
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 (      __snake_case	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  KandinskyVaaInpaintPipeline
     SCREAMING_SNAKE_CASE_		   =  ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     SCREAMING_SNAKE_CASE_		   =  [
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     SCREAMING_SNAKE_CASE_		   =  [
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     SCREAMING_SNAKE_CASE_		   =  False






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




           """simple docstring"""







           return 3_2






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           return 3_2






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           return self.time_input_dim






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




           """simple docstring"""







           return self.time_input_dim * 4






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




           """simple docstring"""







           return 1_0_0






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           torch.manual_seed(0  )

           UpperCamelCase__    :   List[Any]					=				{
               "in_channels": 9,
               # Out channels is double in channels because predicts mean and variance
               "out_channels": 8,
               "addition_embed_type": "image",
               "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__    :   Optional[int]					=				UNetaDConditionModel(**A_  )
           return model






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return {
               "block_out_channels": [3_2, 6_4],
               "down_block_types": ["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",
               ],
               "vq_embed_dim": 4,
           }






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           torch.manual_seed(0  )
           UpperCamelCase__    :   Union[str, Any]					=				VQModel(**self.dummy_movq_kwargs  )
           return model






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.dummy_unet
           UpperCamelCase__    :   Optional[Any]					=				self.dummy_movq

           UpperCamelCase__    :   Optional[Any]					=				DDIMScheduler(
               num_train_timesteps=1_0_0_0						,						beta_schedule='''linear'''						,						beta_start=0.00085						,						beta_end=0.012						,						clip_sample=A_						,						set_alpha_to_one=A_						,						steps_offset=1						,						prediction_type='''epsilon'''						,						thresholding=A_						,						)

           UpperCamelCase__    :   Dict					=				{
               "unet": unet,
               "scheduler": scheduler,
               "movq": movq,
           }

           return components






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   int					=				floats_tensor((1, self.text_embedder_hidden_size)						,						rng=random.Random(A_  )  ).to(A_  )
           UpperCamelCase__    :   Any					=				floats_tensor((1, self.text_embedder_hidden_size)						,						rng=random.Random(seed + 1  )  ).to(
               A_  )
           # create init_image
           UpperCamelCase__    :   Any					=				floats_tensor((1, 3, 6_4, 6_4)						,						rng=random.Random(A_  )  ).to(A_  )
           UpperCamelCase__    :   Optional[Any]					=				image.cpu().permute(0						,						2						,						3						,						1  )[0]
           UpperCamelCase__    :   str					=				Image.fromarray(np.uinta(A_  )  ).convert('''RGB'''  ).resize((2_5_6, 2_5_6)  )
           # create mask
           UpperCamelCase__    :   Dict					=				np.ones((6_4, 6_4)						,						dtype=np.floataa  )
           UpperCamelCase__    :   Union[str, Any]					=				0

           if str(A_  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   Tuple					=				torch.manual_seed(A_  )
           else:
                 UpperCamelCase__    :   Optional[int]					=				torch.Generator(device=A_  ).manual_seed(A_  )
           UpperCamelCase__    :   Optional[int]					=				{
               "image": init_image,
               "mask_image": mask,
               "image_embeds": image_embeds,
               "negative_image_embeds": negative_image_embeds,
               "generator": generator,
               "height": 6_4,
               "width": 6_4,
               "num_inference_steps": 2,
               "guidance_scale": 4.0,
               "output_type": "np",
           }
           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				"cpu"

           UpperCamelCase__    :   Dict					=				self.get_dummy_components()

           UpperCamelCase__    :   str					=				self.pipeline_class(**A_  )
           UpperCamelCase__    :   Any					=				pipe.to(A_  )

           pipe.set_progress_bar_config(disable=A_  )

           UpperCamelCase__    :   int					=				pipe(**self.get_dummy_inputs(A_  )  )
           UpperCamelCase__    :   List[Any]					=				output.images

           UpperCamelCase__    :   List[str]					=				pipe(
               **self.get_dummy_inputs(A_  )						,						return_dict=A_						,						)[0]

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

           print(F'''image.shape {image.shape}'''  )

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

           UpperCamelCase__    :   List[str]					=				np.array(
               [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]  )

           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()}'''






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           super().test_inference_batch_single_identical(expected_max_diff=3e-3  )




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





     """simple docstring"""







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




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''  )

           UpperCamelCase__    :   Any					=				load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
           UpperCamelCase__    :   Tuple					=				np.ones((7_6_8, 7_6_8)						,						dtype=np.floataa  )
           UpperCamelCase__    :   str					=				0

           UpperCamelCase__    :   Tuple					=				"a hat"

           UpperCamelCase__    :   Tuple					=				KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''						,						torch_dtype=torch.floataa  )
           pipe_prior.to(A_  )

           UpperCamelCase__    :   Optional[Any]					=				KandinskyVaaInpaintPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''						,						torch_dtype=torch.floataa  )
           UpperCamelCase__    :   int					=				pipeline.to(A_  )
           pipeline.set_progress_bar_config(disable=A_  )

           UpperCamelCase__    :   Tuple					=				torch.Generator(device='''cpu'''  ).manual_seed(0  )
           UpperCamelCase__    :   List[Any]					=				pipe_prior(
               A_						,						generator=A_						,						num_inference_steps=5						,						negative_prompt=''''''						,						).to_tuple()

           UpperCamelCase__    :   Union[str, Any]					=				pipeline(
               image=A_						,						mask_image=A_						,						image_embeds=A_						,						negative_image_embeds=A_						,						generator=A_						,						num_inference_steps=1_0_0						,						height=7_6_8						,						width=7_6_8						,						output_type='''np'''						,						)

           UpperCamelCase__    :   Union[str, Any]					=				output.images[0]

           assert image.shape == (7_6_8, 7_6_8, 3)

           assert_mean_pixel_difference(A_						,						A_  )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      ={
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CLIPSegModel",
        "CLIPSegPreTrainedModel",
        "CLIPSegTextModel",
        "CLIPSegVisionModel",
        "CLIPSegForImageSegmentation",
    ]

if TYPE_CHECKING:
    from .configuration_clipseg import (
        CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPSegConfig,
        CLIPSegTextConfig,
        CLIPSegVisionConfig,
    )
    from .processing_clipseg import CLIPSegProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clipseg import (
            CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPSegForImageSegmentation,
            CLIPSegModel,
            CLIPSegPreTrainedModel,
            CLIPSegTextModel,
            CLIPSegVisionModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


