


"""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 _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Tuple													=						KandinskyVaaInpaintPipeline
   __UpperCAmelCase				: str													=						['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   __UpperCAmelCase				: Tuple													=						[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   __UpperCAmelCase				: Tuple													=						[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase				: Tuple													=						False







   @property
   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       return self.time_input_dim







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return self.time_input_dim * 4







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

       '''simple docstring'''




       return 100







   @property
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       torch.manual_seed(0		)

       snake_case					:   str         =					{
           "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					:   Tuple         =					UNetaDConditionModel(**UpperCamelCase__		)
       return model







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

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   List[str]         =					self.dummy_unet
       snake_case					:   List[str]         =					self.dummy_movq

       snake_case					:   Optional[Any]         =					DDIMScheduler(
           num_train_timesteps=1000		,			beta_schedule="linear"		,			beta_start=0.00085		,			beta_end=0.012		,			clip_sample=UpperCamelCase__		,			set_alpha_to_one=UpperCamelCase__		,			steps_offset=1		,			prediction_type="epsilon"		,			thresholding=UpperCamelCase__		,			)

       snake_case					:   str         =					{
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0		)			->	List[Any]:

       '''simple docstring'''




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

       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case					:   str         =					torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case					:   int         =					torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
       snake_case					:   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 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Dict         =					"cpu"

       snake_case					:   Tuple         =					self.get_dummy_components()

       snake_case					:   Tuple         =					self.pipeline_class(**UpperCamelCase__		)
       snake_case					:   Dict         =					pipe.to(UpperCamelCase__		)

       pipe.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   Any         =					pipe(**self.get_dummy_inputs(UpperCamelCase__		)		)
       snake_case					:   Any         =					output.images

       snake_case					:   List[Any]         =					pipe(
           **self.get_dummy_inputs(UpperCamelCase__		)		,			return_dict=UpperCamelCase__		,			)[0]

       snake_case					:   Any         =					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					:   Dict         =					np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]		)

       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 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"		)

       snake_case					:   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					:   int         =					0

       snake_case					:   Any         =					"a hat"

       snake_case					:   Any         =					KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"		,			torch_dtype=torch.floataa		)
       pipe_prior.to(UpperCamelCase__		)

       snake_case					:   str         =					KandinskyVaaInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder-inpaint"		,			torch_dtype=torch.floataa		)
       snake_case					:   Optional[int]         =					pipeline.to(UpperCamelCase__		)
       pipeline.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   Dict         =					torch.Generator(device="cpu"		).manual_seed(0		)
       snake_case      ,snake_case					:   Dict         =					pipe_prior(
           UpperCamelCase__		,			generator=UpperCamelCase__		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

       snake_case					:   Dict         =					pipeline(
           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"		,			)

       snake_case					:   Optional[Any]         =					output.images[0]

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

       assert_mean_pixel_difference(UpperCamelCase__		,			UpperCamelCase__		)









"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





