

'''simple docstring'''




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_lowerCAmelCase :    List[Any]         =						logging.get_logger(__name__)

_lowerCAmelCase :    List[Any]         =						{
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class   _SCREAMING_SNAKE_CASE   (   snake_case_     ):
 __SCREAMING_SNAKE_CASE					:int      	=					"detr"
 __SCREAMING_SNAKE_CASE					:List[str]      	=					["past_key_values"]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }



 def __init__(   self		:   Dict			,				a__		:   Optional[int]=True			,				a__		:   List[Any]=None			,				a__		:   Optional[Any]=3			,				a__		:   Optional[Any]=100			,				a__		:   int=6			,				a__		:   List[str]=2048			,				a__		:   Any=8			,				a__		:   Optional[Any]=6			,				a__		:   Tuple=2048			,				a__		:   Tuple=8			,				a__		:   Any=0.0			,				a__		:   Union[str, Any]=0.0			,				a__		:   List[str]=True			,				a__		:   List[Any]="relu"			,				a__		:   Optional[Any]=256			,				a__		:   int=0.1			,				a__		:   Any=0.0			,				a__		:   str=0.0			,				a__		:   Any=0.02			,				a__		:   Tuple=1.0			,				a__		:   Optional[Any]=False			,				a__		:   Optional[int]="sine"			,				a__		:   List[Any]="resnet50"			,				a__		:   Tuple=True			,				a__		:   int=False			,				a__		:   Optional[int]=1			,				a__		:   Optional[int]=5			,				a__		:   Tuple=2			,				a__		:   Dict=1			,				a__		:   Union[str, Any]=1			,				a__		:   Optional[Any]=5			,				a__		:   str=2			,				a__		:   Any=0.1			,				**a__		:   List[str]			,				):


      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.'''      )
                __magic_name__						       =    CONFIG_MAPPING['resnet'](out_features=['''stage4''']      )
           elif isinstance(_A			,				_A      ):
                __magic_name__						       =    backbone_config.get('''model_type'''      )
                __magic_name__						       =    CONFIG_MAPPING[backbone_model_type]
                __magic_name__						       =    config_class.from_dict(_A      )
           # set timm attributes to None
           __magic_name__						       =    None, None, None

      __magic_name__						       =    use_timm_backbone
      __magic_name__						       =    backbone_config
      __magic_name__						       =    num_channels
      __magic_name__						       =    num_queries
      __magic_name__						       =    d_model
      __magic_name__						       =    encoder_ffn_dim
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    encoder_attention_heads
      __magic_name__						       =    decoder_ffn_dim
      __magic_name__						       =    decoder_layers
      __magic_name__						       =    decoder_attention_heads
      __magic_name__						       =    dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    activation_function
      __magic_name__						       =    init_std
      __magic_name__						       =    init_xavier_std
      __magic_name__						       =    encoder_layerdrop
      __magic_name__						       =    decoder_layerdrop
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    auxiliary_loss
      __magic_name__						       =    position_embedding_type
      __magic_name__						       =    backbone
      __magic_name__						       =    use_pretrained_backbone
      __magic_name__						       =    dilation
      # Hungarian matcher
      __magic_name__						       =    class_cost
      __magic_name__						       =    bbox_cost
      __magic_name__						       =    giou_cost
      # Loss coefficients
      __magic_name__						       =    mask_loss_coefficient
      __magic_name__						       =    dice_loss_coefficient
      __magic_name__						       =    bbox_loss_coefficient
      __magic_name__						       =    giou_loss_coefficient
      __magic_name__						       =    eos_coefficient
      super().__init__(is_encoder_decoder=_A			,				**_A      )



 @property
 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.encoder_attention_heads



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      return self.d_model



 @classmethod
 def 			snake_case__  (   cls		:   Tuple			,				a__		:   PretrainedConfig			,				**a__		:   Union[str, Any]      ):
      return cls(backbone_config=_A			,				**_A      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    copy.deepcopy(self.__dict__      )
      if output["backbone_config"] is not None:
           __magic_name__						       =    self.backbone_config.to_dict()
      __magic_name__						       =    self.__class__.model_type
      return output






class   _SCREAMING_SNAKE_CASE   (   snake_case_     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					version.parse("""1.11"""     )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ('''pixel_mask''', {0: '''batch'''}),
          ]      )



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      return 1E-5



 @property
 def 			snake_case__  (   self		:   int      ):
      return 12









'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					StableDiffusionInpaintPipeline
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE					:Tuple      	=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE					:str      	=					frozenset(
     []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					frozenset([]     )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    UNetaDConditionModel(
          block_out_channels=(32, 64)			,				layers_per_block=2			,				sample_size=32			,				in_channels=9			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=32			,				attention_head_dim=(2, 4)			,				use_linear_projection=a__			,				)
      __magic_name__						       =    PNDMScheduler(skip_prk_steps=a__      )
      torch.manual_seed(0      )
      __magic_name__						       =    AutoencoderKL(
          block_out_channels=[32, 64]			,				in_channels=3			,				out_channels=3			,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,				latent_channels=4			,				sample_size=128			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    CLIPTextConfig(
          bos_token_id=0			,				eos_token_id=2			,				hidden_size=32			,				intermediate_size=37			,				layer_norm_eps=1E-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1000			,				hidden_act='''gelu'''			,				projection_dim=512			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]			,				a__		:   List[Any]=0      ):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )
      __magic_name__						       =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': init_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    StableDiffusionInpaintPipeline(**a__      )
      __magic_name__						       =    sd_pipe.to(a__      )
      sd_pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    sd_pipe(**a__      ).images
      __magic_name__						       =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __magic_name__						       =    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2



 def 			snake_case__  (   self		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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



 def 			snake_case__  (   self		:   int      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(a__			,				safety_checker=a__      )
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 9E-3



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				torch_dtype=torch.floataa			,				safety_checker=a__			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 5E-1



 def 			snake_case__  (   self		:   List[Any]      ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    PNDMScheduler.from_pretrained(a__			,				subfolder='''scheduler'''      )
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				safety_checker=a__			,				scheduler=a__			,				torch_dtype=torch.floataa			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing(1      )
      pipe.enable_sequential_cpu_offload()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				num_inference_steps=2			,				output_type='''np'''			,				)

      __magic_name__						       =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






