


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case=sys.maxsize					):
         snake_case					=       '''bilinear'''
         snake_case					=       max_size
         snake_case					=       short_edge_length




    def __call__( self    ,      __snake_case					):
         snake_case					=       []
         for img in imgs:
              snake_case				,						snake_case					=       img.shape[:2]
              # later: provide list and randomly choose index for resize
              snake_case					=       np.random.randint(self.short_edge_length[0]    ,      self.short_edge_length[1] + 1					)
              if size == 0:
                   return img
              snake_case					=       size * 1.0 / min(__snake_case    ,      __snake_case					)
              if h < w:
                   snake_case				,						snake_case					=       size, scale * w
              else:
                   snake_case				,						snake_case					=       scale * h, size
              if max(__snake_case    ,      __snake_case					) > self.max_size:
                   snake_case					=       self.max_size * 1.0 / max(__snake_case    ,      __snake_case					)
                   snake_case					=       newh * scale
                   snake_case					=       neww * scale
              snake_case					=       int(neww + 0.5					)
              snake_case					=       int(newh + 0.5					)

              if img.dtype == np.uinta:
                   snake_case					=       Image.fromarray(__snake_case					)
                   snake_case					=       pil_image.resize((neww, newh)    ,      PILImageResampling.BILINEAR					)
                   snake_case					=       np.asarray(__snake_case					)
              else:
                   snake_case					=       img.permute(2    ,      0    ,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
                   snake_case					=       nn.functional.interpolate(
                       __snake_case    ,      (newh, neww)    ,      mode=self.interp_method    ,      align_corners=__snake_case					).squeeze(0					)
              img_augs.append(__snake_case					)

         return img_augs






class 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case					):
         snake_case					=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,      cfg.INPUT.MAX_SIZE_TEST					)
         snake_case					=       cfg.INPUT.FORMAT
         snake_case					=       cfg.SIZE_DIVISIBILITY
         snake_case					=       cfg.PAD_VALUE
         snake_case					=       cfg.INPUT.MAX_SIZE_TEST
         snake_case					=       cfg.MODEL.DEVICE
         snake_case					=       torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)    ,      1    ,      1					)
         snake_case					=       torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)    ,      1    ,      1					)
         snake_case					=       lambda __snake_case					: (x - self.pixel_mean) / self.pixel_std
    def 	a_    ( self    ,      __snake_case					):
         snake_case					=       tuple(max(__snake_case					) for s in zip(*[img.shape for img in images]					)					)
         snake_case					=       [im.shape[-2:] for im in images]
         snake_case					=       [
             nn.functional.pad(
                 __snake_case    ,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,      value=self.pad_value    ,      )
             for size, im in zip(__snake_case    ,      __snake_case					)
         ]

         return torch.stack(__snake_case					), torch.tensor(__snake_case					)




    def __call__( self    ,      __snake_case    ,      __snake_case=False					):
         with torch.no_grad():
              if not isinstance(__snake_case    ,      __snake_case					):
                   snake_case					=       [images]
              if single_image:
                   assert len(__snake_case					) == 1
              for i in range(len(__snake_case					)					):
                   if isinstance(images[i]    ,      torch.Tensor					):
                        images.insert(__snake_case    ,      images.pop(__snake_case					).to(self.device					).float()					)
                   elif not isinstance(images[i]    ,      torch.Tensor					):
                        images.insert(
                            __snake_case    ,      torch.as_tensor(img_tensorize(images.pop(__snake_case					)    ,      input_format=self.input_format					)					)
                            .to(self.device					)
                            .float()    ,      )
            # resize smallest edge
              snake_case					=       torch.tensor([im.shape[:2] for im in images]					)
              snake_case					=       self.aug(__snake_case					)
              # transpose images and convert to torch tensors
              # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
              # now normalize before pad to avoid useless arithmetic
              snake_case					=       [self.normalizer(__snake_case					) for x in images]
              # now pad them to do the following operations
              snake_case				,						snake_case					=       self.pad(__snake_case					)
              # Normalize

              if self.size_divisibility > 0:
                   raise NotImplementedError()
              # pad
              snake_case					=       torch.true_divide(__snake_case    ,      __snake_case					)
              if single_image:
                   return images[0], sizes[0], scales_yx[0]
              else:
                   return images, sizes, scales_yx






def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes

def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     assert torch.isfinite(UpperCamelCase_       ).all(), "Box tensor contains infinite or NaN!"
     snake_case				,						snake_case					=       box_size
     tensor[:, 0].clamp_(min=0							,max=UpperCamelCase_       )
     tensor[:, 1].clamp_(min=0							,max=UpperCamelCase_       )
     tensor[:, 2].clamp_(min=0							,max=UpperCamelCase_       )
     tensor[:, 3].clamp_(min=0							,max=UpperCamelCase_       )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




