






"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__					: List[Any]=	logging.get_logger(__name__)  # pylint: disable=invalid-name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    warnings.warn(
        'The preprocess method is deprecated and will be removed in a future version. Please'
        ' use VaeImageProcessor.preprocess instead' ,	SCREAMING_SNAKE_CASE ,	)
    if isinstance(SCREAMING_SNAKE_CASE ,	torch.Tensor					):
        return image
    elif isinstance(SCREAMING_SNAKE_CASE ,	PIL.Image.Image					):
        UpperCamelCase__         =    [image]

    if isinstance(image[0] ,	PIL.Image.Image					):
        UpperCamelCase__       ,						UpperCamelCase__         =    image[0].size
        UpperCamelCase__       ,						UpperCamelCase__         =    (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        UpperCamelCase__         =    [np.array(i.resize((w, h) ,	resample=PIL_INTERPOLATION['lanczos']					)					)[None, :] for i in image]
        UpperCamelCase__         =    np.concatenate(SCREAMING_SNAKE_CASE ,	axis=0					)
        UpperCamelCase__         =    np.array(SCREAMING_SNAKE_CASE					).astype(np.floataa					) / 255.0
        UpperCamelCase__         =    image.transpose(0 ,	3 ,	1 ,	2					)
        UpperCamelCase__         =    2.0 * image - 1.0
        UpperCamelCase__         =    torch.from_numpy(SCREAMING_SNAKE_CASE					)
    elif isinstance(image[0] ,	torch.Tensor					):
        UpperCamelCase__         =    torch.cat(SCREAMING_SNAKE_CASE ,	dim=0					)
    return image







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    if isinstance(SCREAMING_SNAKE_CASE ,	torch.Tensor					):
        return mask
    elif isinstance(SCREAMING_SNAKE_CASE ,	PIL.Image.Image					):
        UpperCamelCase__         =    [mask]

    if isinstance(mask[0] ,	PIL.Image.Image					):
        UpperCamelCase__       ,						UpperCamelCase__         =    mask[0].size
        UpperCamelCase__       ,						UpperCamelCase__         =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        UpperCamelCase__         =    [np.array(m.convert('L'					).resize((w, h) ,	resample=PIL_INTERPOLATION['nearest']					)					)[None, :] for m in mask]
        UpperCamelCase__         =    np.concatenate(SCREAMING_SNAKE_CASE ,	axis=0					)
        UpperCamelCase__         =    mask.astype(np.floataa					) / 255.0
        UpperCamelCase__         =    0
        UpperCamelCase__         =    1
        UpperCamelCase__         =    torch.from_numpy(SCREAMING_SNAKE_CASE					)
    elif isinstance(mask[0] ,	torch.Tensor					):
        UpperCamelCase__         =    torch.cat(SCREAMING_SNAKE_CASE ,	dim=0					)
    return mask





class    __lowerCamelCase    (  _a  ):
   a      :				UNetaDModel
   a      :				RePaintScheduler

   def __init__(					self ,				snake_case_ ,				snake_case_      )		->       Union[str, Any]:
       super().__init__()
       self.register_modules(unet=snake_case_ ,				scheduler=snake_case_      )







   @torch.no_grad()
   def __call__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = 250 ,				snake_case_ = 0.0 ,				snake_case_ = 10 ,				snake_case_ = 10 ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				)		->       Union[ImagePipelineOutput, Tuple]:
       UpperCamelCase__         =    image

       UpperCamelCase__         =    _preprocess_image(snake_case_      )
       UpperCamelCase__         =    original_image.to(device=self.device ,				dtype=self.unet.dtype      )
       UpperCamelCase__         =    _preprocess_mask(snake_case_      )
       UpperCamelCase__         =    mask_image.to(device=self.device ,				dtype=self.unet.dtype      )

       UpperCamelCase__         =    original_image.shape[0]

       # sample gaussian noise to begin the loop
       if isinstance(snake_case_ ,				snake_case_      ) and len(snake_case_      ) != batch_size:
           raise ValueError(
               F'You have passed a list of generators of length {len(snake_case_      )}, but requested an effective batch'
               F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

       UpperCamelCase__         =    original_image.shape
       UpperCamelCase__         =    randn_tensor(snake_case_ ,				generator=snake_case_ ,				device=self.device ,				dtype=self.unet.dtype      )

       # set step values
       self.scheduler.set_timesteps(snake_case_ ,				snake_case_ ,				snake_case_ ,				self.device      )
       UpperCamelCase__         =    eta

       UpperCamelCase__         =    self.scheduler.timesteps[0] + 1
       UpperCamelCase__         =    generator[0] if isinstance(snake_case_ ,				snake_case_      ) else generator
       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
           if t < t_last:
               # predict the noise residual
               UpperCamelCase__         =    self.unet(snake_case_ ,				snake_case_      ).sample
               # compute previous image: x_t -> x_t-1
               UpperCamelCase__         =    self.scheduler.step(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      ).prev_sample

           else:
               # compute the reverse: x_t-1 -> x_t
               UpperCamelCase__         =    self.scheduler.undo_step(snake_case_ ,				snake_case_ ,				snake_case_      )
           UpperCamelCase__         =    t

       UpperCamelCase__         =    (image / 2 + 0.5).clamp(0 ,				1      )
       UpperCamelCase__         =    image.cpu().permute(0 ,				2 ,				3 ,				1      ).numpy()
       if output_type == "pil":
           UpperCamelCase__         =    self.numpy_to_pil(snake_case_      )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=snake_case_      )












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


A__					: Any=	logging.get_logger(__name__)

A__					: str=	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""layoutlmv3"""

   def __init__(					self ,				snake_case_=5_0265 ,				snake_case_=768 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=3072 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=2 ,				snake_case_=1024 ,				snake_case_=128 ,				snake_case_=128 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=64 ,				snake_case_=256 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=224 ,				snake_case_=3 ,				snake_case_=16 ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(
           vocab_size=snake_case_ ,				hidden_size=snake_case_ ,				num_hidden_layers=snake_case_ ,				num_attention_heads=snake_case_ ,				intermediate_size=snake_case_ ,				hidden_act=snake_case_ ,				hidden_dropout_prob=snake_case_ ,				attention_probs_dropout_prob=snake_case_ ,				max_position_embeddings=snake_case_ ,				type_vocab_size=snake_case_ ,				initializer_range=snake_case_ ,				layer_norm_eps=snake_case_ ,				pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    max_ad_position_embeddings
       UpperCamelCase__         =    coordinate_size
       UpperCamelCase__         =    shape_size
       UpperCamelCase__         =    has_relative_attention_bias
       UpperCamelCase__         =    rel_pos_bins
       UpperCamelCase__         =    max_rel_pos
       UpperCamelCase__         =    has_spatial_attention_bias
       UpperCamelCase__         =    rel_ad_pos_bins
       UpperCamelCase__         =    max_rel_ad_pos
       UpperCamelCase__         =    text_embed
       UpperCamelCase__         =    visual_embed
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    classifier_dropout







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=version.parse("""1.12"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       # The order of inputs is different for question answering and sequence classification
       if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ]      )
       else:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels'}),
               ]      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-5

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 3 ,				snake_case_ = 40 ,				snake_case_ = 40 ,				)		->       Mapping[str, Any]:
       setattr(processor.image_processor ,				'apply_ocr' ,				snake_case_      )

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_batch ,				num_token_to_add=0      )
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase__         =    processor.tokenizer.num_special_tokens_to_add(snake_case_      )
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_sequence ,				num_token_to_add=snake_case_      )
       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__         =    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase__         =    [[[48, 84, 73, 128]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCamelCase__         =    self._generate_dummy_images(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    dict(
           processor(
               snake_case_ ,				text=snake_case_ ,				boxes=snake_case_ ,				return_tensors=snake_case_ ,				)      )

       return inputs




