hat"""

														lowerCamelCase__ :     Optional[int]	  =	KandinskyVaaPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-prior"""      ,							torch_dtype=torch.floataa					)
														pipe_prior.to(UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	KandinskyVaaInpaintPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""      ,							torch_dtype=torch.floataa					)
														lowerCamelCase__ :     str	  =	pipeline.to(UpperCamelCase__					)
														pipeline.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	torch.Generator(device="""cpu"""					).manual_seed(0					)
														lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	pipe_prior(
														    UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=5      ,							negative_prompt=""""""      ,							).to_tuple()

														lowerCamelCase__ :     Tuple	  =	pipeline(
														    image=UpperCamelCase__      ,							mask_image=UpperCamelCase__      ,							image_embeds=UpperCamelCase__      ,							negative_image_embeds=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=100      ,							height=768      ,							width=768      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     Tuple	  =	output.images[0]

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

														assert_mean_pixel_difference(UpperCamelCase__      ,							UpperCamelCase__					)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

