



"""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 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					KandinskyVaaControlnetImgaImgPipeline
       UpperCamelCase						=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase						=					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase						=					[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase						=					False



       @property
       def 					lowercase__    (							self      :  str   )			->						str:


             '''simple docstring'''




             return 32



       @property
       def 					lowercase__    (							self      :  Union[str, Any]   )			->						Optional[Any]:


             '''simple docstring'''




             return 32



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             return self.time_input_dim



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						str:


             '''simple docstring'''




             return self.time_input_dim * 4



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


             '''simple docstring'''




             return 100



       @property
       def 					lowercase__    (							self      :  Optional[int]   )			->						List[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(**_UpperCAmelCase   )
             return model



       @property
       def 					lowercase__    (							self      :  Optional[int]   )			->						Optional[int]:


             '''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 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




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



       def 					lowercase__    (							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.dummy_unet
             UpperCAmelCase_      			=							self.dummy_movq

             UpperCAmelCase_      			=							{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0085,
                 "beta_end": 0.012,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             UpperCAmelCase_      			=							DDIMScheduler(**_UpperCAmelCase   )

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

             return components



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Optional[int]		,    _UpperCAmelCase      :  List[Any]=0   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							floats_tensor((1, self.text_embedder_hidden_size)		,    rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
             UpperCAmelCase_      			=							floats_tensor((1, self.text_embedder_hidden_size)		,    rng=random.Random(seed + 1   )   ).to(
                 _UpperCAmelCase   )
             # create init_image
             UpperCAmelCase_      			=							floats_tensor((1, 3, 64, 64)		,    rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
             UpperCAmelCase_      			=							image.cpu().permute(0		,    2		,    3		,    1   )[0]
             UpperCAmelCase_      			=							Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert("RGB"   ).resize((256, 256)   )
             # create hint
             UpperCAmelCase_      			=							floats_tensor((1, 3, 64, 64)		,    rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

             if str(_UpperCAmelCase   ).startswith("mps"   ):
                   UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             else:
                   UpperCAmelCase_      			=							torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							{
                 "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 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							"cpu"

             UpperCAmelCase_      			=							self.get_dummy_components()

             UpperCAmelCase_      			=							self.pipeline_class(**_UpperCAmelCase   )
             UpperCAmelCase_      			=							pipe.to(_UpperCAmelCase   )

             pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
             UpperCAmelCase_      			=							output.images

             UpperCAmelCase_      			=							pipe(
                 **self.get_dummy_inputs(_UpperCAmelCase   )		,    return_dict=_UpperCAmelCase		,    )[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(
                 [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]   )
             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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       def 					lowercase__    (							self      :  Tuple   )			->						Tuple:


             '''simple docstring'''




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







       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"   )

             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
             UpperCAmelCase_      			=							init_image.resize((512, 512)   )

             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(_UpperCAmelCase   )   ).float() / 255.0
             UpperCAmelCase_      			=							hint.permute(2		,    0		,    1   ).unsqueeze(0   )

             UpperCAmelCase_      			=							"A robot, 4k photo"

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

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

             pipeline.set_progress_bar_config(disable=_UpperCAmelCase   )

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

             UpperCAmelCase_			,   UpperCAmelCase_      			=							pipe_prior(
                 _UpperCAmelCase		,    image=_UpperCAmelCase		,    strength=0.85		,    generator=_UpperCAmelCase		,    negative_prompt=""		,    ).to_tuple()

             UpperCAmelCase_      			=							pipeline(
                 image=_UpperCAmelCase		,    image_embeds=_UpperCAmelCase		,    negative_image_embeds=_UpperCAmelCase		,    hint=_UpperCAmelCase		,    generator=_UpperCAmelCase		,    num_inference_steps=100		,    height=512		,    width=512		,    strength=0.5		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

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

             assert_mean_pixel_difference(_UpperCAmelCase		,    _UpperCAmelCase   )









"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


lowerCamelCase       	=						logging.get_logger(__name__)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def __init__(							self      :  Union[str, Any]		,    *_UpperCAmelCase      :  List[Any]		,    **_UpperCAmelCase      :  Union[str, Any]   )			->						None:


             '''simple docstring'''




             warnings.warn(
                 "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use FlavaImageProcessor instead."		,    _UpperCAmelCase		,    )
             super().__init__(*_UpperCAmelCase		,    **_UpperCAmelCase   )




