






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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'''



		_snake_case    :    Tuple       						=  KandinskyInpaintPipeline
		_snake_case    :    Union[str, Any]       						=  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
		_snake_case    :    Any       						=  [
		    '''prompt''',
		    '''negative_prompt''',
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		    '''mask_image''',
		]
		_snake_case    :    List[str]       						=  [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''latents''',
		    '''guidance_scale''',
		    '''negative_prompt''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		_snake_case    :    Optional[Any]       						=  False



		@property
		def 					__UpperCAmelCase			(							self   )	->							Any:
							return 3_2



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							return 3_2



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.time_input_dim



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return self.time_input_dim * 4



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return 1_0_0



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Tuple            =						XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
							return tokenizer



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							torch.manual_seed(0   )
							UpperCAmelCase_    :      int            =						MCLIPConfig(
							    numDims=self.cross_attention_dim				,		transformerDimensions=self.text_embedder_hidden_size				,		hidden_size=self.text_embedder_hidden_size				,		intermediate_size=3_7				,		num_attention_heads=4				,		num_hidden_layers=5				,		vocab_size=1_0_0_5				,		)

							UpperCAmelCase_    :      Union[str, Any]            =						MultilingualCLIP(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						text_encoder.eval()

							return text_encoder



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							torch.manual_seed(0   )

							UpperCAmelCase_    :      Optional[int]            =						{
							    'in_channels': 9,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'text_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': 'text_image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							UpperCAmelCase_    :      Tuple            =						UNetaDConditionModel(**_UpperCamelCase   )
							return model



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							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 					__UpperCAmelCase			(							self   )	->							Tuple:
							torch.manual_seed(0   )
							UpperCAmelCase_    :      Tuple            =						VQModel(**self.dummy_movq_kwargs   )
							return model



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      Optional[Any]            =						self.dummy_text_encoder
							UpperCAmelCase_    :      Optional[Any]            =						self.dummy_tokenizer
							UpperCAmelCase_    :      Dict            =						self.dummy_unet
							UpperCAmelCase_    :      Dict            =						self.dummy_movq

							UpperCAmelCase_    :      List[Any]            =						DDIMScheduler(
							    num_train_timesteps=1_0_0_0				,		beta_schedule='linear'				,		beta_start=0.0_00_85				,		beta_end=0.0_12				,		clip_sample=_UpperCamelCase				,		set_alpha_to_one=_UpperCamelCase				,		steps_offset=1				,		prediction_type='epsilon'				,		thresholding=_UpperCamelCase				,		)

							UpperCAmelCase_    :      Optional[int]            =						{
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components



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

							if str(_UpperCamelCase   ).startswith('mps'   ):
												UpperCAmelCase_    :      List[Any]            =						torch.manual_seed(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Dict            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						{
							    'prompt': 'horse',
							    '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 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      int            =						'cpu'

							UpperCAmelCase_    :      int            =						self.get_dummy_components()

							UpperCAmelCase_    :      Tuple            =						self.pipeline_class(**_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						pipe.to(_UpperCamelCase   )

							pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						pipe(**self.get_dummy_inputs(_UpperCamelCase   )   )
							UpperCAmelCase_    :      Tuple            =						output.images

							UpperCAmelCase_    :      int            =						pipe(
							    **self.get_dummy_inputs(_UpperCamelCase   )				,		return_dict=_UpperCamelCase				,		)[0]

							UpperCAmelCase_    :      Tuple            =						image[0, -3:, -3:, -1]
							UpperCAmelCase_    :      Any            =						image_from_tuple[0, -3:, -3:, -1]

							print(f"image.shape {image.shape}"   )

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

							UpperCAmelCase_    :      int            =						np.array(
							    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

							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 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





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







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

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

							UpperCAmelCase_    :      int            =						'a hat'

							UpperCAmelCase_    :      int            =						KandinskyPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-prior'				,		torch_dtype=torch.floataa   )
							pipe_prior.to(_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						KandinskyInpaintPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-inpaint'				,		torch_dtype=torch.floataa   )
							UpperCAmelCase_    :      Optional[int]            =						pipeline.to(_UpperCamelCase   )
							pipeline.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						torch.Generator(device='cpu'   ).manual_seed(0   )
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						pipe_prior(
							    _UpperCamelCase				,		generator=_UpperCamelCase				,		num_inference_steps=5				,		negative_prompt=''				,		).to_tuple()

							UpperCAmelCase_    :      List[Any]            =						pipeline(
							    _UpperCamelCase				,		image=_UpperCamelCase				,		mask_image=_UpperCamelCase				,		image_embeds=_UpperCamelCase				,		negative_image_embeds=_UpperCamelCase				,		generator=_UpperCamelCase				,		num_inference_steps=1_0_0				,		height=7_6_8				,		width=7_6_8				,		output_type='np'				,		)

							UpperCAmelCase_    :      Tuple            =						output.images[0]

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

							assert_mean_pixel_difference(_UpperCamelCase				,		_UpperCamelCase   )














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

__UpperCAmelCase								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )
							self.check_model_type(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						{}, {}
							if padding is not None:
												UpperCAmelCase_    :      List[str]            =						padding
							if truncation is not None:
												UpperCAmelCase_    :      Tuple            =						truncation
							if top_k is not None:
												UpperCAmelCase_    :      Dict            =						top_k
							return preprocess_params, {}, postprocess_params



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							if isinstance(_UpperCamelCase				,		(Image.Image, str)   ) and isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'question': question}
							else:
												UpperCAmelCase_    :      List[str]            =						image
							UpperCAmelCase_    :      Optional[Any]            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=False   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      Dict            =						self.tokenizer(
							    inputs['question']				,		return_tensors=self.framework				,		padding=_UpperCamelCase				,		truncation=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.image_processor(images=_UpperCamelCase				,		return_tensors=self.framework   )
							model_inputs.update(_UpperCamelCase   )
							return model_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      Any            =						self.model(**_UpperCamelCase   )
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=5   )	->							str:
							if top_k > self.model.config.num_labels:
												UpperCAmelCase_    :      Union[str, Any]            =						self.model.config.num_labels

							if self.framework == "pt":
												UpperCAmelCase_    :      List[str]            =						model_outputs.logits.sigmoid()[0]
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						probs.topk(_UpperCamelCase   )
							else:
												raise ValueError(f"Unsupported framework: {self.framework}"   )

							UpperCAmelCase_    :      Optional[Any]            =						scores.tolist()
							UpperCAmelCase_    :      Tuple            =						ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase				,		_UpperCamelCase   )]






