




# 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		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   StableDiffusionControlNetImgaImgPipeline
		__snake_case									=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
		__snake_case									=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		__snake_case									=   IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}     )
		__snake_case									=   IMAGE_TO_IMAGE_IMAGE_PARAMS



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0   ):







								"""simple docstring"""



								if str(UpperCamelCase__   ).startswith('''mps'''   ):
														lowerCAmelCase_      			=  torch.manual_seed(UpperCamelCase__   )
								else:
														lowerCAmelCase_      			=  torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )

								lowerCAmelCase_      			=  2
								lowerCAmelCase_      			=  randn_tensor(
								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),	generator=UpperCamelCase__,	device=torch.device(UpperCamelCase__   ),	)
								lowerCAmelCase_      			=  floats_tensor(control_image.shape,	rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
								lowerCAmelCase_      			=  image.cpu().permute(0,	2,	3,	1   )[0]
								lowerCAmelCase_      			=  Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert('''RGB'''   ).resize((64, 64)   )
								lowerCAmelCase_      			=  {
								    '''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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







class  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   StableDiffusionControlNetImgaImgPipeline
		__snake_case									=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
		__snake_case									=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		__snake_case									=   frozenset([]     )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  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(UpperCamelCase__   ):
														if isinstance(UpperCamelCase__,	torch.nn.Convad   ):
																				torch.nn.init.normal(m.weight   )
																				m.bias.data.fill_(1.0   )

								lowerCAmelCase_      			=  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(UpperCamelCase__   )

								torch.manual_seed(0   )
								lowerCAmelCase_      			=  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(UpperCamelCase__   )

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

								lowerCAmelCase_      			=  MultiControlNetModel([controlneta, controlneta]   )

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



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0   ):







								"""simple docstring"""



								if str(UpperCamelCase__   ).startswith('''mps'''   ):
														lowerCAmelCase_      			=  torch.manual_seed(UpperCamelCase__   )
								else:
														lowerCAmelCase_      			=  torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )

								lowerCAmelCase_      			=  2

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

								lowerCAmelCase_      			=  floats_tensor(control_image[0].shape,	rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
								lowerCAmelCase_      			=  image.cpu().permute(0,	2,	3,	1   )[0]
								lowerCAmelCase_      			=  Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert('''RGB'''   ).resize((64, 64)   )
								lowerCAmelCase_      			=  {
								    '''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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_dummy_components()
								lowerCAmelCase_      			=  self.pipeline_class(**UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )

								lowerCAmelCase_      			=  10.0
								lowerCAmelCase_      			=  4

								lowerCAmelCase_      			=  self.get_dummy_inputs(UpperCamelCase__   )
								lowerCAmelCase_      			=  steps
								lowerCAmelCase_      			=  scale
								lowerCAmelCase_      			=  pipe(**UpperCamelCase__   )[0]

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

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

								lowerCAmelCase_      			=  self.get_dummy_inputs(UpperCamelCase__   )
								lowerCAmelCase_      			=  steps
								lowerCAmelCase_      			=  scale
								lowerCAmelCase_      			=  pipe(**UpperCamelCase__,	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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''   )

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

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

								lowerCAmelCase_      			=  pipe(
								    UpperCamelCase__,	UpperCamelCase__,	control_image=UpperCamelCase__,	generator=UpperCamelCase__,	output_type='''np''',	num_inference_steps=50,	strength=0.6,	)

								lowerCAmelCase_      			=  output.images[0]

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

								lowerCAmelCase_      			=  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










from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A     =			logging.get_logger(__name__)

_A     =			{
    '''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
    # See all ViT models at https://huggingface.co/models?filter=vit
}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   'vit'



		def __init__(     self,	UpperCamelCase__=768,	UpperCamelCase__=12,	UpperCamelCase__=12,	UpperCamelCase__=3072,	UpperCamelCase__="gelu",	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__=0.02,	UpperCamelCase__=1E-12,	UpperCamelCase__=224,	UpperCamelCase__=16,	UpperCamelCase__=3,	UpperCamelCase__=True,	UpperCamelCase__=16,	**UpperCamelCase__,	):







								"""simple docstring"""



								super().__init__(**UpperCamelCase__   )

								lowerCAmelCase_      			=  hidden_size
								lowerCAmelCase_      			=  num_hidden_layers
								lowerCAmelCase_      			=  num_attention_heads
								lowerCAmelCase_      			=  intermediate_size
								lowerCAmelCase_      			=  hidden_act
								lowerCAmelCase_      			=  hidden_dropout_prob
								lowerCAmelCase_      			=  attention_probs_dropout_prob
								lowerCAmelCase_      			=  initializer_range
								lowerCAmelCase_      			=  layer_norm_eps
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  patch_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  qkv_bias
								lowerCAmelCase_      			=  encoder_stride







class  A		(  __UpperCAmelCase     ):
		__snake_case									=   version.parse('1.11'     )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]   )







		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 1E-4




