






# 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       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       StableDiffusionControlNetImgaImgPipeline
				lowercase__											=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				lowercase__											=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__											=       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}	)
				lowercase__											=       IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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  )
								lowercase__    =			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  )
								lowercase__    =			DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__    =			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  )
								lowercase__    =			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=1_000,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    List[str]=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

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

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )



class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       StableDiffusionControlNetImgaImgPipeline
				lowercase__											=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				lowercase__											=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__											=       frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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(lowerCamelCase						:    Optional[Any]  ):
												if isinstance(lowerCamelCase,  torch.nn.Convad  ):
																torch.nn.init.normal(m.weight  )
																m.bias.data.fill_(1.0  )

								lowercase__    =			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  )
								lowercase__    =			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  )
								lowercase__    =			DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__    =			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  )
								lowercase__    =			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=1_000,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__    =			MultiControlNetModel([controlneta, controlneta]  )

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




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Union[str, Any]=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowercase__    =			2

								lowercase__    =			[
								    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  ),  ),
								]

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

								'''simple docstring'''
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			self.pipeline_class(**lowerCamelCase  )
								pipe.to(lowerCamelCase  )

								lowercase__    =			10.0
								lowercase__    =			4

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			steps
								lowercase__    =			scale
								lowercase__    =			pipe(**lowerCamelCase  )[0]

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

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

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			steps
								lowercase__    =			scale
								lowercase__    =			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 				lowercase__   (     self						:    str  ):

								'''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 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			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       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''  )

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

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

								lowercase__    =			pipe(
								    lowerCamelCase,  lowerCamelCase,  control_image=lowerCamelCase,  generator=lowerCamelCase,  output_type='''np''',  num_inference_steps=50,  strength=0.6,  )

								lowercase__    =			output.images[0]

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

								lowercase__    =			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__	:			Optional[int]			   = {}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Optional[int]			   = ['GPTSw3Tokenizer']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							A__	:			Optional[int]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




