


'''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		(lowercase_    ,	lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				StableDiffusionControlNetImgaImgPipeline
      lowerCAmelCase__   :	Any       =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
      lowerCAmelCase__   :	int       =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__   :	Any       =				IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}	)
      lowerCAmelCase__   :	List[Any]       =				IMAGE_TO_IMAGE_IMAGE_PARAMS



      def  UpperCamelCase__						(self       :       Optional[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=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''  ,		clip_sample=UpperCamelCase  ,		set_alpha_to_one=UpperCamelCase  ,		)
             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=1000  ,		)
             lowercase__      =					CLIPTextModel(UpperCamelCase					)
             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  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       int=0					):

             '''simple docstring'''







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

             lowercase__      =					2
             lowercase__      =					randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,		generator=UpperCamelCase  ,		device=torch.device(UpperCamelCase					)  ,		)
             lowercase__      =					floats_tensor(control_image.shape  ,		rng=random.Random(UpperCamelCase					)					).to(UpperCamelCase					)
             lowercase__      =					image.cpu().permute(0  ,		2  ,		3  ,		1					)[0]
             lowercase__      =					Image.fromarray(np.uinta(UpperCamelCase					)					).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  UpperCamelCase__						(self       :       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       :       List[Any]					):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)






      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             self._test_inference_batch_single_identical(expected_max_diff=2E-3					)






class     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Union[str, Any]       =				StableDiffusionControlNetImgaImgPipeline
      lowerCAmelCase__   :	Optional[int]       =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
      lowerCAmelCase__   :	List[str]       =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__   :	Dict       =				frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



      def  UpperCamelCase__						(self       :       Tuple					):

             '''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(UpperCamelCase       :       str					):
                    if isinstance(UpperCamelCase  ,		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(UpperCamelCase					)

             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(UpperCamelCase					)

             torch.manual_seed(0					)
             lowercase__      =					DDIMScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''  ,		clip_sample=UpperCamelCase  ,		set_alpha_to_one=UpperCamelCase  ,		)
             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=1000  ,		)
             lowercase__      =					CLIPTextModel(UpperCamelCase					)
             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  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]=0					):

             '''simple docstring'''







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

             lowercase__      =					2

             lowercase__      =					[
                 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					)  ,		),
             ]

             lowercase__      =					floats_tensor(control_image[0].shape  ,		rng=random.Random(UpperCamelCase					)					).to(UpperCamelCase					)
             lowercase__      =					image.cpu().permute(0  ,		2  ,		3  ,		1					)[0]
             lowercase__      =					Image.fromarray(np.uinta(UpperCamelCase					)					).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  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_components()
             lowercase__      =					self.pipeline_class(**UpperCamelCase					)
             pipe.to(UpperCamelCase					)

             lowercase__      =					10.0
             lowercase__      =					4

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

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

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

             lowercase__      =					self.get_dummy_inputs(UpperCamelCase					)
             lowercase__      =					steps
             lowercase__      =					scale
             lowercase__      =					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  UpperCamelCase__						(self       :       List[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  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             self._test_inference_batch_single_identical(expected_max_diff=2E-3					)






      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_components()
             lowercase__      =					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     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







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






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







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

             lowercase__      =					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					)

             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(
                 UpperCamelCase  ,		UpperCamelCase  ,		control_image=UpperCamelCase  ,		generator=UpperCamelCase  ,		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








'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def _SCREAMING_SNAKE_CASE ()			->			Dict:






       """simple docstring"""







       lowercase__      =					ArgumentParser('''Transformers CLI tool'''    ,					usage='''transformers-cli <command> [<args>]'''			)
       lowercase__      =					parser.add_subparsers(help='''transformers-cli command helpers'''			)

       # Register commands
       ConvertCommand.register_subcommand(A			)
       DownloadCommand.register_subcommand(A			)
       EnvironmentCommand.register_subcommand(A			)
       RunCommand.register_subcommand(A			)
       ServeCommand.register_subcommand(A			)
       UserCommands.register_subcommand(A			)
       AddNewModelCommand.register_subcommand(A			)
       AddNewModelLikeCommand.register_subcommand(A			)
       LfsCommands.register_subcommand(A			)
       PTtoTFCommand.register_subcommand(A			)

       # Let's go
       lowercase__      =					parser.parse_args()

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

       # Run
       lowercase__      =					args.func(A			)
       service.run()


if __name__ == "__main__":
 main()




