
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Any	=		KandinskyVaaControlnetPipeline
						UpperCamelCase_							: List[Any]	=		["image_embeds", "negative_image_embeds", "hint"]
						UpperCamelCase_							: Optional[Any]	=		["image_embeds", "negative_image_embeds", "hint"]
						UpperCamelCase_							: List[Any]	=		[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCamelCase_							: str	=		False

						@property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								return 32

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								return 32

						@property
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								return self.time_input_dim

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								return self.time_input_dim * 4

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								return 1_00

						@property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								torch.manual_seed(0				)

								_UpperCamelCase						    =	{
								    """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,
								}

								_UpperCamelCase						    =	UNetaDConditionModel(**a				)
								return model

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								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				)					->      Any:


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	VQModel(**self.dummy_movq_kwargs				)
								return model

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.dummy_unet
								_UpperCamelCase						    =	self.dummy_movq

								_UpperCamelCase						    =	DDIMScheduler(
								    num_train_timesteps=10_00       ,							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       ,							)

								_UpperCamelCase						    =	{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}
								return components

						def  A_				(			self       ,							a       ,							a=0				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(a				)				).to(a				)
								_UpperCamelCase						    =	floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(seed + 1				)				).to(
								    a				)

								# create hint
								_UpperCamelCase						    =	floats_tensor((1, 3, 64, 64)       ,							rng=random.Random(a				)				).to(a				)

								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """hint""": hint,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """guidance_scale""": 4.0,
								    """num_inference_steps""": 2,
								    """output_type""": """np""",
								}
								return inputs







						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""

								_UpperCamelCase						    =	self.get_dummy_components()

								_UpperCamelCase						    =	self.pipeline_class(**a				)
								_UpperCamelCase						    =	pipe.to(a				)

								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	pipe(**self.get_dummy_inputs(a				)				)
								_UpperCamelCase						    =	output.images

								_UpperCamelCase						    =	pipe(
								    **self.get_dummy_inputs(a				)       ,							return_dict=a       ,							)[0]

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

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

								_UpperCamelCase						    =	np.array(
								    [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]				)

								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	):

						def  A_				(			self				)					->      str:


								'''simple docstring'''

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







						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""				)

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/hint_image_cat.png"""				)
								_UpperCamelCase						    =	torch.from_numpy(np.array(a				)				).float() / 255.0
								_UpperCamelCase						    =	hint.permute(2       ,							0       ,							1				).unsqueeze(0				)

								_UpperCamelCase						    =	KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""       ,							torch_dtype=torch.floataa				)
								pipe_prior.to(a				)

								_UpperCamelCase						    =	KandinskyVaaControlnetPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,							torch_dtype=torch.floataa				)
								_UpperCamelCase						    =	pipeline.to(a				)
								pipeline.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	"""A robot, 4k photo"""

								_UpperCamelCase						    =	torch.Generator(device="""cuda"""				).manual_seed(0				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	pipe_prior(
								    a       ,							generator=a       ,							num_inference_steps=5       ,							negative_prompt=""""""       ,							).to_tuple()

								_UpperCamelCase						    =	torch.Generator(device="""cuda"""				).manual_seed(0				)
								_UpperCamelCase						    =	pipeline(
								    image_embeds=a       ,							negative_image_embeds=a       ,							hint=a       ,							generator=a       ,							num_inference_steps=1_00       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	output.images[0]

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

								assert_mean_pixel_difference(a       ,							a				)





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	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       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	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				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

								return self.tokenizer.decode(*a       ,							**a				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								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  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							a       ,							)
								return self.image_processor



