
"""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__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Any				   =	KandinskyVaaInpaintPipeline
		A_						:       Tuple				   =	['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:       List[str]				   =	[
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:       List[str]				   =	[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:       Tuple				   =	False

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

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

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

		@property
		def 							__lowerCamelCase							(      self ):
			return 1_00

		@property
		def 							__lowerCamelCase							(      self ):
			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': '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			:						Optional[Any]									=      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE )
			return model

		@property
		def 							__lowerCamelCase							(      self ):
			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 ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						str									=      VQModel(**self.dummy_movq_kwargs )
			return model

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      self.dummy_unet
			__lowerCAmelCase			:						List[str]									=      self.dummy_movq

			__lowerCAmelCase			:						Union[str, Any]									=      DDIMScheduler(
			    num_train_timesteps=10_00       ,		beta_schedule='linear'       ,		beta_start=0.0_0085       ,		beta_end=0.012       ,		clip_sample=_SCREAMING_SNAKE_CASE       ,		set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,		steps_offset=1       ,		prediction_type='epsilon'       ,		thresholding=_SCREAMING_SNAKE_CASE       ,		)

			__lowerCAmelCase			:						str									=      {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0 ):
			__lowerCAmelCase			:						List[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(seed + 1 ) ).to(
			    _SCREAMING_SNAKE_CASE )
			# create init_image
			__lowerCAmelCase			:						Tuple									=      floats_tensor((1, 3, 64, 64)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      image.cpu().permute(0       ,		2       ,		3       ,		1 )[0]
			__lowerCAmelCase			:						List[Any]									=      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((2_56, 2_56) )
			# create mask
			__lowerCAmelCase			:						Optional[Any]									=      np.ones((64, 64)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						Dict									=      0

			if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
				__lowerCAmelCase			:						Dict									=      torch.manual_seed(_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						Optional[int]									=      torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      {
			    '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 ):
			__lowerCAmelCase			:						Dict									=      'cpu'

			__lowerCAmelCase			:						Union[str, Any]									=      self.get_dummy_components()

			__lowerCAmelCase			:						List[Any]									=      self.pipeline_class(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      pipe.to(_SCREAMING_SNAKE_CASE )

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Union[str, Any]									=      output.images

			__lowerCAmelCase			:						Optional[Any]									=      pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )       ,		return_dict=_SCREAMING_SNAKE_CASE       ,		)[0]

			__lowerCAmelCase			:						str									=      image[0, -3:, -3:, -1]
			__lowerCAmelCase			:						int									=      image_from_tuple[0, -3:, -3:, -1]

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

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

			__lowerCAmelCase			:						List[Any]									=      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 							__lowerCamelCase							(      self ):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




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

		def 							__lowerCamelCase							(      self ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_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			:						Union[str, Any]									=      np.ones((7_68, 7_68)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						List[str]									=      0

			__lowerCAmelCase			:						Dict									=      'a hat'

			__lowerCAmelCase			:						Optional[int]									=      KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'       ,		torch_dtype=torch.floataa )
			pipe_prior.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						str									=      KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,		torch_dtype=torch.floataa )
			__lowerCAmelCase			:						str									=      pipeline.to(_SCREAMING_SNAKE_CASE )
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      torch.Generator(device='cpu' ).manual_seed(0 )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      pipe_prior(
			    _SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=5       ,		negative_prompt=''       ,		).to_tuple()

			__lowerCAmelCase			:						int									=      pipeline(
			    image=_SCREAMING_SNAKE_CASE       ,		mask_image=_SCREAMING_SNAKE_CASE       ,		image_embeds=_SCREAMING_SNAKE_CASE       ,		negative_image_embeds=_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=1_00       ,		height=7_68       ,		width=7_68       ,		output_type='np'       ,		)

			__lowerCAmelCase			:						Optional[int]									=      output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )




"""simple docstring"""






from __future__ import annotations

_lowercase : Dict      						=						1.6_021E-19  # units = C






def      snake_case__   (		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		):
			"""simple docstring"""
			if (conductivity, electron_conc, mobility).count(0	) != 1:
						raise ValueError('''You cannot supply more or less than 2 values'''	)
			elif conductivity < 0:
						raise ValueError('''Conductivity cannot be negative'''	)
			elif electron_conc < 0:
						raise ValueError('''Electron concentration cannot be negative'''	)
			elif mobility < 0:
						raise ValueError('''mobility cannot be negative'''	)
			elif conductivity == 0:
						return (
						    "conductivity",
						    mobility * electron_conc * ELECTRON_CHARGE,
						)
			elif electron_conc == 0:
						return (
						    "electron_conc",
						    conductivity / (mobility * ELECTRON_CHARGE),
						)
			else:
						return (
						    "mobility",
						    conductivity / (electron_conc * ELECTRON_CHARGE),
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




