


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				KandinskyVaaInpaintPipeline
		__UpperCAmelCase		:			List[Any]										=				['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 			__UpperCAmelCase       (  self      ):
				return 32

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 32

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

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

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 100

		@property
		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )

				__a           =			{
				    '''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,
				}

				__a           =			UNetaDConditionModel(**_a      )
				return model

		@property
		def 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			VQModel(**self.dummy_movq_kwargs      )
				return model

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.dummy_unet
				__a           =			self.dummy_movq

				__a           =			DDIMScheduler(
				    num_train_timesteps=1_000							, beta_schedule='''linear'''							, beta_start=0.0_0085							, beta_end=0.012							, clip_sample=_a							, set_alpha_to_one=_a							, steps_offset=1							, prediction_type='''epsilon'''							, thresholding=_a							, )

				__a           =			{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''movq''': movq,
				}

				return components

		def 			__UpperCAmelCase       (  self							, _a							, _a=0      ):
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(seed + 1      )      ).to(
				    _a      )
				# create init_image
				__a           =			floats_tensor((1, 3, 64, 64)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			image.cpu().permute(0							, 2							, 3							, 1      )[0]
				__a           =			Image.fromarray(np.uinta(_a      )      ).convert('''RGB'''      ).resize((256, 256)      )
				# create mask
				__a           =			np.ones((64, 64)							, dtype=np.floataa      )
				__a           =			0

				if str(_a      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(_a      )
				else:
						__a           =			torch.Generator(device=_a      ).manual_seed(_a      )
				__a           =			{
				    '''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 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''

				__a           =			self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=_a      )

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

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

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

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

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

				__a           =			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 			__UpperCAmelCase       (  self      ):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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


		'''simple docstring'''



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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

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

				__a           =			'''a hat'''

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

				__a           =			KandinskyVaaInpaintPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''							, torch_dtype=torch.floataa      )
				__a           =			pipeline.to(_a      )
				pipeline.set_progress_bar_config(disable=_a      )

				__a           =			torch.Generator(device='''cpu'''      ).manual_seed(0      )
				__a      ,     __a           =			pipe_prior(
				    _a							, generator=_a							, num_inference_steps=5							, negative_prompt=''''''							, ).to_tuple()

				__a           =			pipeline(
				    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'''							, )

				__a           =			output.images[0]

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

				assert_mean_pixel_difference(_a							, _a      )










"""simple docstring"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






