
'''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  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		KandinskyVaaControlnetImgaImgPipeline
     __magic_name__						=		['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     __magic_name__						=		['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     __magic_name__						=		[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __magic_name__						=		False





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






         """simple docstring"""






         return 32





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 32





     @property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return self.time_input_dim





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






         """simple docstring"""






         return self.time_input_dim * 4





     @property
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         return 100





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






         """simple docstring"""






         torch.manual_seed(0	)

         A       :   List[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,
         }

         A       :   List[str]					=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE	)
         return model





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






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   int					=						VQModel(**self.dummy_movq_kwargs	)
         return model





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   List[str]					=						self.dummy_unet
         A       :   Tuple					=						self.dummy_movq

         A       :   List[str]					=						{
             '''num_train_timesteps''': 1000,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.00_085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         A       :   str					=						DDIMScheduler(**SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				List[Any]:






         """simple docstring"""






         A       :   int					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1	)	).to(
             SCREAMING_SNAKE_CASE	)
         # create init_image
         A       :   Optional[int]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						image.cpu().permute(0						,			2						,			3						,			1	)[0]
         A       :   Tuple					=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('''RGB'''	).resize((256, 256)	)
         # create hint
         A       :   Any					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)

         if str(SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   str					=						torch.manual_seed(SCREAMING_SNAKE_CASE	)
         else:
             A       :   List[str]					=						torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						{
             '''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 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Optional[Any]					=						'''cpu'''

         A       :   Dict					=						self.get_dummy_components()

         A       :   Optional[Any]					=						self.pipeline_class(**SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						pipe.to(SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)	)
         A       :   Union[str, Any]					=						output.images

         A       :   str					=						pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)						,			return_dict=SCREAMING_SNAKE_CASE						,			)[0]

         A       :   Optional[Any]					=						image[0, -3:, -3:, -1]
         A       :   Optional[Any]					=						image_from_tuple[0, -3:, -3:, -1]

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

         A       :   List[Any]					=						np.array(
             [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]	)
         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  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''	)

         A       :   int					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''	)
         A       :   List[Any]					=						init_image.resize((512, 512)	)

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

         A       :   int					=						'''A robot, 4k photo'''

         A       :   str					=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''						,			torch_dtype=torch.floataa	)
         pipe_prior.to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,			torch_dtype=torch.floataa	)
         A       :   List[Any]					=						pipeline.to(SCREAMING_SNAKE_CASE	)

         pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A,  A       :   Optional[Any]					=						pipe_prior(
             SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			strength=0.85						,			generator=SCREAMING_SNAKE_CASE						,			negative_prompt=''''''						,			).to_tuple()

         A       :   str					=						pipeline(
             image=SCREAMING_SNAKE_CASE						,			image_embeds=SCREAMING_SNAKE_CASE						,			negative_image_embeds=SCREAMING_SNAKE_CASE						,			hint=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=100						,			height=512						,			width=512						,			strength=0.5						,			output_type='''np'''						,			)

         A       :   str					=						output.images[0]

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

         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_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	)]




