



"""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        __A       (	A_     ,A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				StableDiffusionControlNetImgaImgPipeline
		lowerCAmelCase			:					Any   	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		lowerCAmelCase			:					List[str]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Tuple   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}    )
		lowerCAmelCase			:					int   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS







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






				"""simple docstring"""



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

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







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[Any]			,_snake_case						:  Any=0						)					->		Any:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    Optional[Any]				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    str				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    List[Any]				=			2
				lowercase__   :    Optional[int]				=			randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,)
				lowercase__   :    str				=			floats_tensor(control_image.shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Optional[Any]				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[Any]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    Union[str, 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 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""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 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				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			:					Union[str, Any]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Dict   	=				frozenset([]    )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    List[Any]				=			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						:  Optional[int]						):
						if isinstance(_snake_case			,torch.nn.Convad						):
								torch.nn.init.normal(m.weight						)
								m.bias.data.fill_(1.0						)

				lowercase__   :    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)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

				torch.manual_seed(0						)
				lowercase__   :    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)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

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

				lowercase__   :    int				=			MultiControlNetModel([controlneta, controlneta]						)

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







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Dict			,_snake_case						:  Union[str, Any]=0						)					->		List[Any]:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    int				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    Dict				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    int				=			2

				lowercase__   :    Optional[Any]				=			[
				    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						)			,),
				]

				lowercase__   :    Dict				=			floats_tensor(control_image[0].shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Dict				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[int]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    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 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.get_dummy_components()
				lowercase__   :    Dict				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)

				lowercase__   :    Optional[Any]				=			10.0
				lowercase__   :    Tuple				=			4

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[Any]				=			steps
				lowercase__   :    Any				=			scale
				lowercase__   :    Optional[Any]				=			pipe(**_snake_case						)[0]

				lowercase__   :    List[str]				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    int				=			scale
				lowercase__   :    List[str]				=			pipe(**_snake_case			,control_guidance_start=0.1			,control_guidance_end=0.2						)[0]

				lowercase__   :    int				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    Dict				=			scale
				lowercase__   :    Dict				=			pipe(**_snake_case			,control_guidance_start=[0.1, 0.3]			,control_guidance_end=[0.2, 0.7]						)[0]

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    List[Any]				=			steps
				lowercase__   :    Optional[int]				=			scale
				lowercase__   :    List[Any]				=			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 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



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







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






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







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






				"""simple docstring"""



				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







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






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			self.get_dummy_components()
				lowercase__   :    Optional[Any]				=			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 					UpperCAmelCase    (	self						:  Any						)					->		int:






				"""simple docstring"""



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







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






				"""simple docstring"""



				lowercase__   :    int				=			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''						)

				lowercase__   :    Any				=			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						)

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

				lowercase__   :    List[Any]				=			pipe(
				    _snake_case			,_snake_case			,control_image=_snake_case			,generator=_snake_case			,output_type='''np'''			,num_inference_steps=50			,strength=0.6			,)

				lowercase__   :    List[Any]				=			output.images[0]

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

				lowercase__   :    Dict				=			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



'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCAmelCase :       Any			   =     [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]







def    a__       (  a__      ,    a__=None      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      True
     while ask_again:
          __SCREAMING_SNAKE_CASE     =      input(a__						)
          try:
               if default is not None and len(a__						) == 0:
                    return default
               return convert_value(a__						) if convert_value is not None else result
          except Exception:
               if error_message is not None:
                    print(a__						)







def    a__       (  a__      ,    a__=[]      ,    a__=None      ,    a__=0						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      BulletMenu(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      menu.run(default_choice=a__						)
     return convert_value(a__						) if convert_value is not None else result







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DynamoBackend(DYNAMO_BACKENDS[value]						).value







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     return {"yes": True, "no": False}[value.lower()]







class 						lowerCAmelCase__   (      argparse.RawDescriptionHelpFormatter					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      super()._format_usage(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      usage.replace("""<command> [<args>] """					,							""""""			)
      return usage






