






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class   a__	(						_lowerCamelCase    ):
       lowerCamelCase__:     Optional[int]							    =     ["""image_processor""", """tokenizer"""]
       lowerCamelCase__:     Tuple							    =     """LayoutLMv3ImageProcessor"""
       lowerCamelCase__:     int							    =     ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")

       def __init__(   self      :     Optional[int]      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							**lowerCamelCase_      :     Any    ):
            a_    :     str										=    None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                     """ instead."""      ,							A__      ,							)
                 a_    :     List[str]										=    kwargs.pop("""feature_extractor"""    )

            a_    :     Optional[int]										=    image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                 raise ValueError("""You need to specify an `image_processor`."""    )
            if tokenizer is None:
                 raise ValueError("""You need to specify a `tokenizer`."""    )

            super().__init__(A__      ,							A__    )

       def __call__(   self      :     Optional[int]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     List[str] = None      ,							lowerCamelCase_      :     Union[str, Any] = None      ,							lowerCamelCase_      :     Tuple = None      ,							lowerCamelCase_      :     Optional[int] = True      ,							lowerCamelCase_      :     List[str] = False      ,							lowerCamelCase_      :     List[str] = None      ,							lowerCamelCase_      :     List[Any] = None      ,							lowerCamelCase_      :     Optional[int] = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     List[Any] = None      ,							lowerCamelCase_      :     Any = False      ,							lowerCamelCase_      :     List[Any] = False      ,							lowerCamelCase_      :     Optional[int] = False      ,							lowerCamelCase_      :     Union[str, Any] = False      ,							lowerCamelCase_      :     Optional[Any] = True      ,							lowerCamelCase_      :     Tuple = None      ,							**lowerCamelCase_      :     List[Any]      ,							):
            # verify input
            if self.image_processor.apply_ocr and (boxes is not None):
                 raise ValueError(
                     """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""    )

            if self.image_processor.apply_ocr and (word_labels is not None):
                 raise ValueError(
                     """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""    )

            # first, apply the image processor
            a_    :     Tuple										=    self.image_processor(images=A__      ,							return_tensors=A__    )

            # second, apply the tokenizer
            if text is not None and self.image_processor.apply_ocr and text_pair is None:
                 if isinstance(A__      ,							A__    ):
                      a_    :     Union[str, Any]										=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
                 a_    :     Union[str, Any]										=    features["""words"""]

            a_    :     Optional[Any]										=    self.tokenizer(
                text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=A__      ,							add_special_tokens=A__      ,							padding=A__      ,							truncation=A__      ,							max_length=A__      ,							stride=A__      ,							pad_to_multiple_of=A__      ,							return_token_type_ids=A__      ,							return_attention_mask=A__      ,							return_overflowing_tokens=A__      ,							return_special_tokens_mask=A__      ,							return_offsets_mapping=A__      ,							return_length=A__      ,							verbose=A__      ,							return_tensors=A__      ,							**A__      ,							)

            # add pixel values
            a_    :     int										=    features.pop("""pixel_values"""    )
            if return_overflowing_tokens is True:
                 a_    :     Tuple										=    self.get_overflowing_images(A__      ,							encoded_inputs["""overflow_to_sample_mapping"""]    )
            a_    :     Dict										=    images

            return encoded_inputs

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple    ):
            # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
            a_    :     List[str]										=    []
            for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]    )

            if len(A__    ) != len(A__    ):
                 raise ValueError(
                     """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                     F''' {len(A__    )} and {len(A__    )}'''    )

            return images_with_overflow

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							*lowerCamelCase_      :     Union[str, Any]      ,							**lowerCamelCase_      :     List[Any]    ):
            return self.tokenizer.batch_decode(*A__      ,							**A__    )

       def 						UpperCAmelCase(   self      :     Any      ,							*lowerCamelCase_      :     Optional[Any]      ,							**lowerCamelCase_      :     List[Any]    ):
            return self.tokenizer.decode(*A__      ,							**A__    )

       @property
       def 						UpperCAmelCase(   self      :     Tuple    ):
            return ["input_ids", "bbox", "attention_mask", "pixel_values"]

       @property
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,							A__      ,							)
            return self.image_processor_class

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,							A__      ,							)
            return self.image_processor







from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase        =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     CLIPSegForImageSegmentation      ,							lowerCamelCase_      :     CLIPSegProcessor      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     CLIPTextModel      ,							lowerCamelCase_      :     CLIPTokenizer      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,							lowerCamelCase_      :     StableDiffusionSafetyChecker      ,							lowerCamelCase_      :     CLIPImageProcessor      ,							):
            super().__init__()

            if hasattr(scheduler.config      ,							"""steps_offset"""    ) and scheduler.config.steps_offset != 1:
                 a_    :     Dict										=    (
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                     """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                     """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                     """ file"""
                 )
                 deprecate("""steps_offset!=1"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    dict(scheduler.config    )
                 a_    :     Optional[int]										=    1
                 a_    :     int										=    FrozenDict(lowerCamelCase_    )

            if hasattr(scheduler.config      ,							"""skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
                 a_    :     List[str]										=    (
                     F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                     """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
                     """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
                     """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
                     """ Hub, it would be very nice if you could open a Pull request for the"""
                     """ `scheduler/scheduler_config.json` file"""
                 )
                 deprecate("""skip_prk_steps not set"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     List[str]										=    dict(scheduler.config    )
                 a_    :     List[str]										=    True
                 a_    :     str										=    FrozenDict(lowerCamelCase_    )

            if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

            self.register_modules(
                segmentation_model=lowerCamelCase_      ,							segmentation_processor=lowerCamelCase_      ,							vae=lowerCamelCase_      ,							text_encoder=lowerCamelCase_      ,							tokenizer=lowerCamelCase_      ,							unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[Union[str, int]] = "auto"    ):
            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 a_    :     Any										=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.enable_attention_slicing(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            a_    :     Any										=    torch.device("""cuda"""    )

            for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                 if cpu_offloaded_model is not None:
                      cpu_offload(lowerCamelCase_      ,							lowerCamelCase_    )

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						UpperCAmelCase(   self      :     int    ):
            if self.device != torch.device("""meta"""    ) or not hasattr(self.unet      ,							"""_hf_hook"""    ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(lowerCamelCase_      ,							"""_hf_hook"""    )
                     and hasattr(module._hf_hook      ,							"""execution_device"""    )
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device    )
            return self.device

       @torch.no_grad()
       def __call__(   self      :     Any      ,							lowerCamelCase_      :     Union[str, List[str]]      ,							lowerCamelCase_      :     Union[torch.FloatTensor, PIL.Image.Image]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     float = 7.5      ,							lowerCamelCase_      :     Optional[Union[str, List[str]]] = None      ,							lowerCamelCase_      :     Optional[int] = 1      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     Optional[torch.Generator] = None      ,							lowerCamelCase_      :     Optional[torch.FloatTensor] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							lowerCamelCase_      :     int = 1      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     str										=    self.segmentation_processor(
                text=[text]      ,							images=[image]      ,							padding="""max_length"""      ,							return_tensors="""pt"""    ).to(self.device    )
            a_    :     List[str]										=    self.segmentation_model(**lowerCamelCase_    )
            a_    :     str										=    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
            a_    :     Optional[int]										=    self.numpy_to_pil(lowerCamelCase_    )[0].resize(image.size    )

            # Run inpainting pipeline with the generated mask
            a_    :     Any										=    StableDiffusionInpaintPipeline(
                vae=self.vae      ,							text_encoder=self.text_encoder      ,							tokenizer=self.tokenizer      ,							unet=self.unet      ,							scheduler=self.scheduler      ,							safety_checker=self.safety_checker      ,							feature_extractor=self.feature_extractor      ,							)
            return inpainting_pipeline(
                prompt=lowerCamelCase_      ,							image=lowerCamelCase_      ,							mask_image=lowerCamelCase_      ,							height=lowerCamelCase_      ,							width=lowerCamelCase_      ,							num_inference_steps=lowerCamelCase_      ,							guidance_scale=lowerCamelCase_      ,							negative_prompt=lowerCamelCase_      ,							num_images_per_prompt=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							output_type=lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							callback=lowerCamelCase_      ,							callback_steps=lowerCamelCase_      ,							)



