
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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class 	lowerCamelCase						(    _lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase				,					unittest.TestCase    ):
     UpperCAmelCase__							:				List[Any] 	=						StableDiffusionControlNetInpaintPipeline
     UpperCAmelCase__							:				Dict 	=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     UpperCAmelCase__							:				str 	=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__							:				Optional[int] 	=						frozenset({"control_image"}    )  # skip `image` and `mask` for now, only test for control_image
     UpperCAmelCase__							:				int 	=						TEXT_TO_IMAGE_IMAGE_PARAMS





     def 						UpperCAmelCase(self :       Tuple  )		->		Tuple:
         torch.manual_seed(0  )
         snake_case						=   UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       sample_size=3_2							,       in_channels=9							,       out_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,       cross_attention_dim=3_2							,       )
         torch.manual_seed(0  )
         snake_case						=   ControlNetModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       in_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       cross_attention_dim=3_2							,       conditioning_embedding_out_channels=(1_6, 3_2)							,       )
         torch.manual_seed(0  )
         snake_case						=   DDIMScheduler(
             beta_start=0.0_00_85							,       beta_end=0.0_12							,       beta_schedule="scaled_linear"							,       clip_sample=_A							,       set_alpha_to_one=_A							,       )
         torch.manual_seed(0  )
         snake_case						=   AutoencoderKL(
             block_out_channels=[3_2, 6_4]							,       in_channels=3							,       out_channels=3							,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,       latent_channels=4							,       )
         torch.manual_seed(0  )
         snake_case						=   CLIPTextConfig(
             bos_token_id=0							,       eos_token_id=2							,       hidden_size=3_2							,       intermediate_size=3_7							,       layer_norm_eps=1E-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_0_0_0							,       )
         snake_case						=   CLIPTextModel(_A  )
         snake_case						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

         snake_case						=   {
             "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							,       _A :       Any							,       _A :       List[Any]=0  )		->		Optional[Any]:


         if str(_A  ).startswith("mps"  ):
             snake_case						=   torch.manual_seed(_A  )
         else:
             snake_case						=   torch.Generator(device=_A  ).manual_seed(_A  )

         snake_case						=   2
         snake_case						=   randn_tensor(
             (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,       generator=_A							,       device=torch.device(_A  )							,       )
         snake_case						=   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(_A  )  ).to(_A  )
         snake_case						=   init_image.cpu().permute(0							,       2							,       3							,       1  )[0]

         snake_case						=   Image.fromarray(np.uinta(_A  )  ).convert("RGB"  ).resize((6_4, 6_4)  )
         snake_case						=   Image.fromarray(np.uinta(init_image + 4  )  ).convert("RGB"  ).resize((6_4, 6_4)  )

         snake_case						=   {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "image": image,
             "mask_image": mask_image,
             "control_image": control_image,
         }

         return inputs





     def 						UpperCAmelCase(self :       str  )		->		Union[str, Any]:
         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 :       Any  )		->		List[Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )





     def 						UpperCAmelCase(self :       str  )		->		List[Any]:
         self._test_inference_batch_single_identical(expected_max_diff=2E-3  )







class 	lowerCamelCase						(    _lowerCamelCase    ):
     UpperCAmelCase__							:				List[Any] 	=						StableDiffusionControlNetInpaintPipeline
     UpperCAmelCase__							:				List[str] 	=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     UpperCAmelCase__							:				Dict 	=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__							:				Optional[int] 	=						frozenset([]    )





     def 						UpperCAmelCase(self :       List[str]  )		->		str:
         torch.manual_seed(0  )
         snake_case						=   UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       sample_size=3_2							,       in_channels=4							,       out_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,       cross_attention_dim=3_2							,       )
         torch.manual_seed(0  )
         snake_case						=   ControlNetModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       in_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       cross_attention_dim=3_2							,       conditioning_embedding_out_channels=(1_6, 3_2)							,       )
         torch.manual_seed(0  )
         snake_case						=   DDIMScheduler(
             beta_start=0.0_00_85							,       beta_end=0.0_12							,       beta_schedule="scaled_linear"							,       clip_sample=_A							,       set_alpha_to_one=_A							,       )
         torch.manual_seed(0  )
         snake_case						=   AutoencoderKL(
             block_out_channels=[3_2, 6_4]							,       in_channels=3							,       out_channels=3							,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,       latent_channels=4							,       )
         torch.manual_seed(0  )
         snake_case						=   CLIPTextConfig(
             bos_token_id=0							,       eos_token_id=2							,       hidden_size=3_2							,       intermediate_size=3_7							,       layer_norm_eps=1E-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_0_0_0							,       )
         snake_case						=   CLIPTextModel(_A  )
         snake_case						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

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







class 	lowerCamelCase						(    _lowerCamelCase				,					_lowerCamelCase				,					unittest.TestCase    ):
     UpperCAmelCase__							:				Dict 	=						StableDiffusionControlNetInpaintPipeline
     UpperCAmelCase__							:				List[Any] 	=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     UpperCAmelCase__							:				List[Any] 	=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





     def 						UpperCAmelCase(self :       Optional[int]  )		->		Any:
         torch.manual_seed(0  )
         snake_case						=   UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       sample_size=3_2							,       in_channels=9							,       out_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,       cross_attention_dim=3_2							,       )
         torch.manual_seed(0  )

         def init_weights(_A :       List[str]  ):
             if isinstance(_A							,       torch.nn.Convad  ):
                 torch.nn.init.normal(m.weight  )
                 m.bias.data.fill_(1.0  )

         snake_case						=   ControlNetModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       in_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       cross_attention_dim=3_2							,       conditioning_embedding_out_channels=(1_6, 3_2)							,       )
         controlneta.controlnet_down_blocks.apply(_A  )

         torch.manual_seed(0  )
         snake_case						=   ControlNetModel(
             block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       in_channels=4							,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,       cross_attention_dim=3_2							,       conditioning_embedding_out_channels=(1_6, 3_2)							,       )
         controlneta.controlnet_down_blocks.apply(_A  )

         torch.manual_seed(0  )
         snake_case						=   DDIMScheduler(
             beta_start=0.0_00_85							,       beta_end=0.0_12							,       beta_schedule="scaled_linear"							,       clip_sample=_A							,       set_alpha_to_one=_A							,       )
         torch.manual_seed(0  )
         snake_case						=   AutoencoderKL(
             block_out_channels=[3_2, 6_4]							,       in_channels=3							,       out_channels=3							,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,       latent_channels=4							,       )
         torch.manual_seed(0  )
         snake_case						=   CLIPTextConfig(
             bos_token_id=0							,       eos_token_id=2							,       hidden_size=3_2							,       intermediate_size=3_7							,       layer_norm_eps=1E-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_0_0_0							,       )
         snake_case						=   CLIPTextModel(_A  )
         snake_case						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

         snake_case						=   MultiControlNetModel([controlneta, controlneta]  )

         snake_case						=   {
             "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 :       List[Any]							,       _A :       Tuple							,       _A :       Tuple=0  )		->		Any:


         if str(_A  ).startswith("mps"  ):
             snake_case						=   torch.manual_seed(_A  )
         else:
             snake_case						=   torch.Generator(device=_A  ).manual_seed(_A  )

         snake_case						=   2

         snake_case						=   [
             randn_tensor(
                 (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,       generator=_A							,       device=torch.device(_A  )							,       ),
             randn_tensor(
                 (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,       generator=_A							,       device=torch.device(_A  )							,       ),
         ]
         snake_case						=   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(_A  )  ).to(_A  )
         snake_case						=   init_image.cpu().permute(0							,       2							,       3							,       1  )[0]

         snake_case						=   Image.fromarray(np.uinta(_A  )  ).convert("RGB"  ).resize((6_4, 6_4)  )
         snake_case						=   Image.fromarray(np.uinta(init_image + 4  )  ).convert("RGB"  ).resize((6_4, 6_4)  )

         snake_case						=   {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "image": image,
             "mask_image": mask_image,
             "control_image": control_image,
         }

         return inputs





     def 						UpperCAmelCase(self :       Any  )		->		Optional[Any]:
         snake_case						=   self.get_dummy_components()
         snake_case						=   self.pipeline_class(**_A  )
         pipe.to(_A  )

         snake_case						=   10.0
         snake_case						=   4

         snake_case						=   self.get_dummy_inputs(_A  )
         snake_case						=   steps
         snake_case						=   scale
         snake_case						=   pipe(**_A  )[0]

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

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

         snake_case						=   self.get_dummy_inputs(_A  )
         snake_case						=   steps
         snake_case						=   scale
         snake_case						=   pipe(**_A							,       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 :       Union[str, Any]  )		->		Optional[int]:
         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  )		->		Tuple:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )





     def 						UpperCAmelCase(self :       Any  )		->		str:
         self._test_inference_batch_single_identical(expected_max_diff=2E-3  )





     def 						UpperCAmelCase(self :       List[str]  )		->		str:
         snake_case						=   self.get_dummy_components()
         snake_case						=   self.pipeline_class(**_A  )
         pipe.to(_A  )
         pipe.set_progress_bar_config(disable=_A  )
         with tempfile.TemporaryDirectory() as tmpdir:
             try:
                 # save_pretrained is not implemented for Multi-ControlNet
                 pipe.save_pretrained(_A  )
             except NotImplementedError:
                 pass







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





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		List[Any]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 						UpperCAmelCase(self :       List[Any]  )		->		Union[str, Any]:
         snake_case						=   ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"  )

         snake_case						=   StableDiffusionControlNetInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"							,       safety_checker=_A							,       controlnet=_A  )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=_A  )

         snake_case						=   torch.Generator(device="cpu"  ).manual_seed(0  )
         snake_case						=   load_image(
             "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"  ).resize((5_1_2, 5_1_2)  )

         snake_case						=   load_image(
             "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
             "/stable_diffusion_inpaint/input_bench_mask.png"  ).resize((5_1_2, 5_1_2)  )

         snake_case						=   "pitch black hole"

         snake_case						=   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  ).resize((5_1_2, 5_1_2)  )

         snake_case						=   pipe(
             _A							,       image=_A							,       mask_image=_A							,       control_image=_A							,       generator=_A							,       output_type="np"							,       num_inference_steps=3							,       )

         snake_case						=   output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         snake_case						=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"  )

         assert np.abs(expected_image - image  ).max() < 9E-2





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		int:
         snake_case						=   ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"  )

         snake_case						=   StableDiffusionControlNetInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"							,       safety_checker=_A							,       controlnet=_A  )
         snake_case						=   DDIMScheduler.from_config(pipe.scheduler.config  )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=_A  )

         snake_case						=   torch.Generator(device="cpu"  ).manual_seed(3_3  )

         snake_case						=   load_image(
             "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"  )
         snake_case						=   init_image.resize((5_1_2, 5_1_2)  )

         snake_case						=   load_image(
             "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"  )
         snake_case						=   mask_image.resize((5_1_2, 5_1_2)  )

         snake_case						=   "a handsome man with ray-ban sunglasses"

         def make_inpaint_condition(_A :       int							,       _A :       Optional[int]  ):
             snake_case						=   np.array(image.convert("RGB"  )  ).astype(np.floataa  ) / 2_55.0
             snake_case						=   np.array(image_mask.convert("L"  )  ).astype(np.floataa  ) / 2_55.0

             assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
             snake_case						=   -1.0  # set as masked pixel
             snake_case						=   np.expand_dims(_A							,       0  ).transpose(0							,       3							,       1							,       2  )
             snake_case						=   torch.from_numpy(_A  )
             return image

         snake_case						=   make_inpaint_condition(_A							,       _A  )

         snake_case						=   pipe(
             _A							,       image=_A							,       mask_image=_A							,       control_image=_A							,       guidance_scale=9.0							,       eta=1.0							,       generator=_A							,       num_inference_steps=2_0							,       output_type="np"							,       )
         snake_case						=   output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         snake_case						=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"  )

         assert np.abs(expected_image - image  ).max() < 9E-2




"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






