

import copy

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


_a		       =   logging.get_logger(__name__)

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






class 							A_ (		__lowerCAmelCase					):
     _lowercase			:     List[Any]								=	'deta'
     _lowercase			:     Optional[int]								=	{
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }


     def __init__(							self			:		Union[str, Any]			,  UpperCAmelCase			:		Any=None			,  UpperCAmelCase			:		str=9_0_0			,  UpperCAmelCase			:		Dict=2_0_4_8			,  UpperCAmelCase			:		str=6			,  UpperCAmelCase			:		int=2_0_4_8			,  UpperCAmelCase			:		Optional[int]=8			,  UpperCAmelCase			:		Union[str, Any]=6			,  UpperCAmelCase			:		Dict=1_0_2_4			,  UpperCAmelCase			:		Any=8			,  UpperCAmelCase			:		Any=0.0			,  UpperCAmelCase			:		int=True			,  UpperCAmelCase			:		int="relu"			,  UpperCAmelCase			:		List[Any]=2_5_6			,  UpperCAmelCase			:		Optional[Any]=0.1			,  UpperCAmelCase			:		Dict=0.0			,  UpperCAmelCase			:		List[str]=0.0			,  UpperCAmelCase			:		List[Any]=0.02			,  UpperCAmelCase			:		Optional[Any]=1.0			,  UpperCAmelCase			:		Optional[int]=True			,  UpperCAmelCase			:		Optional[Any]=False			,  UpperCAmelCase			:		Any="sine"			,  UpperCAmelCase			:		List[Any]=5			,  UpperCAmelCase			:		int=4			,  UpperCAmelCase			:		int=4			,  UpperCAmelCase			:		Optional[Any]=True			,  UpperCAmelCase			:		int=3_0_0			,  UpperCAmelCase			:		Optional[Any]=True			,  UpperCAmelCase			:		int=True			,  UpperCAmelCase			:		List[Any]=1			,  UpperCAmelCase			:		List[str]=5			,  UpperCAmelCase			:		List[Any]=2			,  UpperCAmelCase			:		Union[str, Any]=1			,  UpperCAmelCase			:		Optional[Any]=1			,  UpperCAmelCase			:		Tuple=5			,  UpperCAmelCase			:		Tuple=2			,  UpperCAmelCase			:		Dict=0.1			,  UpperCAmelCase			:		Any=0.25			,  **UpperCAmelCase			:		List[Any]			,  )							->	Dict:





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

      __lowerCAmelCase:   Any   	=      backbone_config
      __lowerCAmelCase:   Union[str, Any]   	=      num_queries
      __lowerCAmelCase:   int   	=      max_position_embeddings
      __lowerCAmelCase:   Tuple   	=      d_model
      __lowerCAmelCase:   List[str]   	=      encoder_ffn_dim
      __lowerCAmelCase:   int   	=      encoder_layers
      __lowerCAmelCase:   Any   	=      encoder_attention_heads
      __lowerCAmelCase:   Any   	=      decoder_ffn_dim
      __lowerCAmelCase:   Any   	=      decoder_layers
      __lowerCAmelCase:   int   	=      decoder_attention_heads
      __lowerCAmelCase:   Any   	=      dropout
      __lowerCAmelCase:   Optional[Any]   	=      attention_dropout
      __lowerCAmelCase:   int   	=      activation_dropout
      __lowerCAmelCase:   Tuple   	=      activation_function
      __lowerCAmelCase:   int   	=      init_std
      __lowerCAmelCase:   Dict   	=      init_xavier_std
      __lowerCAmelCase:   int   	=      encoder_layerdrop
      __lowerCAmelCase:   Any   	=      auxiliary_loss
      __lowerCAmelCase:   str   	=      position_embedding_type
      # deformable attributes
      __lowerCAmelCase:   Tuple   	=      num_feature_levels
      __lowerCAmelCase:   Union[str, Any]   	=      encoder_n_points
      __lowerCAmelCase:   Dict   	=      decoder_n_points
      __lowerCAmelCase:   Tuple   	=      two_stage
      __lowerCAmelCase:   List[str]   	=      two_stage_num_proposals
      __lowerCAmelCase:   List[Any]   	=      with_box_refine
      __lowerCAmelCase:   List[str]   	=      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
      __lowerCAmelCase:   int   	=      class_cost
      __lowerCAmelCase:   Dict   	=      bbox_cost
      __lowerCAmelCase:   Optional[int]   	=      giou_cost
      # Loss coefficients
      __lowerCAmelCase:   Union[str, Any]   	=      mask_loss_coefficient
      __lowerCAmelCase:   Union[str, Any]   	=      dice_loss_coefficient
      __lowerCAmelCase:   List[str]   	=      bbox_loss_coefficient
      __lowerCAmelCase:   str   	=      giou_loss_coefficient
      __lowerCAmelCase:   int   	=      eos_coefficient
      __lowerCAmelCase:   Tuple   	=      focal_alpha
      super().__init__(is_encoder_decoder=UpperCAmelCase			,  **UpperCAmelCase					)


     @property
     def   UpperCAmelCase  (							self			:		Dict					)							->	int:
      return self.encoder_attention_heads


     @property
     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	int:
      return self.d_model


     def   UpperCAmelCase  (							self			:		Dict					)							->	Tuple:
      __lowerCAmelCase:   List[Any]   	=      copy.deepcopy(self.__dict__					)
      __lowerCAmelCase:   Tuple   	=      self.backbone_config.to_dict()
      __lowerCAmelCase:   Optional[Any]   	=      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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



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





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     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    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, 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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





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

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            '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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

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

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

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

        a__:     str										=	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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





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







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



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





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

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

        assert_mean_pixel_difference(lowercase    ,			lowercase)


