"""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       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    KandinskyVaaInpaintPipeline
					UpperCamelCase_ :	List[str]			=    ["image_embeds", "negative_image_embeds", "image", "mask_image"]
					UpperCamelCase_ :	Dict			=    [
					    "image_embeds",
					    "negative_image_embeds",
					    "image",
					    "mask_image",
					]
					UpperCamelCase_ :	List[str]			=    [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					UpperCamelCase_ :	Dict			=    False




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




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return 32




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							return self.time_input_dim




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




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							return 100




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							torch.manual_seed(0     )

							lowerCAmelCase__        =   {
							    """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__        =   UNetaDConditionModel(**snake_case__     )
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   VQModel(**self.dummy_movq_kwargs     )
							return model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   self.dummy_unet
							lowerCAmelCase__        =   self.dummy_movq

							lowerCAmelCase__        =   DDIMScheduler(
							    num_train_timesteps=1000  ,						beta_schedule="""linear"""  ,						beta_start=0.0_0085  ,						beta_end=0.012  ,						clip_sample=snake_case__  ,						set_alpha_to_one=snake_case__  ,						steps_offset=1  ,						prediction_type="""epsilon"""  ,						thresholding=snake_case__  ,						)

							lowerCAmelCase__        =   {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




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

							if str(snake_case__     ).startswith("""mps"""     ):
									lowerCAmelCase__        =   torch.manual_seed(snake_case__     )
							else:
									lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
							lowerCAmelCase__        =   {
							    """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 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   """cpu"""

							lowerCAmelCase__        =   self.get_dummy_components()

							lowerCAmelCase__        =   self.pipeline_class(**snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )

							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   pipe(**self.get_dummy_inputs(snake_case__     )     )
							lowerCAmelCase__        =   output.images

							lowerCAmelCase__        =   pipe(
							    **self.get_dummy_inputs(snake_case__     )  ,						return_dict=snake_case__  ,						)[0]

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

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

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

							lowerCAmelCase__        =   np.array(
							    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]     )

							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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3     )

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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""     )

							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""     )
							lowerCAmelCase__        =   np.ones((768, 768)  ,						dtype=np.floataa     )
							lowerCAmelCase__        =   0

							lowerCAmelCase__        =   """a hat"""

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

							lowerCAmelCase__        =   KandinskyVaaInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""  ,						torch_dtype=torch.floataa     )
							lowerCAmelCase__        =   pipeline.to(snake_case__     )
							pipeline.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   torch.Generator(device="""cpu"""     ).manual_seed(0     )
							lowerCAmelCase__   ,					lowerCAmelCase__        =   pipe_prior(
							    snake_case__  ,						generator=snake_case__  ,						num_inference_steps=5  ,						negative_prompt=""""""  ,						).to_tuple()

							lowerCAmelCase__        =   pipeline(
							    image=snake_case__  ,						mask_image=snake_case__  ,						image_embeds=snake_case__  ,						negative_image_embeds=snake_case__  ,						generator=snake_case__  ,						num_inference_steps=100  ,						height=768  ,						width=768  ,						output_type="""np"""  ,						)

							lowerCAmelCase__        =   output.images[0]

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

							assert_mean_pixel_difference(snake_case__  ,						snake_case__     )





"""simple docstring"""




from math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if num <= 0:
				raise ValueError("""math domain error"""     )
		if num > 1_71.5:
				raise OverflowError("""math range error"""     )
		elif num - int(lowerCamelCase__     ) not in (0, 0.5):
				raise NotImplementedError("""num must be an integer or a half-integer"""     )
		elif num == 0.5:
				return sqrt(lowerCamelCase__     )
		else:
				return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			__lowerCAmelCase    :							Dict													=						1.0
			while num:
						__lowerCAmelCase    :							Any													=						float(input("Gamma of: "))
						print(F"gamma({num}) = {gamma(num)}")
						print("\nEnter 0 to exit...")




