





'''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       UpperCAmelCase (						lowercase__			,       unittest.TestCase  ):
		UpperCAmelCase					:	Tuple 			=		KandinskyVaaControlnetImgaImgPipeline
		UpperCAmelCase					:	int 			=		['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
		UpperCAmelCase					:	Union[str, Any] 			=		['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
		UpperCAmelCase					:	Any 			=		[
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''strength''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		UpperCAmelCase					:	Optional[int] 			=		False




		@property
		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						return 3_2




		@property
		def 				UpperCAmelCase__   (self      :	int				)					-> List[str]:
						return 3_2




		@property
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[str]:
						return self.time_input_dim




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> List[Any]:
						return self.time_input_dim * 4




		@property
		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
						return 1_0_0




		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						torch.manual_seed(0				)

						lowercase         = {
						    "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,
						}

						lowercase         = UNetaDConditionModel(**__lowerCamelCase				)
						return model




		@property
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> str:
						return {
						    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}




		@property
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[Any]:
						torch.manual_seed(0				)
						lowercase         = VQModel(**self.dummy_movq_kwargs				)
						return model




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Any:
						lowercase         = self.dummy_unet
						lowercase         = self.dummy_movq

						lowercase         = {
						    "num_train_timesteps": 1_0_0_0,
						    "beta_schedule": "linear",
						    "beta_start": 0.0_0_0_8_5,
						    "beta_end": 0.0_1_2,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						lowercase         = DDIMScheduler(**__lowerCamelCase				)

						lowercase         = {
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	str							,			A__      :	Union[str, Any]=0				)					-> str:
						lowercase         = floats_tensor((1, self.text_embedder_hidden_size)							,			rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowercase         = floats_tensor((1, self.text_embedder_hidden_size)							,			rng=random.Random(seed + 1				)				).to(
						    __lowerCamelCase				)
						# create init_image
						lowercase         = floats_tensor((1, 3, 6_4, 6_4)							,			rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowercase         = image.cpu().permute(0							,			2							,			3							,			1				)[0]
						lowercase         = Image.fromarray(np.uinta(__lowerCamelCase				)				).convert("RGB"				).resize((2_5_6, 2_5_6)				)
						# create hint
						lowercase         = floats_tensor((1, 3, 6_4, 6_4)							,			rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)

						if str(__lowerCamelCase				).startswith("mps"				):
										lowercase         = torch.manual_seed(__lowerCamelCase				)
						else:
										lowercase         = torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
						lowercase         = {
						    "image": init_image,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "hint": hint,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "num_inference_steps": 1_0,
						    "guidance_scale": 7.0,
						    "strength": 0.2,
						    "output_type": "np",
						}
						return inputs





		def 				UpperCAmelCase__   (self      :	Any				)					-> List[str]:
						lowercase         = "cpu"

						lowercase         = self.get_dummy_components()

						lowercase         = self.pipeline_class(**__lowerCamelCase				)
						lowercase         = pipe.to(__lowerCamelCase				)

						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowercase         = pipe(**self.get_dummy_inputs(__lowerCamelCase				)				)
						lowercase         = output.images

						lowercase         = pipe(
						    **self.get_dummy_inputs(__lowerCamelCase				)							,			return_dict=__lowerCamelCase							,			)[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)

						lowercase         = np.array(
						    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]				)
						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 				UpperCAmelCase__   (self      :	Dict				)					-> Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





		def 				UpperCAmelCase__   (self      :	int				)					-> Union[str, Any]:
						lowercase         = load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"				)

						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
						lowercase         = init_image.resize((5_1_2, 5_1_2)				)

						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/hint_image_cat.png"				)
						lowercase         = torch.from_numpy(np.array(__lowerCamelCase				)				).float() / 2_5_5.0
						lowercase         = hint.permute(2							,			0							,			1				).unsqueeze(0				)

						lowercase         = "A robot, 4k photo"

						lowercase         = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-prior"							,			torch_dtype=torch.floataa				)
						pipe_prior.to(__lowerCamelCase				)

						lowercase         = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-controlnet-depth"							,			torch_dtype=torch.floataa				)
						lowercase         = pipeline.to(__lowerCamelCase				)

						pipeline.set_progress_bar_config(disable=__lowerCamelCase				)

						lowercase         = torch.Generator(device="cpu"				).manual_seed(0				)

						lowercase         = pipe_prior(
						    __lowerCamelCase							,			image=__lowerCamelCase							,			strength=0.8_5							,			generator=__lowerCamelCase							,			negative_prompt=""							,			).to_tuple()

						lowercase         = pipeline(
						    image=__lowerCamelCase							,			image_embeds=__lowerCamelCase							,			negative_image_embeds=__lowerCamelCase							,			hint=__lowerCamelCase							,			generator=__lowerCamelCase							,			num_inference_steps=1_0_0							,			height=5_1_2							,			width=5_1_2							,			strength=0.5							,			output_type="np"							,			)

						lowercase         = output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						assert_mean_pixel_difference(__lowerCamelCase							,			__lowerCamelCase				)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




