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


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												return 32


						@property
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												return 32


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												return self.time_input_dim


						@property
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												return self.time_input_dim * 4


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												return 100


						@property
						def        __A       (       self	:				int						):





												'''simple docstring'''





												torch.manual_seed(0						)

												SCREAMING_SNAKE_CASE							:    List[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,
												}

												SCREAMING_SNAKE_CASE							:    Dict							=							UNetaDConditionModel(**UpperCamelCase__						)
												return model


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												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        __A       (       self	:				List[str]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.dummy_unet
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_movq

												SCREAMING_SNAKE_CASE							:    int							=							DDIMScheduler(
												    num_train_timesteps=1000    ,		beta_schedule='''linear'''    ,		beta_start=0.0_0085    ,		beta_end=0.012    ,		clip_sample=UpperCamelCase__    ,		set_alpha_to_one=UpperCamelCase__    ,		steps_offset=1    ,		prediction_type='''epsilon'''    ,		thresholding=UpperCamelCase__    ,		)

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

												return components


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				Optional[int]=0						):





												'''simple docstring'''





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

												if str(UpperCamelCase__						).startswith('''mps'''						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							torch.manual_seed(UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							'''cpu'''

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							pipe(**self.get_dummy_inputs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    Any							=							output.images

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							pipe(
												    **self.get_dummy_inputs(UpperCamelCase__						)    ,		return_dict=UpperCamelCase__    ,		)[0]

												SCREAMING_SNAKE_CASE							:    Dict							=							image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE							:    Any							=							image_from_tuple[0, -3:, -3:, -1]

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

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

												SCREAMING_SNAKE_CASE							:    Tuple							=							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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






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


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''						)

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

												SCREAMING_SNAKE_CASE							:    Any							=							'''a hat'''

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							KandinskyVaaPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-prior'''    ,		torch_dtype=torch.floataa						)
												pipe_prior.to(UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    Any							=							torch.Generator(device='''cpu'''						).manual_seed(0						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							pipe_prior(
												    UpperCamelCase__    ,		generator=UpperCamelCase__    ,		num_inference_steps=5    ,		negative_prompt=''''''    ,		).to_tuple()

												SCREAMING_SNAKE_CASE							:    List[str]							=							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'''    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							output.images[0]

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

												assert_mean_pixel_difference(UpperCamelCase__    ,		UpperCamelCase__						)






import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





