
"""simple docstring"""





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
    from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import SwinaSRImageProcessor


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=13							,  lowercase_=32							,  lowercase_=1							,  lowercase_=3							,  lowercase_=16							,  lowercase_=[1, 2, 1]							,  lowercase_=[2, 2, 4]							,  lowercase_=2							,  lowercase_=2.0							,  lowercase_=True							,  lowercase_=0.0							,  lowercase_=0.0							,  lowercase_=0.1							,  lowercase_="gelu"							,  lowercase_=False							,  lowercase_=True							,  lowercase_=0.02							,  lowercase_=1E-5							,  lowercase_=True							,  lowercase_=None							,  lowercase_=False							,  lowercase_=2							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       parent
     UpperCAmelCase_   :   Dict     	=       batch_size
     UpperCAmelCase_   :   Optional[Any]     	=       image_size
     UpperCAmelCase_   :   List[Any]     	=       patch_size
     UpperCAmelCase_   :   List[Any]     	=       num_channels
     UpperCAmelCase_   :   str     	=       embed_dim
     UpperCAmelCase_   :   Any     	=       depths
     UpperCAmelCase_   :   Tuple     	=       num_heads
     UpperCAmelCase_   :   str     	=       window_size
     UpperCAmelCase_   :   Optional[Any]     	=       mlp_ratio
     UpperCAmelCase_   :   List[str]     	=       qkv_bias
     UpperCAmelCase_   :   Optional[int]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   Optional[int]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   Optional[Any]     	=       drop_path_rate
     UpperCAmelCase_   :   str     	=       hidden_act
     UpperCAmelCase_   :   int     	=       use_absolute_embeddings
     UpperCAmelCase_   :   int     	=       patch_norm
     UpperCAmelCase_   :   List[str]     	=       layer_norm_eps
     UpperCAmelCase_   :   Optional[Any]     	=       initializer_range
     UpperCAmelCase_   :   int     	=       is_training
     UpperCAmelCase_   :   str     	=       scope
     UpperCAmelCase_   :   Tuple     	=       use_labels
     UpperCAmelCase_   :   Union[str, Any]     	=       upscale

     # here we set some attributes to make tests pass
     UpperCAmelCase_   :   Tuple     	=       len(lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       embed_dim
     UpperCAmelCase_   :   int     	=       (image_size // patch_size) ** 2







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     UpperCAmelCase_   :   int     	=       None
     if self.use_labels:
        UpperCAmelCase_   :   Union[str, Any]     	=       ids_tensor([self.batch_size]							,  self.type_sequence_label_size    )

     UpperCAmelCase_   :   Optional[int]     	=       self.get_config()

     return config, pixel_values, labels







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return SwinaSRConfig(
         image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  embed_dim=self.embed_dim							,  depths=self.depths							,  num_heads=self.num_heads							,  window_size=self.window_size							,  mlp_ratio=self.mlp_ratio							,  qkv_bias=self.qkv_bias							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  drop_path_rate=self.drop_path_rate							,  hidden_act=self.hidden_act							,  use_absolute_embeddings=self.use_absolute_embeddings							,  path_norm=self.patch_norm							,  layer_norm_eps=self.layer_norm_eps							,  initializer_range=self.initializer_range							,  upscale=self.upscale							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       SwinaSRModel(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Optional[Any]     	=       model(lowercase_    )

     self.parent.assertEqual(
         result.last_hidden_state.shape							,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       SwinaSRForImageSuperResolution(lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   List[str]     	=       model(lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       self.image_size * self.upscale
     self.parent.assertEqual(
         result.reconstruction.shape							,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       self.prepare_config_and_inputs()
     UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       config_and_inputs
     UpperCAmelCase_   :   Optional[Any]     	=       {"pixel_values": pixel_values}
     return config, inputs_dict




@require_torch
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						{"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

  SCREAMING_SNAKE_CASE__     :		Tuple    	=						False
  SCREAMING_SNAKE_CASE__     :		Dict    	=						False
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						False
  SCREAMING_SNAKE_CASE__     :		str    	=						False
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						False







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       SwinaSRModelTester(self    )
     UpperCAmelCase_   :   int     	=       ConfigTester(self							,  config_class=lowercase_							,  embed_dim=37    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_super_resolution(*lowercase_    )







  @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not use inputs_embeds"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not support training yet"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not support training yet"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   int     	=       model_class(lowercase_    )
        self.assertIsInstance(model.get_input_embeddings()							,  (nn.Module)    )
        UpperCAmelCase_   :   Optional[Any]     	=       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowercase_							,  nn.Linear    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Dict     	=       model_class(lowercase_    )
        UpperCAmelCase_   :   List[str]     	=       inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase_   :   Any     	=       [*signature.parameters.keys()]

        UpperCAmelCase_   :   Union[str, Any]     	=       ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,  lowercase_    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_   :   Dict     	=       SwinaSRModel.from_pretrained(lowercase_    )
        self.assertIsNotNone(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase_   :   Union[str, Any]     	=       _config_zero_init(lowercase_    )
     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Union[str, Any]     	=       model_class(config=lowercase_    )
        for name, param in model.named_parameters():
           if "logit_scale" in name:
              continue
           if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()							,  [0.0, 1.0]							,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,  )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   str     	=       True

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Tuple     	=       True
        UpperCAmelCase_   :   Dict     	=       False
        UpperCAmelCase_   :   Any     	=       True
        UpperCAmelCase_   :   List[Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   Tuple     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   Union[str, Any]     	=       outputs.attentions
        UpperCAmelCase_   :   Any     	=       len(self.model_tester.depths    )
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        UpperCAmelCase_   :   Optional[Any]     	=       True
        UpperCAmelCase_   :   List[str]     	=       config.window_size**2
        UpperCAmelCase_   :   Union[str, Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   List[Any]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   Dict     	=       outputs.attentions
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        self.assertListEqual(
            list(attentions[0].shape[-3:]    )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )
        UpperCAmelCase_   :   Dict     	=       len(lowercase_    )

        # Check attention is always last and order is fine
        UpperCAmelCase_   :   Optional[int]     	=       True
        UpperCAmelCase_   :   str     	=       True
        UpperCAmelCase_   :   Optional[Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   List[str]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

        self.assertEqual(out_len + 1							,  len(lowercase_    )    )

        UpperCAmelCase_   :   Optional[int]     	=       outputs.attentions

        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]    )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )




@require_vision
@require_torch
@slow
class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       SwinaSRImageProcessor()
     UpperCAmelCase_   :   Tuple     	=       SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"    ).to(lowercase_    )

     UpperCAmelCase_   :   Union[str, Any]     	=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     UpperCAmelCase_   :   List[Any]     	=       processor(images=lowercase_							,  return_tensors="pt"    ).to(lowercase_    )

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_    )

     # verify the logits
     UpperCAmelCase_   :   Optional[Any]     	=       torch.Size([1, 3, 976, 1296]    )
     self.assertEqual(outputs.reconstruction.shape							,  lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       torch.tensor(
         [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]    ).to(lowercase_    )
     self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]							,  lowercase_							,  atol=1E-4    )    )







# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
   _UpperCAmelCase  						=      TEXT_TO_IMAGE_IMAGE_PARAMS

   def 		A				(   self      :							Dict				)							->					int:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		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				, )
    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				, )
    torch.manual_seed(0				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Tuple=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2
    a							=		randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, )
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
        "image": image,
        "mask_image": mask_image,
        "control_image": control_image,
    }

    return inputs

   def 		A				(   self      :							Optional[Any]				)							->					int:


    """simple docstring"""






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()				, reason="XFormers attention is only available with CUDA and `xformers` installed"				, )
   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset([]						)

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		UNetaDConditionModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=4				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, )
    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				, )
    torch.manual_seed(0				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

   def 		A				(   self      :							Tuple				)							->					Union[str, Any]:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		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				, )
    torch.manual_seed(0				)

    def init_weights(__lowerCAmelCase      :							Tuple				):
     if isinstance(__lowerCAmelCase				, torch.nn.Convad				):
      torch.nn.init.normal(m.weight				)
      m.bias.data.fill_(1.0				)

    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    controlneta.controlnet_down_blocks.apply(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		ControlNetModel(
        block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
    controlneta.controlnet_down_blocks.apply(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				, )
    torch.manual_seed(0				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		MultiControlNetModel([controlneta, controlneta]				)

    a							=		{
        "unet": unet,
        "controlnet": controlnet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2

    a							=		[
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
    ]
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
        "image": image,
        "mask_image": mask_image,
        "control_image": control_image,
    }

    return inputs

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)

    a							=		1_0.0
    a							=		4

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=0.1				, control_guidance_end=0.2				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=[0.1, 0.3]				, control_guidance_end=[0.2, 0.7]				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=0.4				, control_guidance_end=[0.5, 0.8]				)[0]

    # make sure that all outputs are different
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3

   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()				, reason="XFormers attention is only available with CUDA and `xformers` installed"				, )
   def 		A				(   self      :							Optional[int]				)							->					str:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)

   def 		A				(   self      :							Dict				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)




   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)
    with tempfile.TemporaryDirectory() as tmpdir:
     try:
      # save_pretrained is not implemented for Multi-ControlNet
      pipe.save_pretrained(__lowerCAmelCase				)
     except NotImplementedError:
      pass



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

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






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

   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-inpainting"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(0				)
    a							=		load_image(
        "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
        "/stable_diffusion_inpaint/input_bench_mask.png"				).resize((512, 512)				)

    a							=		"pitch black hole"

    a							=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((512, 512)				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, generator=__lowerCAmelCase				, output_type="np"				, num_inference_steps=3				, )

    a							=		output.images[0]

    assert image.shape == (512, 512, 3)

    a							=		load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"				)

    assert np.abs(expected_image - image				).max() < 9E-2




   def 		A				(   self      :							Any				)							->					Dict:


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    a							=		DDIMScheduler.from_config(pipe.scheduler.config				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(33				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"				)
    a							=		init_image.resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"				)
    a							=		mask_image.resize((512, 512)				)

    a							=		"a handsome man with ray-ban sunglasses"

    def make_inpaint_condition(__lowerCAmelCase      :							Any				, __lowerCAmelCase      :							int				):
     a							=		np.array(image.convert("RGB"				)				).astype(np.floataa				) / 2_5_5.0
     a							=		np.array(image_mask.convert("L"				)				).astype(np.floataa				) / 2_5_5.0

     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
     a							=		-1.0  # set as masked pixel
     a							=		np.expand_dims(__lowerCAmelCase				, 0				).transpose(0				, 3				, 1				, 2				)
     a							=		torch.from_numpy(__lowerCAmelCase				)
     return image

    a							=		make_inpaint_condition(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, guidance_scale=9.0				, eta=1.0				, generator=__lowerCAmelCase				, num_inference_steps=20				, output_type="np"				, )
    a							=		output.images[0]

    assert image.shape == (512, 512, 3)

    a							=		load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"				)

    assert np.abs(expected_image - image				).max() < 9E-2

