




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image



class       a					:

     '''simple docstring'''





     def __init__(     self       :      int ,   __snake_case       :      Any ,   __snake_case       :      Dict=2 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Union[str, Any]=False ,   __snake_case       :      Union[str, Any]=10 ,   __snake_case       :      List[str]=3 ,   __snake_case       :      Optional[Any]=32 * 8 ,   __snake_case       :      Tuple=32 * 8 ,   __snake_case       :      List[str]=4 ,   __snake_case       :      int=64 ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_auxiliary_loss
         UpperCAmelCase_										=					num_queries
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					min_size
         UpperCAmelCase_										=					max_size
         UpperCAmelCase_										=					num_labels
         UpperCAmelCase_										=					hidden_dim
         UpperCAmelCase_										=					hidden_dim
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
             __snake_case    )

         UpperCAmelCase_										=					torch.ones([self.batch_size, self.min_size, self.max_size] ,   device=__snake_case    )

         UpperCAmelCase_										=					(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,   device=__snake_case    ) > 0.5
         ).float()
         UpperCAmelCase_										=					(torch.rand((self.batch_size, self.num_labels) ,   device=__snake_case    ) > 0.5).long()

         UpperCAmelCase_										=					self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					MaskaFormerConfig(
             hidden_size=self.hidden_dim ,   )
         UpperCAmelCase_										=					self.num_queries
         UpperCAmelCase_										=					self.num_labels

         UpperCAmelCase_										=					[1, 1, 1, 1]
         UpperCAmelCase_										=					self.num_channels

         UpperCAmelCase_										=					64
         UpperCAmelCase_										=					1_28
         UpperCAmelCase_										=					self.hidden_dim
         UpperCAmelCase_										=					self.hidden_dim
         UpperCAmelCase_										=					self.hidden_dim
         return config
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.prepare_config_and_inputs()
         UpperCAmelCase_										=					{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
         return config, inputs_dict
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      str ,   __snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					output.encoder_hidden_states
         UpperCAmelCase_										=					output.pixel_decoder_hidden_states
         UpperCAmelCase_										=					output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__snake_case    ) ,   len(config.backbone_config.depths    )    )
         self.parent.assertTrue(len(__snake_case    ) ,   len(config.backbone_config.depths    )    )
         self.parent.assertTrue(len(__snake_case    ) ,   config.decoder_layers    )
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Optional[int]=False    ):
         with torch.no_grad():
             UpperCAmelCase_										=					MaskaFormerModel(config=__snake_case    )
             model.to(__snake_case    )
             model.eval()

             UpperCAmelCase_										=					model(pixel_values=__snake_case ,   pixel_mask=__snake_case    )
             UpperCAmelCase_										=					model(__snake_case ,   output_hidden_states=__snake_case    )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape ,   (self.batch_size, self.num_queries, self.hidden_dim) ,   )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
         self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

         if output_hidden_states:
             self.check_output_hidden_state(__snake_case ,   __snake_case    )




     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					MaskaFormerForUniversalSegmentation(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()

         def comm_check_on_output(__snake_case       :      Tuple    ):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
             self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape ,   (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,   )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape ,   (self.batch_size, self.num_queries, self.num_labels + 1)    )

         with torch.no_grad():
             UpperCAmelCase_										=					model(pixel_values=__snake_case ,   pixel_mask=__snake_case    )
             UpperCAmelCase_										=					model(__snake_case    )

             comm_check_on_output(__snake_case    )

             UpperCAmelCase_										=					model(
                 pixel_values=__snake_case ,   pixel_mask=__snake_case ,   mask_labels=__snake_case ,   class_labels=__snake_case    )

         comm_check_on_output(__snake_case    )

         self.parent.assertTrue(result.loss is not None    )
         self.parent.assertEqual(result.loss.shape ,   torch.Size([1]    )    )



@require_torch
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     lowerCAmelCase    :	Union[str, Any]       		=  {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

     lowerCAmelCase    :	List[str]       		=  False
     lowerCAmelCase    :	Union[str, Any]       		=  False
     lowerCAmelCase    :	Optional[Any]       		=  False
     lowerCAmelCase    :	Tuple       		=  False
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					MaskaFormerModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case ,   has_text_modality=__snake_case    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         self.config_tester.run_common_tests()
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case ,   **__snake_case ,   output_hidden_states=__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case    )
     @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         pass
     @unittest.skip(reason='''Mask2Former is not a generative model'''    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         pass
     @unittest.skip(reason='''Mask2Former does not use token embeddings'''    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         pass
     @require_torch_multi_gpu
     @unittest.skip(
         reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''    )
     def   lowerCamelCase_       (     self       :      Any    ):
         pass
     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
     def   lowerCamelCase_       (     self       :      str    ):
         pass
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCAmelCase_										=					['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,   __snake_case    )
     @slow
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             UpperCAmelCase_										=					MaskaFormerModel.from_pretrained(__snake_case    )
             self.assertIsNotNone(__snake_case    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					(self.model_tester.min_size,) * 2
         UpperCAmelCase_										=					{
             '''pixel_values''': torch.randn((2, 3, *size) ,   device=__snake_case    ),
             '''mask_labels''': torch.randn((2, 10, *size) ,   device=__snake_case    ),
             '''class_labels''': torch.zeros(2 ,   10 ,   device=__snake_case    ).long(),
         }
         UpperCAmelCase_										=					self.model_tester.get_config()

         UpperCAmelCase_										=					MaskaFormerForUniversalSegmentation(__snake_case    ).to(__snake_case    )
         UpperCAmelCase_										=					model(**__snake_case    )
         self.assertTrue(outputs.loss is not None    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case ,   **__snake_case ,   output_hidden_states=__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCAmelCase_										=					model_class(__snake_case    ).to(__snake_case    )
             UpperCAmelCase_										=					model(**__snake_case ,   output_attentions=__snake_case    )
             self.assertTrue(outputs.attentions is not None    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         if not self.model_tester.is_training:
             return

         UpperCAmelCase_										=					self.all_model_classes[1]
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()

         UpperCAmelCase_										=					model_class(__snake_case    )
         model.to(__snake_case    )
         model.train()

         UpperCAmelCase_										=					model(__snake_case ,   mask_labels=__snake_case ,   class_labels=__snake_case    ).loss
         loss.backward()




     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.all_model_classes[1]
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase_										=					True
         UpperCAmelCase_										=					True

         UpperCAmelCase_										=					model_class(__snake_case    ).to(__snake_case    )
         model.train()

         UpperCAmelCase_										=					model(__snake_case ,   mask_labels=__snake_case ,   class_labels=__snake_case    )

         UpperCAmelCase_										=					outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         UpperCAmelCase_										=					outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         UpperCAmelCase_										=					outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         UpperCAmelCase_										=					outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__snake_case    )

         self.assertIsNotNone(encoder_hidden_states.grad    )
         self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
         self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
         self.assertIsNotNone(attentions.grad    )


_lowerCamelCase      			=						1e-4




def  SCREAMING_SNAKE_CASE     (	)  ->							Dict:
    UpperCAmelCase_										=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
    return image



@require_vision
@slow
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     @cached_property
     def   lowerCamelCase_       (     self       :      int    ):
         return "facebook/mask2former-swin-small-coco-instance"
     @cached_property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints    ) if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					MaskaFormerModel.from_pretrained(self.model_checkpoints    ).to(__snake_case    )
         UpperCAmelCase_										=					self.default_image_processor
         UpperCAmelCase_										=					prepare_img()
         UpperCAmelCase_										=					image_processor(__snake_case ,   return_tensors='''pt'''    ).to(__snake_case    )
         UpperCAmelCase_										=					inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
         # check size
         self.assertEqual(__snake_case ,   (1, 3, 3_84, 3_84)    )

         with torch.no_grad():
             UpperCAmelCase_										=					model(**__snake_case    )

         UpperCAmelCase_										=					torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]    ).to(__snake_case    )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3] ,   __snake_case ,   atol=__snake_case    )    )

         UpperCAmelCase_										=					torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]    ).to(__snake_case    )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,   __snake_case ,   atol=__snake_case    )    )

         UpperCAmelCase_										=					torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]    ).to(__snake_case    )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,   __snake_case ,   atol=__snake_case    )    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(__snake_case    ).eval()
         UpperCAmelCase_										=					self.default_image_processor
         UpperCAmelCase_										=					prepare_img()
         UpperCAmelCase_										=					image_processor(__snake_case ,   return_tensors='''pt'''    ).to(__snake_case    )
         UpperCAmelCase_										=					inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
         # check size
         self.assertEqual(__snake_case ,   (1, 3, 3_84, 3_84)    )

         with torch.no_grad():
             UpperCAmelCase_										=					model(**__snake_case    )
         # masks_queries_logits
         UpperCAmelCase_										=					outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    )
         UpperCAmelCase_										=					[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         UpperCAmelCase_										=					torch.tensor(__snake_case    ).to(__snake_case    )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,   __snake_case ,   atol=__snake_case    )    )
         # class_queries_logits
         UpperCAmelCase_										=					outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape ,   (1, model.config.num_queries, model.config.num_labels + 1)    )
         UpperCAmelCase_										=					torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]    ).to(__snake_case    )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,   __snake_case ,   atol=__snake_case    )    )




     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(__snake_case    ).eval()
         UpperCAmelCase_										=					self.default_image_processor

         UpperCAmelCase_										=					image_processor(
             [np.zeros((3, 8_00, 13_33)    ), np.zeros((3, 8_00, 13_33)    )] ,   segmentation_maps=[np.zeros((3_84, 3_84)    ).astype(np.floataa    ), np.zeros((3_84, 3_84)    ).astype(np.floataa    )] ,   return_tensors='''pt''' ,   )

         UpperCAmelCase_										=					inputs['''pixel_values'''].to(__snake_case    )
         UpperCAmelCase_										=					[el.to(__snake_case    ) for el in inputs['''mask_labels''']]
         UpperCAmelCase_										=					[el.to(__snake_case    ) for el in inputs['''class_labels''']]

         with torch.no_grad():
             UpperCAmelCase_										=					model(**__snake_case    )

         self.assertTrue(outputs.loss is not None    )











# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class       a					(       _A     ,       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[str]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Union[str, Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
     lowerCAmelCase    :	List[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     def   lowerCamelCase_       (     self       :      Dict    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					DDIMScheduler(
             beta_start=0.00_085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=__snake_case ,   set_alpha_to_one=__snake_case ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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=10_00 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__snake_case    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         UpperCAmelCase_										=					{
             '''unet''': unet,
             '''controlnet''': controlnet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Any=0    ):
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )

         UpperCAmelCase_										=					2
         UpperCAmelCase_										=					randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,   generator=__snake_case ,   device=torch.device(__snake_case    ) ,   )
         UpperCAmelCase_										=					floats_tensor(control_image.shape ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
         UpperCAmelCase_										=					image.cpu().permute(0 ,   2 ,   3 ,   1    )[0]
         UpperCAmelCase_										=					Image.fromarray(np.uinta(__snake_case    )    ).convert('''RGB'''    ).resize((64, 64)    )
         UpperCAmelCase_										=					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''image''': image,
             '''control_image''': control_image,
         }

         return inputs
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         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   lowerCamelCase_       (     self       :      Optional[Any]    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )




     def   lowerCamelCase_       (     self       :      Dict    ):
         self._test_inference_batch_single_identical(expected_max_diff=2E-3    )



class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Dict       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Any       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[int]       		=  frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )

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

         UpperCAmelCase_										=					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(__snake_case    )

         torch.manual_seed(0    )
         UpperCAmelCase_										=					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(__snake_case    )

         torch.manual_seed(0    )
         UpperCAmelCase_										=					DDIMScheduler(
             beta_start=0.00_085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=__snake_case ,   set_alpha_to_one=__snake_case ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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=10_00 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__snake_case    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         UpperCAmelCase_										=					MultiControlNetModel([controlneta, controlneta]    )

         UpperCAmelCase_										=					{
             '''unet''': unet,
             '''controlnet''': controlnet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Optional[Any]=0    ):
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )

         UpperCAmelCase_										=					2

         UpperCAmelCase_										=					[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,   generator=__snake_case ,   device=torch.device(__snake_case    ) ,   ),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,   generator=__snake_case ,   device=torch.device(__snake_case    ) ,   ),
         ]

         UpperCAmelCase_										=					floats_tensor(control_image[0].shape ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
         UpperCAmelCase_										=					image.cpu().permute(0 ,   2 ,   3 ,   1    )[0]
         UpperCAmelCase_										=					Image.fromarray(np.uinta(__snake_case    )    ).convert('''RGB'''    ).resize((64, 64)    )
         UpperCAmelCase_										=					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''image''': image,
             '''control_image''': control_image,
         }

         return inputs
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					self.pipeline_class(**__snake_case    )
         pipe.to(__snake_case    )

         UpperCAmelCase_										=					10.0
         UpperCAmelCase_										=					4

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					steps
         UpperCAmelCase_										=					scale
         UpperCAmelCase_										=					pipe(**__snake_case    )[0]

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

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

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					steps
         UpperCAmelCase_										=					scale
         UpperCAmelCase_										=					pipe(**__snake_case ,   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   lowerCamelCase_       (     self       :      Optional[int]    ):
         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   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_inference_batch_single_identical(expected_max_diff=2E-3    )




     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					self.pipeline_class(**__snake_case    )
         pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )
         with tempfile.TemporaryDirectory() as tmpdir:
             try:
                 # save_pretrained is not implemented for Multi-ControlNet
                 pipe.save_pretrained(__snake_case    )
             except NotImplementedError:
                 pass



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

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''    )

         UpperCAmelCase_										=					StableDiffusionControlNetImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5''' ,   safety_checker=__snake_case ,   controlnet=__snake_case    )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					torch.Generator(device='''cpu'''    ).manual_seed(0    )
         UpperCAmelCase_										=					'''evil space-punk bird'''
         UpperCAmelCase_										=					load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''    ).resize((5_12, 5_12)    )
         UpperCAmelCase_										=					load_image(
             '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''    ).resize((5_12, 5_12)    )

         UpperCAmelCase_										=					pipe(
             __snake_case ,   __snake_case ,   control_image=__snake_case ,   generator=__snake_case ,   output_type='''np''' ,   num_inference_steps=50 ,   strength=0.6 ,   )

         UpperCAmelCase_										=					output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         UpperCAmelCase_										=					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''    )

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





