





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							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	KandinskyInpaintPipeline
 UpperCAmelCase__									=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 UpperCAmelCase__									=	[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 UpperCAmelCase__									=	[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase__									=	False
 @property
 def   A_			(   self						:   List[Any]			)    ->			List[str]:
     return 32
 @property
 def   A_			(   self						:   Optional[int]			)    ->			Union[str, Any]:
     return 32
 @property
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     return self.time_input_dim
 @property
 def   A_			(   self						:   int			)    ->			Optional[Any]:
     return self.time_input_dim * 4
 @property
 def   A_			(   self						:   Any			)    ->			List[str]:
     return 100
 @property
 def   A_			(   self						:   Tuple			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
     return tokenizer
 @property
 def   A_			(   self						:   List[Any]			)    ->			Union[str, Any]:
     torch.manual_seed(0			)
     lowerCamelCase__					:   Any      				=	MCLIPConfig(
         numDims=self.cross_attention_dim    ,  transformerDimensions=self.text_embedder_hidden_size    ,  hidden_size=self.text_embedder_hidden_size    ,  intermediate_size=37    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  vocab_size=1005    ,  )

     lowerCamelCase__					:   List[Any]      				=	MultilingualCLIP(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	text_encoder.eval()

     return text_encoder
 @property
 def   A_			(   self						:   List[str]			)    ->			Union[str, Any]:
     torch.manual_seed(0			)

     lowerCamelCase__					:   Optional[Any]      				=	{
         '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,
     }

     lowerCamelCase__					:   Tuple      				=	UNetaDConditionModel(**UpperCAmelCase			)
     return model
 @property
 def   A_			(   self						:   List[Any]			)    ->			List[Any]:
     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						:   Tuple			)    ->			Any:
     torch.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	VQModel(**self.dummy_movq_kwargs			)
     return model
 def   A_			(   self						:   int			)    ->			Any:
     lowerCamelCase__					:   Any      				=	self.dummy_text_encoder
     lowerCamelCase__					:   Union[str, Any]      				=	self.dummy_tokenizer
     lowerCamelCase__					:   int      				=	self.dummy_unet
     lowerCamelCase__					:   Any      				=	self.dummy_movq

     lowerCamelCase__					:   Optional[Any]      				=	DDIMScheduler(
         num_train_timesteps=1000    ,  beta_schedule='linear'    ,  beta_start=0.0_0_0_8_5    ,  beta_end=0.0_1_2    ,  clip_sample=UpperCAmelCase    ,  set_alpha_to_one=UpperCAmelCase    ,  steps_offset=1    ,  prediction_type='epsilon'    ,  thresholding=UpperCAmelCase    ,  )

     lowerCamelCase__					:   List[Any]      				=	{
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }

     return components
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   int=0			)    ->			Any:
     lowerCamelCase__					:   Optional[Any]      				=	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(UpperCAmelCase			)			).to(UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(seed + 1			)			).to(UpperCAmelCase			)
     # create init_image
     lowerCamelCase__					:   Any      				=	floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(UpperCAmelCase			)			).to(UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
     lowerCamelCase__					:   str      				=	Image.fromarray(np.uinta(UpperCAmelCase			)			).convert('RGB'			).resize((256, 256)			)
     # create mask
     lowerCamelCase__					:   Union[str, Any]      				=	np.ones((64, 64)    ,  dtype=np.floataa			)
     lowerCamelCase__					:   Dict      				=	0

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

     lowerCamelCase__					:   List[Any]      				=	self.get_dummy_components()

     lowerCamelCase__					:   Union[str, Any]      				=	self.pipeline_class(**UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	pipe.to(UpperCAmelCase			)

     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   str      				=	pipe(**self.get_dummy_inputs(UpperCAmelCase			)			)
     lowerCamelCase__					:   Union[str, Any]      				=	output.images

     lowerCamelCase__					:   List[str]      				=	pipe(
         **self.get_dummy_inputs(UpperCAmelCase			)    ,  return_dict=UpperCAmelCase    ,  )[0]

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

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

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

     lowerCamelCase__					:   str      				=	np.array(
         [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]			)

     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						:   List[Any]			)    ->			Union[str, Any]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3			)


@slow
@require_torch_gpu
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Tuple			)    ->			Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 def   A_			(   self						:   int			)    ->			List[str]:
     lowerCamelCase__					:   Tuple      				=	load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'			)

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

     lowerCamelCase__					:   int      				=	'a hat'

     lowerCamelCase__					:   List[Any]      				=	KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,  torch_dtype=torch.floataa			)
     pipe_prior.to(UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	KandinskyInpaintPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-inpaint'    ,  torch_dtype=torch.floataa			)
     lowerCamelCase__					:   Any      				=	pipeline.to(UpperCAmelCase			)
     pipeline.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   Dict      				=	torch.Generator(device='cpu'			).manual_seed(0			)
     lowerCamelCase__					:   List[Any]      				=	pipe_prior(
         UpperCAmelCase    ,  generator=UpperCAmelCase    ,  num_inference_steps=5    ,  negative_prompt=''    ,  ).to_tuple()

     lowerCamelCase__					:   Optional[Any]      				=	pipeline(
         UpperCAmelCase    ,  image=UpperCAmelCase    ,  mask_image=UpperCAmelCase    ,  image_embeds=UpperCAmelCase    ,  negative_image_embeds=UpperCAmelCase    ,  generator=UpperCAmelCase    ,  num_inference_steps=100    ,  height=768    ,  width=768    ,  output_type='np'    ,  )

     lowerCamelCase__					:   Union[str, Any]      				=	output.images[0]

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

     assert_mean_pixel_difference(UpperCAmelCase    ,  UpperCAmelCase			)










from ..utils import DummyObject, requires_backends

class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Tuple    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Optional[int]			)    ->			List[str]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[str]			)    ->			int:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Any    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   str    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Dict			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Dict			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[str]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   int			)    ->			Dict:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[int]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[str]			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Tuple    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)





