

# This model implementation is heavily based on:

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 							a__							( UpperCAmelCase						,      UpperCAmelCase						,      UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




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


  def 			_lowercase   (      self     :			Optional[int]		)       ->str:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = 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		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = 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		)
        SCREAMING_SNAKE_CASE			:   int	  = 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		)
        SCREAMING_SNAKE_CASE			:   str	  = 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		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = 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					,					)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = CLIPTextModel(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = 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 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Dict=0		)       ->Any:
        """simple docstring"""







        if str(UpperCAmelCase__		).startswith("""mps"""		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.manual_seed(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Any	  = torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 2
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = randn_tensor(
            (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,					generator=UpperCAmelCase__					,					device=torch.device(UpperCAmelCase__		)					,					)
        SCREAMING_SNAKE_CASE			:   List[str]	  = floats_tensor((1, 3, 3_2, 3_2)					,					rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = init_image.cpu().permute(0					,					2					,					3					,					1		)[0]

        SCREAMING_SNAKE_CASE			:   List[str]	  = Image.fromarray(np.uinta(UpperCAmelCase__		)		).convert("""RGB"""		).resize((6_4, 6_4)		)
        SCREAMING_SNAKE_CASE			:   Dict	  = Image.fromarray(np.uinta(init_image + 4		)		).convert("""RGB"""		).resize((6_4, 6_4)		)

        SCREAMING_SNAKE_CASE			:   Optional[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,
            """mask_image""": mask_image,
            """control_image""": control_image,
        }

        return inputs


  def 			_lowercase   (      self     :			List[str]		)       ->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 			_lowercase   (      self     :			List[str]		)       ->List[str]:
        """simple docstring"""







        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)




  def 			_lowercase   (      self     :			str		)       ->Tuple:
        """simple docstring"""







        self._test_inference_batch_single_identical(expected_max_diff=2e-3		)







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							=StableDiffusionControlNetInpaintPipeline
  UpperCAmelCase__   :			str      							=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCAmelCase__   :			List[Any]      							=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCAmelCase__   :			Dict      							=frozenset([]							)


  def 			_lowercase   (      self     :			Union[str, Any]		)       ->Union[str, Any]:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = 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		)
        SCREAMING_SNAKE_CASE			:   Dict	  = 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		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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					,					)
        SCREAMING_SNAKE_CASE			:   List[str]	  = CLIPTextModel(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = {
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







class 							a__							( UpperCAmelCase						,      UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Optional[int]      							=StableDiffusionControlNetInpaintPipeline
  UpperCAmelCase__   :			List[Any]      							=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCAmelCase__   :			Dict      							=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


  def 			_lowercase   (      self     :			Optional[Any]		)       ->Optional[int]:
        """simple docstring"""







        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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(UpperCAmelCase__     :			Dict		):
              if isinstance(UpperCAmelCase__					,					torch.nn.Convad		):
                    torch.nn.init.normal(m.weight		)
                    m.bias.data.fill_(1.0		)

        SCREAMING_SNAKE_CASE			:   Any	  = 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(UpperCAmelCase__		)

        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE			:   Dict	  = 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(UpperCAmelCase__		)

        torch.manual_seed(0		)
        SCREAMING_SNAKE_CASE			:   int	  = 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		)
        SCREAMING_SNAKE_CASE			:   str	  = 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		)
        SCREAMING_SNAKE_CASE			:   str	  = 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					,					)
        SCREAMING_SNAKE_CASE			:   List[str]	  = CLIPTextModel(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Dict	  = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

        SCREAMING_SNAKE_CASE			:   List[str]	  = MultiControlNetModel([controlneta, controlneta]		)

        SCREAMING_SNAKE_CASE			:   str	  = {
            """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     :			List[str]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Union[str, Any]=0		)       ->Optional[Any]:
        """simple docstring"""







        if str(UpperCAmelCase__		).startswith("""mps"""		):
              SCREAMING_SNAKE_CASE			:   Any	  = torch.manual_seed(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Any	  = torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 2

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = [
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,					generator=UpperCAmelCase__					,					device=torch.device(UpperCAmelCase__		)					,					),
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,					generator=UpperCAmelCase__					,					device=torch.device(UpperCAmelCase__		)					,					),
        ]
        SCREAMING_SNAKE_CASE			:   Any	  = floats_tensor((1, 3, 3_2, 3_2)					,					rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = init_image.cpu().permute(0					,					2					,					3					,					1		)[0]

        SCREAMING_SNAKE_CASE			:   int	  = Image.fromarray(np.uinta(UpperCAmelCase__		)		).convert("""RGB"""		).resize((6_4, 6_4)		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = Image.fromarray(np.uinta(init_image + 4		)		).convert("""RGB"""		).resize((6_4, 6_4)		)

        SCREAMING_SNAKE_CASE			:   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,
            """mask_image""": mask_image,
            """control_image""": control_image,
        }

        return inputs


  def 			_lowercase   (      self     :			Optional[int]		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.get_dummy_components()
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.pipeline_class(**UpperCAmelCase__		)
        pipe.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = 10.0
        SCREAMING_SNAKE_CASE			:   Tuple	  = 4

        SCREAMING_SNAKE_CASE			:   int	  = self.get_dummy_inputs(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = steps
        SCREAMING_SNAKE_CASE			:   Dict	  = scale
        SCREAMING_SNAKE_CASE			:   int	  = pipe(**UpperCAmelCase__		)[0]

        SCREAMING_SNAKE_CASE			:   str	  = self.get_dummy_inputs(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Any	  = steps
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = scale
        SCREAMING_SNAKE_CASE			:   Any	  = pipe(**UpperCAmelCase__					,					control_guidance_start=0.1					,					control_guidance_end=0.2		)[0]

        SCREAMING_SNAKE_CASE			:   Dict	  = self.get_dummy_inputs(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Any	  = steps
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = scale
        SCREAMING_SNAKE_CASE			:   Tuple	  = pipe(**UpperCAmelCase__					,					control_guidance_start=[0.1, 0.3]					,					control_guidance_end=[0.2, 0.7]		)[0]

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.get_dummy_inputs(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = steps
        SCREAMING_SNAKE_CASE			:   Tuple	  = scale
        SCREAMING_SNAKE_CASE			:   Dict	  = 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 			_lowercase   (      self     :			Any		)       ->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     :			Tuple		)       ->int:
        """simple docstring"""







        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)


  def 			_lowercase   (      self     :			Union[str, Any]		)       ->Any:
        """simple docstring"""







        self._test_inference_batch_single_identical(expected_max_diff=2e-3		)




  def 			_lowercase   (      self     :			List[Any]		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = self.get_dummy_components()
        SCREAMING_SNAKE_CASE			:   Tuple	  = 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 							a__							( unittest.TestCase							):







  """simple docstring"""




  def 			_lowercase   (      self     :			str		)       ->str:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			Tuple		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = StableDiffusionControlNetInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-inpainting"""					,					safety_checker=UpperCAmelCase__					,					controlnet=UpperCAmelCase__		)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE			:   List[str]	  = 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)		)

        SCREAMING_SNAKE_CASE			:   Dict	  = """pitch black hole"""

        SCREAMING_SNAKE_CASE			:   int	  = 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)		)

        SCREAMING_SNAKE_CASE			:   Tuple	  = pipe(
            UpperCAmelCase__					,					image=UpperCAmelCase__					,					mask_image=UpperCAmelCase__					,					control_image=UpperCAmelCase__					,					generator=UpperCAmelCase__					,					output_type="""np"""					,					num_inference_steps=3					,					)

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

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

        SCREAMING_SNAKE_CASE			:   str	  = 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 			_lowercase   (      self     :			str		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""		)

        SCREAMING_SNAKE_CASE			:   List[str]	  = StableDiffusionControlNetInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""					,					safety_checker=UpperCAmelCase__					,					controlnet=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = DDIMScheduler.from_config(pipe.scheduler.config		)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Any	  = torch.Generator(device="""cpu"""		).manual_seed(3_3		)

        SCREAMING_SNAKE_CASE			:   Any	  = load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = init_image.resize((5_1_2, 5_1_2)		)

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

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = """a handsome man with ray-ban sunglasses"""

        def make_inpaint_condition(UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			List[str]		):
              SCREAMING_SNAKE_CASE			:   List[str]	  = np.array(image.convert("""RGB"""		)		).astype(np.floataa		) / 2_55.0
              SCREAMING_SNAKE_CASE			:   List[Any]	  = 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"
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = -1.0  # set as masked pixel
              SCREAMING_SNAKE_CASE			:   int	  = np.expand_dims(UpperCAmelCase__					,					0		).transpose(0					,					3					,					1					,					2		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = torch.from_numpy(UpperCAmelCase__		)
              return image

        SCREAMING_SNAKE_CASE			:   Dict	  = make_inpaint_condition(UpperCAmelCase__					,					UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Any	  = pipe(
            UpperCAmelCase__					,					image=UpperCAmelCase__					,					mask_image=UpperCAmelCase__					,					control_image=UpperCAmelCase__					,					guidance_scale=9.0					,					eta=1.0					,					generator=UpperCAmelCase__					,					num_inference_steps=2_0					,					output_type="""np"""					,					)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = output.images[0]

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

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__			:  List[Any]					     =     logging.get_logger(__name__)

UpperCAmelCase__			:  int					     =     {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase__			:  Tuple					     =     {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase__			:  int					     =     {"""Helsinki-NLP/opus-mt-en-de""": 512}
UpperCAmelCase__			:  Union[str, Any]					     =     {}







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Union[str, Any]      							=VOCAB_FILES_NAMES
  UpperCAmelCase__   :			List[Any]      							=PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__   :			int      							=PRETRAINED_INIT_CONFIGURATION
  UpperCAmelCase__   :			int      							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__   :			Tuple      							=["""input_ids""", """attention_mask"""]
  UpperCAmelCase__   :			Tuple      							=re.compile(""">>.+<<"""							)  # type: re.Pattern


  def __init__(      self     :			Dict					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Union[str, Any]=None					,					UpperCAmelCase__     :			Optional[Any]=None					,					UpperCAmelCase__     :			Tuple=None					,					UpperCAmelCase__     :			Optional[Any]="<unk>"					,					UpperCAmelCase__     :			List[str]="</s>"					,					UpperCAmelCase__     :			Any="<pad>"					,					UpperCAmelCase__     :			Union[str, Any]=5_1_2					,					UpperCAmelCase__     :			Optional[Dict[str, Any]] = None					,					UpperCAmelCase__     :			Dict=False					,					**UpperCAmelCase__     :			List[str]					,					)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
            source_lang=UpperCAmelCase__					,					target_lang=UpperCAmelCase__					,					unk_token=UpperCAmelCase__					,					eos_token=UpperCAmelCase__					,					pad_token=UpperCAmelCase__					,					model_max_length=UpperCAmelCase__					,					sp_model_kwargs=self.sp_model_kwargs					,					target_vocab_file=UpperCAmelCase__					,					separate_vocabs=UpperCAmelCase__					,					**UpperCAmelCase__					,					)
        assert Path(UpperCAmelCase__		).exists(), f"cannot find spm source {source_spm}"

        SCREAMING_SNAKE_CASE			:   Any	  = separate_vocabs
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = load_json(UpperCAmelCase__		)
        if self.unk_token not in self.encoder:
              raise KeyError("""<unk> token must be in vocab"""		)
        assert self.pad_token in self.encoder

        if separate_vocabs:
              SCREAMING_SNAKE_CASE			:   str	  = load_json(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   str	  = {v: k for k, v in self.target_encoder.items()}
              SCREAMING_SNAKE_CASE			:   Tuple	  = []
        else:
              SCREAMING_SNAKE_CASE			:   List[str]	  = {v: k for k, v in self.encoder.items()}
              SCREAMING_SNAKE_CASE			:   list	  = [k for k in self.encoder if k.startswith(""">>"""		) and k.endswith("""<<"""		)]

        SCREAMING_SNAKE_CASE			:   Dict	  = source_lang
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = target_lang
        SCREAMING_SNAKE_CASE			:   str	  = [source_spm, target_spm]

        # load SentencePiece model for pre-processing
        SCREAMING_SNAKE_CASE			:   int	  = load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		)
        SCREAMING_SNAKE_CASE			:   Dict	  = load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.spm_source
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.encoder

        # Multilingual target side: default to using first supported language code.

        self._setup_normalizer()


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        try:
              from sacremoses import MosesPunctNormalizer

              SCREAMING_SNAKE_CASE			:   Optional[int]	  = MosesPunctNormalizer(self.source_lang		).normalize
        except (ImportError, FileNotFoundError):
              warnings.warn("""Recommended: pip install sacremoses."""		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = lambda UpperCAmelCase__		: x


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str		)       ->str:
        """simple docstring"""







        return self.punc_normalizer(UpperCAmelCase__		) if x else ""


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->Dict:
        """simple docstring"""







        return self.current_encoder.get(UpperCAmelCase__					,					self.current_encoder[self.unk_token]		)


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			str		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.language_code_re.match(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   list	  = [match.group(0		)] if match else []
        return code, self.language_code_re.sub(""""""					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[str]					,					UpperCAmelCase__     :			str		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   str	  = self.remove_language_code(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = self.current_spm.encode(UpperCAmelCase__					,					out_type=UpperCAmelCase__		)
        return code + pieces


  def 			_lowercase   (      self     :			Dict					,					UpperCAmelCase__     :			int		)       ->str:
        """simple docstring"""







        return self.decoder.get(UpperCAmelCase__					,					self.unk_token		)


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			List[str]					,					**UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """simple docstring"""







        return super().batch_decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[Any]		)       ->Optional[int]:
        """simple docstring"""







        return super().decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			List[str]		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Tuple	  = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = []
        SCREAMING_SNAKE_CASE			:   Tuple	  = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += sp_model.decode_pieces(UpperCAmelCase__		) + token + " "
                    SCREAMING_SNAKE_CASE			:   List[str]	  = []
              else:
                    current_sub_tokens.append(UpperCAmelCase__		)
        out_string += sp_model.decode_pieces(UpperCAmelCase__		)
        return out_string.strip()


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Union[str, Any]=None		)       ->List[int]:
        """simple docstring"""







        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]


  def 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = self.spm_source
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.encoder


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.spm_target
        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = self.target_encoder


  @property
  def 			_lowercase   (      self     :			List[Any]		)       ->int:
        """simple docstring"""







        return len(self.encoder		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[str] = None		)       ->Tuple[str]:
        """simple docstring"""







        if not os.path.isdir(UpperCAmelCase__		):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
              return
        SCREAMING_SNAKE_CASE			:   Tuple	  = []

        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   Dict	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]					,					)
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]					,					)
              save_json(self.encoder					,					UpperCAmelCase__		)
              save_json(self.target_encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]		)
              save_json(self.encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)

        for spm_save_filename, spm_orig_path, spm_model in zip(
            [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]					,					self.spm_files					,					[self.spm_source, self.spm_target]					,					):
              SCREAMING_SNAKE_CASE			:   Any	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename		)
              if os.path.abspath(UpperCAmelCase__		) != os.path.abspath(UpperCAmelCase__		) and os.path.isfile(UpperCAmelCase__		):
                    copyfile(UpperCAmelCase__					,					UpperCAmelCase__		)
                    saved_files.append(UpperCAmelCase__		)
              elif not os.path.isfile(UpperCAmelCase__		):
                    with open(UpperCAmelCase__					,					"""wb"""		) as fi:
                          SCREAMING_SNAKE_CASE			:   List[str]	  = spm_model.serialized_model_proto()
                          fi.write(UpperCAmelCase__		)
                    saved_files.append(UpperCAmelCase__		)

        return tuple(UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int		)       ->Dict:
        """simple docstring"""







        return self.get_src_vocab()


  def 			_lowercase   (      self     :			str		)       ->Union[str, Any]:
        """simple docstring"""







        return dict(self.encoder					,					**self.added_tokens_encoder		)


  def 			_lowercase   (      self     :			str		)       ->Tuple:
        """simple docstring"""







        return dict(self.target_encoder					,					**self.added_tokens_decoder		)


  def __getstate__(      self     :			Optional[int]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.__dict__.copy()
        state.update(
            {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}		)
        return state


  def __setstate__(      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = d

        # for backward compatibility
        if not hasattr(self					,					"""sp_model_kwargs"""		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = {}

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[Any]	  = (load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		) for f in self.spm_files)
        SCREAMING_SNAKE_CASE			:   Any	  = self.spm_source
        self._setup_normalizer()


  def 			_lowercase   (      self     :			Dict					,					*UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[str]		)       ->Dict:
        """simple docstring"""







        return 1


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = set(self.all_special_ids		)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special
        return [1 if x in all_special_ids else 0 for x in seq]




  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			List					,					UpperCAmelCase__     :			Optional[List] = None					,					UpperCAmelCase__     :			bool = False		)       ->List[int]:
        """simple docstring"""







        if already_has_special_tokens:
              return self._special_token_mask(UpperCAmelCase__		)
        elif token_ids_a is None:
              return self._special_token_mask(UpperCAmelCase__		) + [1]
        else:
              return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






def        __lowercase   (  _A    ,     _A      )  ->    sentencepiece.SentencePieceProcessor:
      SCREAMING_SNAKE_CASE			:   List[str]	  = sentencepiece.SentencePieceProcessor(**_A      )
      spm.Load(_A      )
      return spm






def        __lowercase   (  _A    ,     _A      )  ->    None:
      with open(_A    ,     """w"""      ) as f:
            json.dump(_A    ,     _A    ,     indent=2      )







def        __lowercase   (  _A      )  ->    Union[Dict, List]:
      with open(_A    ,     """r"""      ) as f:
            return json.load(_A      )




