





"""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 					_SCREAMING_SNAKE_CASE(   A    ,	A    ,	A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Tuple							=  StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE_   :      str							=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}  )
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  IMAGE_TO_IMAGE_IMAGE_PARAMS




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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							)
             __SCREAMING_SNAKE_CASE   :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)		,)
             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  DDIMScheduler(
                 beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='''scaled_linear'''		,clip_sample=SCREAMING_SNAKE_CASE__		,set_alpha_to_one=SCREAMING_SNAKE_CASE__		,)
             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :Any						=  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							)
             __SCREAMING_SNAKE_CASE   :Dict						=  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		,)
             __SCREAMING_SNAKE_CASE   :Dict						=  CLIPTextModel(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

             __SCREAMING_SNAKE_CASE   :int						=  {
                 '''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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      int:





             """simple docstring"""






             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)

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





             """simple docstring"""






             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             self._test_inference_batch_single_identical(expected_max_diff=2E-3							)






class 					_SCREAMING_SNAKE_CASE(   A    ,	A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       SCREAMING_SNAKE_CASE_   :      List[str]							=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_   :      int							=  frozenset([]  )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :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							)
             __SCREAMING_SNAKE_CASE   :List[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							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  DDIMScheduler(
                 beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='''scaled_linear'''		,clip_sample=SCREAMING_SNAKE_CASE__		,set_alpha_to_one=SCREAMING_SNAKE_CASE__		,)
             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  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							)
             __SCREAMING_SNAKE_CASE   :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=10_00		,)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  CLIPTextModel(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

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

             __SCREAMING_SNAKE_CASE   :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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      Union[str, Any]:





             """simple docstring"""






             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  2

             __SCREAMING_SNAKE_CASE   :int						=  [
                 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__							)		,),
             ]

             __SCREAMING_SNAKE_CASE   :Tuple						=  floats_tensor(control_image[0].shape		,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  image.cpu().permute(0		,2		,3		,1							)[0]
             __SCREAMING_SNAKE_CASE   :Any						=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert('''RGB'''							).resize((64, 64)							)
             __SCREAMING_SNAKE_CASE   :str						=  {
                 '''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							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :str						=  self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
             pipe.to(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  1_0.0
             __SCREAMING_SNAKE_CASE   :int						=  4

             __SCREAMING_SNAKE_CASE   :Any						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  steps
             __SCREAMING_SNAKE_CASE   :Tuple						=  scale
             __SCREAMING_SNAKE_CASE   :Any						=  pipe(**SCREAMING_SNAKE_CASE__							)[0]

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  steps
             __SCREAMING_SNAKE_CASE   :List[Any]						=  scale
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe(**SCREAMING_SNAKE_CASE__		,control_guidance_start=0.1		,control_guidance_end=0.2							)[0]

             __SCREAMING_SNAKE_CASE   :List[str]						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  steps
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  scale
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pipe(**SCREAMING_SNAKE_CASE__		,control_guidance_start=[0.1, 0.3]		,control_guidance_end=[0.2, 0.7]							)[0]

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  steps
             __SCREAMING_SNAKE_CASE   :Any						=  scale
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  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     _UpperCamelCase     (						self							)			->      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]:





             """simple docstring"""






             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             self._test_inference_batch_single_identical(expected_max_diff=2E-3							)




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  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 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




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





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

             __SCREAMING_SNAKE_CASE   :str						=  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__							)

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

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  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		,)

             __SCREAMING_SNAKE_CASE   :List[str]						=  output.images[0]

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

             __SCREAMING_SNAKE_CASE   :Tuple						=  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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def      __lowerCamelCase   (							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ArgumentParser('''Accelerate CLI tool''' ,   usage='''accelerate <command> [<args>]''' ,   allow_abbrev=a_							)
      __SCREAMING_SNAKE_CASE   :str						=  parser.add_subparsers(help='''accelerate command helpers'''							)

      # Register commands
      get_config_parser(subparsers=a_							)
      env_command_parser(subparsers=a_							)
      launch_command_parser(subparsers=a_							)
      tpu_command_parser(subparsers=a_							)
      test_command_parser(subparsers=a_							)

      # Let's go
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      if not hasattr(a_ ,   '''func'''							):
            parser.print_help()
            exit(1							)

      # Run
      args.func(a_							)


if __name__ == "__main__":
  main()