


"""simple docstring"""







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       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =KandinskyVaaControlnetPipeline
       a  :				Optional[Any]   =["image_embeds", "negative_image_embeds", "hint"]
       a  :				Tuple   =["image_embeds", "negative_image_embeds", "hint"]
       a  :				Dict   =[
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       a  :				int   =False

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim * 4

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 100

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)

         lowerCAmelCase     :     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,
         }

         lowerCAmelCase     :     Optional[Any]								=							UNetaDConditionModel(**snake_case__						)
         return model

       @property
       def        lowercase__							(						self						):






         """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						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     Optional[Any]								=							VQModel(**self.dummy_movq_kwargs						)
         return model

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.dummy_unet
         lowerCAmelCase     :     Optional[Any]								=							self.dummy_movq

         lowerCAmelCase     :     str								=							DDIMScheduler(
             num_train_timesteps=1_000		,     beta_schedule="linear"		,     beta_start=0.00085		,     beta_end=0.012		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     steps_offset=1		,     prediction_type="epsilon"		,     thresholding=snake_case__		,     )

         lowerCAmelCase     :     Optional[Any]								=							{
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     Dict								=							floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(seed + 1						)						).to(
             snake_case__						)

         # create hint
         lowerCAmelCase     :     Any								=							floats_tensor((1, 3, 64, 64)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)

         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Union[str, Any]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     str								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase     :     Any								=							{
             "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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"cpu"

         lowerCAmelCase     :     Optional[int]								=							self.get_dummy_components()

         lowerCAmelCase     :     int								=							self.pipeline_class(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							pipe.to(snake_case__						)

         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Dict								=							pipe(**self.get_dummy_inputs(snake_case__						)						)
         lowerCAmelCase     :     List[Any]								=							output.images

         lowerCAmelCase     :     Tuple								=							pipe(
             **self.get_dummy_inputs(snake_case__						)		,     return_dict=snake_case__		,     )[0]

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

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

         lowerCAmelCase     :     Any								=							np.array(
             [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]						)

         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       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





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







       def        lowercase__							(						self						):






         """simple docstring"""





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

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

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

         lowerCAmelCase     :     Dict								=							KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"		,     torch_dtype=torch.floataa						)
         lowerCAmelCase     :     Union[str, Any]								=							pipeline.to(snake_case__						)
         pipeline.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Dict								=							"A robot, 4k photo"

         lowerCAmelCase     :     Union[str, Any]								=							torch.Generator(device="cuda"						).manual_seed(0						)
         lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							pipe_prior(
             snake_case__		,     generator=snake_case__		,     num_inference_steps=5		,     negative_prompt=""		,     ).to_tuple()

         lowerCAmelCase     :     Dict								=							torch.Generator(device="cuda"						).manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							pipeline(
             image_embeds=snake_case__		,     negative_image_embeds=snake_case__		,     hint=snake_case__		,     generator=snake_case__		,     num_inference_steps=100		,     output_type="np"		,     )

         lowerCAmelCase     :     Optional[int]								=							output.images[0]

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

         assert_mean_pixel_difference(snake_case__		,     snake_case__						)







'''simple docstring'''




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowercase			:				str    		=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case						)
class  A				(							__snake_case						):





     def __init__(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         self.check_model_type(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A,  A       :   Dict					=						{}, {}
         if padding is not None:
             A       :   List[str]					=						padding
         if truncation is not None:
             A       :   Dict					=						truncation
         if top_k is not None:
             A       :   Optional[Any]					=						top_k
         return preprocess_params, {}, postprocess_params





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			(Image.Image, str)	) and isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						{'''image''': image, '''question''': question}
         else:
             A       :   Any					=						image
         A       :   Any					=						super().__call__(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         return results





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_image(inputs['''image''']	)
         A       :   Optional[Any]					=						self.tokenizer(
             inputs['''question''']						,			return_tensors=self.framework						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						self.image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors=self.framework	)
         model_inputs.update(SCREAMING_SNAKE_CASE	)
         return model_inputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[Any]					=						self.model(**SCREAMING_SNAKE_CASE	)
         return model_outputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=5	) ->				int:






         """simple docstring"""






         if top_k > self.model.config.num_labels:
             A       :   Dict					=						self.model.config.num_labels

         if self.framework == "pt":
             A       :   Optional[int]					=						model_outputs.logits.sigmoid()[0]
             A,  A       :   int					=						probs.topk(SCREAMING_SNAKE_CASE	)
         else:
             raise ValueError(F'Unsupported framework: {self.framework}'	)

         A       :   int					=						scores.tolist()
         A       :   List[str]					=						ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)]




