






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       str     =   KandinskyVaaControlnetImgaImgPipeline
		__snake_case      :       List[Any]     =   ["image_embeds", "negative_image_embeds", "image", "hint"]
		__snake_case      :       int     =   ["image_embeds", "negative_image_embeds", "image", "hint"]
		__snake_case      :       str     =   [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__snake_case      :       Dict     =   False







		@property
		def  A__      (					self		):
						return 32







		@property
		def  A__      (					self		):
						return 32







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







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







		@property
		def  A__      (					self		):
						return 100







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

						_A	:				Union[str, Any]   =      {
						    '''in_channels''': 8,
						    # Out channels is double in channels because predicts mean and variance
						    '''out_channels''': 8,
						    '''addition_embed_type''': '''image_hint''',
						    '''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	:				Optional[Any]   =      UNetaDConditionModel(**A__		)
						return model







		@property
		def  A__      (					self		):
						return {
						    "block_out_channels": [32, 32, 64, 64],
						    "down_block_types": [
						        "DownEncoderBlock2D",
						        "DownEncoderBlock2D",
						        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}







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







		def  A__      (					self		):
						_A	:				List[Any]   =      self.dummy_unet
						_A	:				Optional[Any]   =      self.dummy_movq

						_A	:				Any   =      {
						    '''num_train_timesteps''': 1000,
						    '''beta_schedule''': '''linear''',
						    '''beta_start''': 0.0_00_85,
						    '''beta_end''': 0.0_12,
						    '''clip_sample''': False,
						    '''set_alpha_to_one''': False,
						    '''steps_offset''': 0,
						    '''prediction_type''': '''epsilon''',
						    '''thresholding''': False,
						}

						_A	:				Optional[Any]   =      DDIMScheduler(**A__		)

						_A	:				List[str]   =      {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components







		def  A__      (					self       ,A__       ,A__=0		):
						_A	:				Optional[Any]   =      floats_tensor((1, self.text_embedder_hidden_size)       ,rng=random.Random(A__		)		).to(A__		)
						_A	:				Optional[Any]   =      floats_tensor((1, self.text_embedder_hidden_size)       ,rng=random.Random(seed + 1		)		).to(
						    A__		)
						# create init_image
						_A	:				Optional[Any]   =      floats_tensor((1, 3, 64, 64)       ,rng=random.Random(A__		)		).to(A__		)
						_A	:				Any   =      image.cpu().permute(0       ,2       ,3       ,1		)[0]
						_A	:				Union[str, Any]   =      Image.fromarray(np.uinta(A__		)		).convert('''RGB'''		).resize((256, 256)		)
						# create hint
						_A	:				Optional[Any]   =      floats_tensor((1, 3, 64, 64)       ,rng=random.Random(A__		)		).to(A__		)

						if str(A__		).startswith('''mps'''		):
										_A	:				str   =      torch.manual_seed(A__		)
						else:
										_A	:				List[str]   =      torch.Generator(device=A__		).manual_seed(A__		)
						_A	:				List[str]   =      {
						    '''image''': init_image,
						    '''image_embeds''': image_embeds,
						    '''negative_image_embeds''': negative_image_embeds,
						    '''hint''': hint,
						    '''generator''': generator,
						    '''height''': 64,
						    '''width''': 64,
						    '''num_inference_steps''': 10,
						    '''guidance_scale''': 7.0,
						    '''strength''': 0.2,
						    '''output_type''': '''np''',
						}
						return inputs







		def  A__      (					self		):
						_A	:				Optional[Any]   =      '''cpu'''

						_A	:				Tuple   =      self.get_dummy_components()

						_A	:				int   =      self.pipeline_class(**A__		)
						_A	:				List[str]   =      pipe.to(A__		)

						pipe.set_progress_bar_config(disable=A__		)

						_A	:				str   =      pipe(**self.get_dummy_inputs(A__		)		)
						_A	:				Optional[Any]   =      output.images

						_A	:				Tuple   =      pipe(
						    **self.get_dummy_inputs(A__		)       ,return_dict=A__       ,)[0]

						_A	:				Optional[int]   =      image[0, -3:, -3:, -1]
						_A	:				Any   =      image_from_tuple[0, -3:, -3:, -1]

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

						_A	:				Optional[Any]   =      np.array(
						    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]		)
						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()}"""






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







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







		def  A__      (					self		):
						_A	:				List[Any]   =      load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''		)

						_A	:				List[str]   =      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
						_A	:				List[str]   =      init_image.resize((512, 512)		)

						_A	:				Dict   =      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinskyv22/hint_image_cat.png'''		)
						_A	:				Optional[int]   =      torch.from_numpy(np.array(A__		)		).float() / 2_55.0
						_A	:				int   =      hint.permute(2       ,0       ,1		).unsqueeze(0		)

						_A	:				List[Any]   =      '''A robot, 4k photo'''

						_A	:				Tuple   =      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-prior'''       ,torch_dtype=torch.floataa		)
						pipe_prior.to(A__		)

						_A	:				Tuple   =      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''       ,torch_dtype=torch.floataa		)
						_A	:				List[str]   =      pipeline.to(A__		)

						pipeline.set_progress_bar_config(disable=A__		)

						_A	:				List[str]   =      torch.Generator(device='''cpu'''		).manual_seed(0		)

						_A  ,				_A	:				Tuple   =      pipe_prior(
						    A__       ,image=A__       ,strength=0.85       ,generator=A__       ,negative_prompt=''''''       ,).to_tuple()

						_A	:				List[Any]   =      pipeline(
						    image=A__       ,image_embeds=A__       ,negative_image_embeds=A__       ,hint=A__       ,generator=A__       ,num_inference_steps=100       ,height=512       ,width=512       ,strength=0.5       ,output_type='''np'''       ,)

						_A	:				Optional[Any]   =      output.images[0]

						assert image.shape == (512, 512, 3)

						assert_mean_pixel_difference(A__       ,A__		)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



