






"""simple docstring"""

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 		__lowerCamelCase				( A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				Optional[int]            =							KandinskyVaaControlnetImgaImgPipeline
		a_	:				str            =							["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
		a_	:				List[str]            =							["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
		a_	:				Union[str, Any]            =							[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a_	:				int            =							False





		@property
		def    lowerCamelCase       (   self							:	List[Any]	):
				return 32





		@property
		def    lowerCamelCase       (   self							:	List[str]	):
				return 32





		@property
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				return self.time_input_dim





		@property
		def    lowerCamelCase       (   self							:	List[str]	):
				return self.time_input_dim * 4





		@property
		def    lowerCamelCase       (   self							:	Optional[int]	):
				return 1_00





		@property
		def    lowerCamelCase       (   self							:	Optional[int]	):
				torch.manual_seed(0	)

				lowerCAmelCase_   :							int									=			{
				    "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,
				}

				lowerCAmelCase_   :							Dict									=			UNetaDConditionModel(**a_	)
				return model





		@property
		def    lowerCamelCase       (   self							:	List[str]	):
				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    lowerCamelCase       (   self							:	int	):
				torch.manual_seed(0	)
				lowerCAmelCase_   :							int									=			VQModel(**self.dummy_movq_kwargs	)
				return model





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Any									=			self.dummy_unet
				lowerCAmelCase_   :							Tuple									=			self.dummy_movq

				lowerCAmelCase_   :							Any									=			{
				    "num_train_timesteps": 10_00,
				    "beta_schedule": "linear",
				    "beta_start": 0.00085,
				    "beta_end": 0.012,
				    "clip_sample": False,
				    "set_alpha_to_one": False,
				    "steps_offset": 0,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				}

				lowerCAmelCase_   :							Dict									=			DDIMScheduler(**a_	)

				lowerCAmelCase_   :							Optional[int]									=			{
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}

				return components





		def    lowerCamelCase       (   self							:	str						,			a_							:	Dict						,			a_							:	str=0	):
				lowerCAmelCase_   :							Dict									=			floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							Dict									=			floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1	)	).to(
				    a_	)
				# create init_image
				lowerCAmelCase_   :							List[str]									=			floats_tensor((1, 3, 64, 64)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							Optional[Any]									=			image.cpu().permute(0						,			2						,			3						,			1	)[0]
				lowerCAmelCase_   :							Optional[Any]									=			Image.fromarray(np.uinta(a_	)	).convert("RGB"	).resize((2_56, 2_56)	)
				# create hint
				lowerCAmelCase_   :							List[str]									=			floats_tensor((1, 3, 64, 64)						,			rng=random.Random(a_	)	).to(a_	)

				if str(a_	).startswith("mps"	):
						lowerCAmelCase_   :							Union[str, Any]									=			torch.manual_seed(a_	)
				else:
						lowerCAmelCase_   :							Dict									=			torch.Generator(device=a_	).manual_seed(a_	)
				lowerCAmelCase_   :							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    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Optional[Any]									=			"cpu"

				lowerCAmelCase_   :							Any									=			self.get_dummy_components()

				lowerCAmelCase_   :							Optional[int]									=			self.pipeline_class(**a_	)
				lowerCAmelCase_   :							str									=			pipe.to(a_	)

				pipe.set_progress_bar_config(disable=a_	)

				lowerCAmelCase_   :							Any									=			pipe(**self.get_dummy_inputs(a_	)	)
				lowerCAmelCase_   :							str									=			output.images

				lowerCAmelCase_   :							List[Any]									=			pipe(
				    **self.get_dummy_inputs(a_	)						,			return_dict=a_						,			)[0]

				lowerCAmelCase_   :							Tuple									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							Any									=			image_from_tuple[0, -3:, -3:, -1]

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

				lowerCAmelCase_   :							Dict									=			np.array(
				    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]	)
				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 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






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





		def    lowerCamelCase       (   self							:	int	):
				lowerCAmelCase_   :							Optional[int]									=			load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"	)

				lowerCAmelCase_   :							List[Any]									=			load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
				lowerCAmelCase_   :							List[str]									=			init_image.resize((5_12, 5_12)	)

				lowerCAmelCase_   :							Any									=			load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinskyv22/hint_image_cat.png"	)
				lowerCAmelCase_   :							Dict									=			torch.from_numpy(np.array(a_	)	).float() / 255.0
				lowerCAmelCase_   :							Optional[int]									=			hint.permute(2						,			0						,			1	).unsqueeze(0	)

				lowerCAmelCase_   :							Any									=			"A robot, 4k photo"

				lowerCAmelCase_   :							Union[str, Any]									=			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-prior"						,			torch_dtype=torch.floataa	)
				pipe_prior.to(a_	)

				lowerCAmelCase_   :							Optional[int]									=			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-controlnet-depth"						,			torch_dtype=torch.floataa	)
				lowerCAmelCase_   :							List[Any]									=			pipeline.to(a_	)

				pipeline.set_progress_bar_config(disable=a_	)

				lowerCAmelCase_   :							Optional[Any]									=			torch.Generator(device="cpu"	).manual_seed(0	)

				lowerCAmelCase_				,	lowerCAmelCase_   :							List[Any]									=			pipe_prior(
				    a_						,			image=a_						,			strength=0.85						,			generator=a_						,			negative_prompt=""						,			).to_tuple()

				lowerCAmelCase_   :							List[Any]									=			pipeline(
				    image=a_						,			image_embeds=a_						,			negative_image_embeds=a_						,			hint=a_						,			generator=a_						,			num_inference_steps=1_00						,			height=5_12						,			width=5_12						,			strength=0.5						,			output_type="np"						,			)

				lowerCAmelCase_   :							Dict									=			output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				assert_mean_pixel_difference(a_						,			a_	)








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				str            =							["""image_processor""", """tokenizer"""]
		a_	:				List[str]            =							"""ViTImageProcessor"""
		a_	:				List[str]            =							("""CLIPTokenizer""", """CLIPTokenizerFast""")





		def __init__(   self							:	List[str]						,			a_							:	str=None						,			a_							:	Dict=None						,			**a_							:	List[Any]	):
				lowerCAmelCase_   :							int									=			None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
						    " instead."						,			a_						,			)
						lowerCAmelCase_   :							Union[str, Any]									=			kwargs.pop("feature_extractor"	)

				lowerCAmelCase_   :							Dict									=			image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError("You need to specify an `image_processor`."	)
				if tokenizer is None:
						raise ValueError("You need to specify a `tokenizer`."	)

				super().__init__(a_						,			a_	)





		def __call__(   self							:	Union[str, Any]						,			a_							:	Any=None						,			a_							:	Dict=None						,			a_							:	List[str]=None						,			a_							:	str=None						,			**a_							:	Any	):
				if text is None and visual_prompt is None and images is None:
						raise ValueError("You have to specify either text, visual prompt or images."	)

				if text is not None and visual_prompt is not None:
						raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."	)

				if text is not None:
						lowerCAmelCase_   :							Optional[Any]									=			self.tokenizer(a_						,			return_tensors=a_						,			**a_	)

				if visual_prompt is not None:
						lowerCAmelCase_   :							Optional[Any]									=			self.image_processor(a_						,			return_tensors=a_						,			**a_	)

				if images is not None:
						lowerCAmelCase_   :							List[str]									=			self.image_processor(a_						,			return_tensors=a_						,			**a_	)

				if visual_prompt is not None and images is not None:
						lowerCAmelCase_   :							Union[str, Any]									=			{
						    "pixel_values": image_features.pixel_values,
						    "conditional_pixel_values": prompt_features.pixel_values,
						}
						return encoding
				elif text is not None and images is not None:
						lowerCAmelCase_   :							Optional[int]									=			image_features.pixel_values
						return encoding
				elif text is not None:
						return encoding
				elif visual_prompt is not None:
						lowerCAmelCase_   :							Dict									=			{
						    "conditional_pixel_values": prompt_features.pixel_values,
						}
						return encoding
				else:
						return BatchEncoding(data=dict(**a_	)						,			tensor_type=a_	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			*a_							:	Optional[Any]						,			**a_							:	List[str]	):
				return self.tokenizer.batch_decode(*a_						,			**a_	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			*a_							:	Tuple						,			**a_							:	Tuple	):
				return self.tokenizer.decode(*a_						,			**a_	)





		@property
		def    lowerCamelCase       (   self							:	List[Any]	):
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,			a_						,			)
				return self.image_processor_class





		@property
		def    lowerCamelCase       (   self							:	Dict	):
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,			a_						,			)
				return self.image_processor
