

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 _UpperCamelCase							(							__A							,	unittest.TestCase   ):
   '''simple docstring'''







   lowerCamelCase__           =KandinskyVaaControlnetPipeline
   lowerCamelCase__           =['image_embeds', 'negative_image_embeds', 'hint']
   lowerCamelCase__           =['image_embeds', 'negative_image_embeds', 'hint']
   lowerCamelCase__           =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase__           =False
   @property
   def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase (			self							: List[str]	)							->       Dict:

    """simple docstring"""

    return self.time_input_dim
   @property
   def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[Any]:

    """simple docstring"""

    return self.time_input_dim * 4
   @property
   def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

    """simple docstring"""

    return 100
   @property
   def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Union[str, Any]:

    """simple docstring"""

    torch.manual_seed(0	)

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {
        "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,
    }

    SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDConditionModel(**a	)
    return model
   @property
   def 							__UpperCamelCase (			self							: Tuple	)							->       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 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

    """simple docstring"""

    torch.manual_seed(0	)
    SCREAMING_SNAKE_CASE   :      Dict				     =       VQModel(**self.dummy_movq_kwargs	)
    return model
   def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.dummy_unet
    SCREAMING_SNAKE_CASE   :      Any				     =       self.dummy_movq

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       DDIMScheduler(
        num_train_timesteps=1000      ,      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      ,      )

    SCREAMING_SNAKE_CASE   :      Tuple				     =       {
        "unet": unet,
        "scheduler": scheduler,
        "movq": movq,
    }
    return components
   def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Optional[Any]      ,      a							: Union[str, Any]=0	)							->       str:

    """simple docstring"""

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

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

    if str(a	).startswith("mps"	):
     SCREAMING_SNAKE_CASE   :      str				     =       torch.manual_seed(a	)
    else:
     SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.Generator(device=a	).manual_seed(a	)
    SCREAMING_SNAKE_CASE   :      int				     =       {
        "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 							__UpperCamelCase (			self							: Dict	)							->       List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      int				     =       "cpu"

    SCREAMING_SNAKE_CASE   :      int				     =       self.get_dummy_components()

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.pipeline_class(**a	)
    SCREAMING_SNAKE_CASE   :      Dict				     =       pipe.to(a	)

    pipe.set_progress_bar_config(disable=a	)

    SCREAMING_SNAKE_CASE   :      Dict				     =       pipe(**self.get_dummy_inputs(a	)	)
    SCREAMING_SNAKE_CASE   :      Tuple				     =       output.images

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pipe(
        **self.get_dummy_inputs(a	)      ,      return_dict=a      ,      )[0]

    SCREAMING_SNAKE_CASE   :      List[str]				     =       image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE   :      Tuple				     =       image_from_tuple[0, -3:, -3:, -1]

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

    SCREAMING_SNAKE_CASE   :      Dict				     =       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 _UpperCamelCase							(							unittest.TestCase   ):
   '''simple docstring'''







   def 							__UpperCamelCase (			self							: Any	)							->       Tuple:

    """simple docstring"""

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






   def 							__UpperCamelCase (			self							: List[str]	)							->       List[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"	)

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

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       KandinskyVaaPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-prior"      ,      torch_dtype=torch.floataa	)
    pipe_prior.to(a	)

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       KandinskyVaaControlnetPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-controlnet-depth"      ,      torch_dtype=torch.floataa	)
    SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline.to(a	)
    pipeline.set_progress_bar_config(disable=a	)

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "A robot, 4k photo"

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.Generator(device="cuda"	).manual_seed(0	)
    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pipe_prior(
        a      ,      generator=a      ,      num_inference_steps=5      ,      negative_prompt=""      ,      ).to_tuple()

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.Generator(device="cuda"	).manual_seed(0	)
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pipeline(
        image_embeds=a      ,      negative_image_embeds=a      ,      hint=a      ,      generator=a      ,      num_inference_steps=100      ,      output_type="np"      ,      )

    SCREAMING_SNAKE_CASE   :      Any				     =       output.images[0]

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

    assert_mean_pixel_difference(a      ,      a	)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						     =    {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BridgeTowerForContrastiveLearning',
			    'BridgeTowerForImageAndTextRetrieval',
			    'BridgeTowerForMaskedLM',
			    'BridgeTowerModel',
			    'BridgeTowerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_bridgetower import (
			    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BridgeTowerConfig,
			    BridgeTowerTextConfig,
			    BridgeTowerVisionConfig,
			)
			from .processing_bridgetower import BridgeTowerProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_bridgetower import BridgeTowerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bridgetower import (
						    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BridgeTowerForContrastiveLearning,
						    BridgeTowerForImageAndTextRetrieval,
						    BridgeTowerForMaskedLM,
						    BridgeTowerModel,
						    BridgeTowerPreTrainedModel,
						)


else:
			import sys

			a_						     =    _LazyModule(__name__, globals()['__file__'], _import_structure)