


'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict=13				,							SCREAMING_SNAKE_CASE      :							List[Any]=30				,							SCREAMING_SNAKE_CASE      :							Any=2				,							SCREAMING_SNAKE_CASE      :							Dict=3				,							SCREAMING_SNAKE_CASE      :							Optional[int]=True				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE      :							Dict=32				,							SCREAMING_SNAKE_CASE      :							str=2				,							SCREAMING_SNAKE_CASE      :							Tuple=4				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=37				,							SCREAMING_SNAKE_CASE      :							Dict="gelu"				,							SCREAMING_SNAKE_CASE      :							str=0.1				,							SCREAMING_SNAKE_CASE      :							List[str]=0.1				,							SCREAMING_SNAKE_CASE      :							Tuple=10				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=0.02				,							SCREAMING_SNAKE_CASE      :							Dict=3				,							SCREAMING_SNAKE_CASE      :							str=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=2				,							):
           _A					:     Tuple     = parent
           _A					:     str     = batch_size
           _A					:     int     = image_size
           _A					:     List[Any]     = patch_size
           _A					:     Dict     = num_channels
           _A					:     int     = is_training
           _A					:     Optional[int]     = use_labels
           _A					:     List[str]     = hidden_size
           _A					:     Dict     = num_hidden_layers
           _A					:     Tuple     = num_attention_heads
           _A					:     List[Any]     = intermediate_size
           _A					:     Optional[int]     = hidden_act
           _A					:     Optional[int]     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     Any     = type_sequence_label_size
           _A					:     str     = initializer_range
           _A					:     List[Any]     = scope
           _A					:     Optional[int]     = encoder_stride

           # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
           _A					:     Union[str, Any]     = (image_size // patch_size) ** 2
           _A					:     List[str]     = num_patches + 2


    def        A   (  self      :							Any):
           _A					:     Dict     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           _A					:     Union[str, Any]     = self.get_config()

           return config, pixel_values, labels


    def        A   (  self      :							Optional[int]):
           return DeiTConfig(
               image_size=self.image_size				,							patch_size=self.patch_size				,							num_channels=self.num_channels				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							is_decoder=SCREAMING_SNAKE_CASE				,							initializer_range=self.initializer_range				,							encoder_stride=self.encoder_stride				,							)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Any):
           _A					:     Optional[int]     = TFDeiTModel(config=SCREAMING_SNAKE_CASE)
           _A					:     Any     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size))


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     int     = TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE)
           _A					:     Any     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.reconstruction.shape				,							(self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           _A					:     List[Any]     = 1
           _A					:     Optional[Any]     = TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE)

           _A					:     int     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A					:     Optional[int]     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.reconstruction.shape				,							(self.batch_size, 1, self.image_size, self.image_size))


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = self.type_sequence_label_size
           _A					:     int     = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _A					:     List[Any]     = 1
           _A					:     List[str]     = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE)

           _A					:     int     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size))


    def        A   (  self      :							Any):
           _A					:     Dict     = self.prepare_config_and_inputs()
           _A  ,			_A  ,			_A					:     List[str]     = config_and_inputs
           _A					:     List[Any]     = {'pixel_values': pixel_values}
           return config, inputs_dict




@require_tf
class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    a													=   (
        {
            "feature-extraction": TFDeiTModel,
            "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    a													=   False
    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Optional[int]):
           _A					:     Any     = TFDeiTModelTester(self)
           _A					:     Optional[int]     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							has_text_modality=SCREAMING_SNAKE_CASE				,							hidden_size=37)


    def        A   (  self      :							Optional[Any]):
           self.config_tester.run_common_tests()


    @unittest.skip(reason='DeiT does not use inputs_embeds')
    def        A   (  self      :							List[Any]):
           pass


    def        A   (  self      :							Union[str, Any]):
           _A  ,			_A					:     Union[str, Any]     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Optional[Any]     = model_class(SCREAMING_SNAKE_CASE)
                  self.assertIsInstance(model.get_input_embeddings()				,							(tf.keras.layers.Layer))
                  _A					:     Optional[Any]     = model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE				,							tf.keras.layers.Dense))


    def        A   (  self      :							Dict):
           _A  ,			_A					:     Optional[Any]     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Optional[int]     = model_class(SCREAMING_SNAKE_CASE)
                  _A					:     Union[str, Any]     = inspect.signature(model.call)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A					:     Optional[int]     = [*signature.parameters.keys()]

                  _A					:     List[Any]     = ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           _A					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]=False):
           _A					:     Optional[int]     = super()._prepare_for_class(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)

           if return_labels:
                  if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters:
                         del inputs_dict["labels"]

           return inputs_dict


    @slow
    def        A   (  self      :							List[str]):
           for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Tuple     = TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    ):
       _A					:     str     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_tf
@require_vision
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							Tuple):
           return (
               DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
               if is_vision_available()
               else None
           )


    @slow
    def        A   (  self      :							List[Any]):
           _A					:     Any     = TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')

           _A					:     Any     = self.default_image_processor
           _A					:     List[Any]     = prepare_img()
           _A					:     Optional[int]     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='tf')

           # forward pass
           _A					:     Dict     = model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     Union[str, Any]     = tf.TensorShape((1, 1000))
           self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = tf.constant([-1.0266, 0.1912, -1.2861])

           self.assertTrue(np.allclose(outputs.logits[0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))










'''simple docstring'''







# 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 			__lowerCamelCase			(	a_    , a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}	)
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS


    def        A   (  self      :							Tuple):
           torch.manual_seed(0)
           _A					:     List[str]     = 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					:     Optional[Any]     = 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					:     Any     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     List[str]     = 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					:     List[str]     = 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					:     Any     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     Tuple     = {
               '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      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[int]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Tuple     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2
           _A					:     Tuple     = randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							)
           _A					:     Tuple     = floats_tensor(control_image.shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Union[str, Any]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Any     = {
               '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        A   (  self      :							str):
           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      :							Tuple):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							int):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)




class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[str]     = 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(SCREAMING_SNAKE_CASE      :							Union[str, Any]):
                  if isinstance(SCREAMING_SNAKE_CASE				,							torch.nn.Convad):
                         torch.nn.init.normal(m.weight)
                         m.bias.data.fill_(1.0)

           _A					:     int     = 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(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     str     = 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(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     List[Any]     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     Tuple     = 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					:     str     = 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					:     List[Any]     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     List[str]     = MultiControlNetModel([controlneta, controlneta])

           _A					:     List[str]     = {
               '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      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[Any]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Union[str, Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2

           _A					:     List[str]     = [
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
           ]

           _A					:     str     = floats_tensor(control_image[0].shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Optional[int]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Optional[Any]     = {
               '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        A   (  self      :							Tuple):
           _A					:     List[str]     = self.get_dummy_components()
           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)

           _A					:     int     = 10.0
           _A					:     Union[str, Any]     = 4

           _A					:     str     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = steps
           _A					:     List[str]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE)[0]

           _A					:     Any     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = steps
           _A					:     Any     = scale
           _A					:     Dict     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=0.1				,							control_guidance_end=0.2)[0]

           _A					:     Tuple     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     str     = steps
           _A					:     List[Any]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=[0.1, 0.3]				,							control_guidance_end=[0.2, 0.7])[0]

           _A					:     List[str]     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = steps
           _A					:     Tuple     = scale
           _A					:     str     = pipe(**SCREAMING_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        A   (  self      :							Optional[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        A   (  self      :							Any):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							Dict):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)


    def        A   (  self      :							str):
           _A					:     Optional[int]     = self.get_dummy_components()
           _A					:     Dict     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           with tempfile.TemporaryDirectory() as tmpdir:
                  try:
                         # save_pretrained is not implemented for Multi-ControlNet
                         pipe.save_pretrained(SCREAMING_SNAKE_CASE)
                  except NotImplementedError:
                         pass




@slow
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


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


    def        A   (  self      :							Any):
           _A					:     Dict     = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny')

           _A					:     List[Any]     = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
               'runwayml/stable-diffusion-v1-5'				,							safety_checker=SCREAMING_SNAKE_CASE				,							controlnet=SCREAMING_SNAKE_CASE)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = torch.Generator(device='cpu').manual_seed(0)
           _A					:     List[Any]     = 'evil space-punk bird'
           _A					:     int     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png').resize((512, 512))
           _A					:     List[str]     = load_image(
               'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png').resize((512, 512))

           _A					:     Dict     = pipe(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							control_image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np'				,							num_inference_steps=50				,							strength=0.6				,							)

           _A					:     Optional[int]     = output.images[0]

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

           _A					:     int     = 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






