





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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_  (    __snake_case				, unittest.TestCase    ):
						__magic_name__:	Optional[int]       					=				KandinskyVaaImgaImgPipeline
						__magic_name__:	str       					=				["image_embeds", "negative_image_embeds", "image"]
						__magic_name__:	Any       					=				[
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						]
						__magic_name__:	Union[str, Any]       					=				[
						    "generator",
						    "height",
						    "width",
						    "strength",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						__magic_name__:	Optional[Any]       					=				False






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







							"""simple docstring"""


							return 32






						@property
						def UpperCAmelCase_ (					self					:					Tuple		)							->			Any:







							"""simple docstring"""


							return 32






						@property
						def UpperCAmelCase_ (					self					:					Tuple		)							->			str:







							"""simple docstring"""


							return self.time_input_dim






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







							"""simple docstring"""


							return self.time_input_dim * 4






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







							"""simple docstring"""


							return 100






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







							"""simple docstring"""


							torch.manual_seed(0		)

							snake_case_						:	Union[str, Any]      =		{
							    '''in_channels''': 4,
							    # 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,
							}

							snake_case_						:	Any      =		UNetaDConditionModel(**_A		)
							return model






						@property
						def UpperCAmelCase_ (					self					:					int		)							->			int:







							"""simple docstring"""


							return {
							    "block_out_channels": [32, 64],
							    "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": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}






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







							"""simple docstring"""


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






						def UpperCAmelCase_ (					self					:					Dict		)							->			Any:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		self.dummy_unet
							snake_case_						:	List[Any]      =		self.dummy_movq

							snake_case_						:	Optional[Any]      =		{
							    '''num_train_timesteps''': 1000,
							    '''beta_schedule''': '''linear''',
							    '''beta_start''': 0.0_0_0_8_5,
							    '''beta_end''': 0.0_1_2,
							    '''clip_sample''': False,
							    '''set_alpha_to_one''': False,
							    '''steps_offset''': 0,
							    '''prediction_type''': '''epsilon''',
							    '''thresholding''': False,
							}

							snake_case_						:	List[Any]      =		DDIMScheduler(**_A		)

							snake_case_						:	Union[str, Any]      =		{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components






						def UpperCAmelCase_ (					self					:					Dict						,      _A					:					Dict						,      _A					:					str=0		)							->			str:







							"""simple docstring"""


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

							if str(_A		).startswith('mps'		):
								snake_case_						:	int      =		torch.manual_seed(_A		)
							else:
								snake_case_						:	Tuple      =		torch.Generator(device=_A		).manual_seed(_A		)
							snake_case_						:	Tuple      =		{
							    '''image''': init_image,
							    '''image_embeds''': image_embeds,
							    '''negative_image_embeds''': negative_image_embeds,
							    '''generator''': generator,
							    '''height''': 64,
							    '''width''': 64,
							    '''num_inference_steps''': 10,
							    '''guidance_scale''': 7.0,
							    '''strength''': 0.2,
							    '''output_type''': '''np''',
							}
							return inputs






						def UpperCAmelCase_ (					self					:					Tuple		)							->			Any:







							"""simple docstring"""


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

							snake_case_						:	Tuple      =		self.get_dummy_components()

							snake_case_						:	List[str]      =		self.pipeline_class(**_A		)
							snake_case_						:	Any      =		pipe.to(_A		)

							pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:	Optional[Any]      =		pipe(**self.get_dummy_inputs(_A		)		)
							snake_case_						:	List[Any]      =		output.images

							snake_case_						:	Union[str, Any]      =		pipe(
							    **self.get_dummy_inputs(_A		)						,      return_dict=_A						,      )[0]

							snake_case_						:	List[str]      =		image[0, -3:, -3:, -1]
							snake_case_						:	Any      =		image_from_tuple[0, -3:, -3:, -1]

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

							snake_case_						:	Optional[int]      =		np.array(
							    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]		)
							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    ):






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







							"""simple docstring"""


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






						def UpperCAmelCase_ (					self					:					str		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	int      =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_img2img_frog.npy'		)

							snake_case_						:	Dict      =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'		)
							snake_case_						:	int      =		'''A red cartoon frog, 4k'''

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

							snake_case_						:	Optional[Any]      =		KandinskyVaaImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder'						,      torch_dtype=torch.floataa		)
							snake_case_						:	Dict      =		pipeline.to(_A		)

							pipeline.set_progress_bar_config(disable=_A		)

							snake_case_						:	str      =		torch.Generator(device='cpu'		).manual_seed(0		)
							snake_case_						:	List[str]      =		pipe_prior(
							    _A						,      generator=_A						,      num_inference_steps=5						,      negative_prompt=''						,      ).to_tuple()

							snake_case_						:	Optional[int]      =		pipeline(
							    image=_A						,      image_embeds=_A						,      negative_image_embeds=_A						,      generator=_A						,      num_inference_steps=100						,      height=768						,      width=768						,      strength=0.2						,      output_type='np'						,      )

							snake_case_						:	List[Any]      =		output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_A						,      _A		)



'''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	)]




