



'''simple docstring'''




import torch

from transformers import AutoModel







class       _lowerCAmelCase				(     torch.nn.Module					):


	def __init__(self			, lowercase="sayef/fsner-bert-base-uncased"				):
				super(__a			, self				).__init__()

				A_      :		List[Any]      =	AutoModel.from_pretrained(__a			, return_dict=__a				)
				A_      :		Any      =	torch.nn.CosineSimilarity(3			, 1E-08				)
				A_      :		Dict      =	torch.nn.Softmax(dim=1				)


	def        _a      (self			, **lowercase				):
				return self.bert(**__a				).last_hidden_state


	def        _a      (self			, lowercase				):
				return token_embeddings.sum(2			, keepdim=__a				)


	def        _a      (self			, lowercase			, lowercase			, lowercase=1				):
				return self.softmax(T * self.cos(__a			, __a				)				)


	def        _a      (self			, lowercase			, lowercase				):
				A_      :		Dict      =	W_supports['sizes'].tolist()
				A_      :		int      =	W_supports['start_token_id'].item()
				A_      :		str      =	W_supports['end_token_id'].item()

				del W_supports["sizes"]
				del W_supports["start_token_id"]
				del W_supports["end_token_id"]

				A_      :		List[Any]      =	self.BERT(**__a				)
				A_      :		Any      =	self.BERT(**__a				)

				A_      :		str      =	None
				A_      :		Any      =	None

				A_      :		Dict      =	W_supports['input_ids'] == start_token_id
				A_      :		Tuple      =	W_supports['input_ids'] == end_token_id

				for i, size in enumerate(__a				):
							if i == 0:
										A_      :		Any      =	0
							else:
										A_      :		int      =	support_sizes[i - 1]

							A_      :		str      =	S[s : s + size][start_token_masks[s : s + size]]
							A_      :		Dict      =	S[s : s + size][end_token_masks[s : s + size]]

							A_      :		str      =	torch.matmul(q[i]			, s_start.T				).sum(1				).softmax(0				)
							A_      :		Tuple      =	torch.matmul(q[i]			, s_end.T				).sum(1				).softmax(0				)

							if p_starts is not None:
										A_      :		Tuple      =	torch.vstack((p_starts, p_start)				)
										A_      :		str      =	torch.vstack((p_ends, p_end)				)
							else:
										A_      :		Any      =	p_start
										A_      :		int      =	p_end

				return p_starts, p_ends




'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyVaaInpaintPipeline
 A__				= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							int  )      ->   int:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Optional[int]													=    {
           'in_channels': 9,
           # 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 :   Optional[Any]													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''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 A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   int													=    self.dummy_unet
       __snake_case :   Dict													=    self.dummy_movq

       __snake_case :   int													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   Optional[Any]													=    {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							str      ,     __a						:							Union[str, Any]      ,     __a						:							List[str]=0  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Any													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   int													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1  )  ).to(
           __a  )
       # create init_image
       __snake_case :   Union[str, Any]													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   List[Any]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   Optional[Any]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[str]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Optional[int]													=    torch.manual_seed(__a  )
       else:
             __snake_case :   List[Any]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   Optional[Any]													=    {
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs
 def A_   (   self						:							List[str]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Tuple													=    'cpu'

       __snake_case :   Dict													=    self.get_dummy_components()

       __snake_case :   List[Any]													=    self.pipeline_class(**__a  )
       __snake_case :   List[Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Union[str, Any]													=    output.images

       __snake_case :   Optional[int]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   Optional[int]													=    image[0, -3:, -3:, -1]
       __snake_case :   Tuple													=    image_from_tuple[0, -3:, -3:, -1]

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

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

       __snake_case :   Union[str, Any]													=    np.array(
           [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]  )

       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 A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Dict													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Optional[int]													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   int													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Optional[Any]													=    0

       __snake_case :   Union[str, Any]													=    'a hat'

       __snake_case :   Optional[int]													=    KandinskyVaaPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   Optional[Any]													=    KandinskyVaaInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Dict													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Optional[int]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   List[str]													=    output.images[0]

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

       assert_mean_pixel_difference(__a      ,     __a  )



