
'''simple docstring'''





import copy

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Dict				=							"deta"
 lowercase_					:    Any				=							{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }







 def __init__(					self		:  Optional[int]			,				snake_case__		:  List[Any]=None			,				snake_case__		:  Union[str, Any]=9_00			,				snake_case__		:  Optional[Any]=20_48			,				snake_case__		:  List[str]=6			,				snake_case__		:  Any=20_48			,				snake_case__		:  str=8			,				snake_case__		:  Tuple=6			,				snake_case__		:  str=10_24			,				snake_case__		:  str=8			,				snake_case__		:  Optional[Any]=0.0			,				snake_case__		:  List[str]=True			,				snake_case__		:  str="relu"			,				snake_case__		:  str=2_56			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Union[str, Any]=0.0			,				snake_case__		:  Union[str, Any]=0.0			,				snake_case__		:  Tuple=0.02			,				snake_case__		:  Union[str, Any]=1.0			,				snake_case__		:  Tuple=True			,				snake_case__		:  Any=False			,				snake_case__		:  int="sine"			,				snake_case__		:  Optional[int]=5			,				snake_case__		:  List[str]=4			,				snake_case__		:  Union[str, Any]=4			,				snake_case__		:  Any=True			,				snake_case__		:  Dict=3_00			,				snake_case__		:  Any=True			,				snake_case__		:  Tuple=True			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Optional[Any]=5			,				snake_case__		:  List[Any]=2			,				snake_case__		:  Dict=1			,				snake_case__		:  Union[str, Any]=1			,				snake_case__		:  Optional[Any]=5			,				snake_case__		:  str=2			,				snake_case__		:  Union[str, Any]=0.1			,				snake_case__		:  List[str]=0.25			,				**snake_case__		:  List[Any]			,				):



   '''simple docstring'''




   if backbone_config is None:
     logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
     UpperCAmelCase__      :					List[str]				       =							CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
   else:
     if isinstance(snake_case__			,				snake_case__ ):
       UpperCAmelCase__      :					Optional[Any]				       =							backbone_config.pop("model_type" )
       UpperCAmelCase__      :					Optional[int]				       =							CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase__      :					List[str]				       =							config_class.from_dict(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							backbone_config
   UpperCAmelCase__      :					Optional[int]				       =							num_queries
   UpperCAmelCase__      :					List[str]				       =							max_position_embeddings
   UpperCAmelCase__      :					Optional[int]				       =							d_model
   UpperCAmelCase__      :					List[str]				       =							encoder_ffn_dim
   UpperCAmelCase__      :					str				       =							encoder_layers
   UpperCAmelCase__      :					Optional[int]				       =							encoder_attention_heads
   UpperCAmelCase__      :					List[str]				       =							decoder_ffn_dim
   UpperCAmelCase__      :					str				       =							decoder_layers
   UpperCAmelCase__      :					Tuple				       =							decoder_attention_heads
   UpperCAmelCase__      :					Tuple				       =							dropout
   UpperCAmelCase__      :					Tuple				       =							attention_dropout
   UpperCAmelCase__      :					int				       =							activation_dropout
   UpperCAmelCase__      :					int				       =							activation_function
   UpperCAmelCase__      :					str				       =							init_std
   UpperCAmelCase__      :					Dict				       =							init_xavier_std
   UpperCAmelCase__      :					Any				       =							encoder_layerdrop
   UpperCAmelCase__      :					List[Any]				       =							auxiliary_loss
   UpperCAmelCase__      :					Dict				       =							position_embedding_type
   # deformable attributes
   UpperCAmelCase__      :					Optional[Any]				       =							num_feature_levels
   UpperCAmelCase__      :					Tuple				       =							encoder_n_points
   UpperCAmelCase__      :					Optional[Any]				       =							decoder_n_points
   UpperCAmelCase__      :					Dict				       =							two_stage
   UpperCAmelCase__      :					str				       =							two_stage_num_proposals
   UpperCAmelCase__      :					List[Any]				       =							with_box_refine
   UpperCAmelCase__      :					List[Any]				       =							assign_first_stage
   if two_stage is True and with_box_refine is False:
     raise ValueError("If two_stage is True, with_box_refine must be True." )
   # Hungarian matcher
   UpperCAmelCase__      :					Optional[Any]				       =							class_cost
   UpperCAmelCase__      :					Optional[int]				       =							bbox_cost
   UpperCAmelCase__      :					Dict				       =							giou_cost
   # Loss coefficients
   UpperCAmelCase__      :					List[Any]				       =							mask_loss_coefficient
   UpperCAmelCase__      :					int				       =							dice_loss_coefficient
   UpperCAmelCase__      :					List[Any]				       =							bbox_loss_coefficient
   UpperCAmelCase__      :					List[Any]				       =							giou_loss_coefficient
   UpperCAmelCase__      :					List[Any]				       =							eos_coefficient
   UpperCAmelCase__      :					List[str]				       =							focal_alpha
   super().__init__(is_encoder_decoder=snake_case__			,				**snake_case__ )







 @property
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   return self.encoder_attention_heads







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.d_model







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							copy.deepcopy(self.__dict__ )
   UpperCAmelCase__      :					str				       =							self.backbone_config.to_dict()
   UpperCAmelCase__      :					str				       =							self.__class__.model_type
   return output





'''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 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

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







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					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=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "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__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  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 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, 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 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Any				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					Optional[int]				       =							image_from_tuple[0, -3:, -3:, -1]

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

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

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   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		:  Dict ):



   '''simple docstring'''




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






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

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[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__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

   UpperCAmelCase__      :					Union[str, Any]				       =							KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



