



"""simple docstring"""
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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Optional[int]						=						KandinskyInpaintPipeline
   __lowerCAmelCase  :   Optional[int]						=						['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   __lowerCAmelCase  :   Union[str, Any]						=						[
       'prompt',
       'negative_prompt',
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   __lowerCAmelCase  :   Optional[int]						=						[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __lowerCAmelCase  :   str						=						False

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return 32

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return 32

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    return self.time_input_dim

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return self.time_input_dim * 4

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return 100

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
    return tokenizer

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    torch.manual_seed(0						)
    UpperCAmelCase	:					Dict	=	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      ,     )

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

    return text_encoder

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    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(**_SCREAMING_SNAKE_CASE						)
    return model

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''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 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    torch.manual_seed(0						)
    UpperCAmelCase	:					str	=	VQModel(**self.dummy_movq_kwargs						)
    return model

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.dummy_text_encoder
    UpperCAmelCase	:					Any	=	self.dummy_tokenizer
    UpperCAmelCase	:					int	=	self.dummy_unet
    UpperCAmelCase	:					str	=	self.dummy_movq

    UpperCAmelCase	:					List[str]	=	DDIMScheduler(
        num_train_timesteps=1000      ,     beta_schedule="""linear"""      ,     beta_start=0.0_0085      ,     beta_end=0.012      ,     clip_sample=_SCREAMING_SNAKE_CASE      ,     set_alpha_to_one=_SCREAMING_SNAKE_CASE      ,     steps_offset=1      ,     prediction_type="""epsilon"""      ,     thresholding=_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Optional[int]	=	{
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=0						)  ->      Dict:





    '''simple docstring'''




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

    if str(_SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
     UpperCAmelCase	:					Dict	=	torch.manual_seed(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					str	=	torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	{
        """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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	"""cpu"""

    UpperCAmelCase	:					str	=	self.get_dummy_components()

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

    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

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

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

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

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

    UpperCAmelCase	:					int	=	np.array(
        [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]						)

    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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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



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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""						)

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

    UpperCAmelCase	:					List[str]	=	"""a hat"""

    UpperCAmelCase	:					Optional[int]	=	KandinskyPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-1-prior"""      ,     torch_dtype=torch.floataa						)
    pipe_prior.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	KandinskyInpaintPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-1-inpaint"""      ,     torch_dtype=torch.floataa						)
    UpperCAmelCase	:					List[Any]	=	pipeline.to(_SCREAMING_SNAKE_CASE						)
    pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	torch.Generator(device="""cpu"""						).manual_seed(0						)
    UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	pipe_prior(
        _SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=5      ,     negative_prompt=""""""      ,     ).to_tuple()

    UpperCAmelCase	:					int	=	pipeline(
        _SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     mask_image=_SCREAMING_SNAKE_CASE      ,     image_embeds=_SCREAMING_SNAKE_CASE      ,     negative_image_embeds=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type="""np"""      ,     )

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

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

    assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)





"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A:   List[str]								=		logging.get_logger(__name__)

A:   Dict								=		{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						'conditional_detr'
   __lowerCAmelCase  :   Union[str, Any]						=						['past_key_values']
   __lowerCAmelCase  :   int						=						{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=300      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="relu"      ,     _SCREAMING_SNAKE_CASE=256      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1.0      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="sine"      ,     _SCREAMING_SNAKE_CASE="resnet50"      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.25      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Tuple:





    '''simple docstring'''




    if backbone_config is not None and use_timm_backbone:
     raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""						)

    if not use_timm_backbone:
     if backbone_config is None:
      logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
      UpperCAmelCase	:					Tuple	=	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
     elif isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					str	=	backbone_config.get("""model_type"""						)
      UpperCAmelCase	:					int	=	CONFIG_MAPPING[backbone_model_type]
      UpperCAmelCase	:					Union[str, Any]	=	config_class.from_dict(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	use_timm_backbone
    UpperCAmelCase	:					Optional[int]	=	backbone_config
    UpperCAmelCase	:					List[str]	=	num_channels
    UpperCAmelCase	:					Any	=	num_queries
    UpperCAmelCase	:					Union[str, Any]	=	d_model
    UpperCAmelCase	:					List[str]	=	encoder_ffn_dim
    UpperCAmelCase	:					Optional[int]	=	encoder_layers
    UpperCAmelCase	:					Union[str, Any]	=	encoder_attention_heads
    UpperCAmelCase	:					Optional[Any]	=	decoder_ffn_dim
    UpperCAmelCase	:					Any	=	decoder_layers
    UpperCAmelCase	:					Optional[int]	=	decoder_attention_heads
    UpperCAmelCase	:					Optional[int]	=	dropout
    UpperCAmelCase	:					Dict	=	attention_dropout
    UpperCAmelCase	:					Dict	=	activation_dropout
    UpperCAmelCase	:					Any	=	activation_function
    UpperCAmelCase	:					Any	=	init_std
    UpperCAmelCase	:					Tuple	=	init_xavier_std
    UpperCAmelCase	:					Optional[int]	=	encoder_layerdrop
    UpperCAmelCase	:					Any	=	decoder_layerdrop
    UpperCAmelCase	:					Any	=	encoder_layers
    UpperCAmelCase	:					Optional[Any]	=	auxiliary_loss
    UpperCAmelCase	:					List[Any]	=	position_embedding_type
    UpperCAmelCase	:					Union[str, Any]	=	backbone
    UpperCAmelCase	:					List[Any]	=	use_pretrained_backbone
    UpperCAmelCase	:					Dict	=	dilation
    # Hungarian matcher
    UpperCAmelCase	:					Optional[int]	=	class_cost
    UpperCAmelCase	:					List[str]	=	bbox_cost
    UpperCAmelCase	:					List[str]	=	giou_cost
    # Loss coefficients
    UpperCAmelCase	:					List[Any]	=	mask_loss_coefficient
    UpperCAmelCase	:					List[str]	=	dice_loss_coefficient
    UpperCAmelCase	:					Optional[int]	=	cls_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	bbox_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	giou_loss_coefficient
    UpperCAmelCase	:					int	=	focal_alpha
    super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.encoder_attention_heads

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.d_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	copy.deepcopy(self.__dict__						)
    if self.backbone_config is not None:
     UpperCAmelCase	:					Union[str, Any]	=	self.backbone_config.to_dict()
    UpperCAmelCase	:					Dict	=	self.__class__.model_type
    return output



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						version.parse('1.11' )

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ("""pixel_mask""", {0: """batch"""}),
        ]						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      float:





    '''simple docstring'''




    return 1E-5




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 12
