



'''simple docstring'''






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__:







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Optional[int]						,      __snake_case			:  Tuple=sys.maxsize     ):
      a						: Optional[int]					       =					"""bilinear"""
      a						: Any					       =					max_size
      a						: Union[str, Any]					       =					short_edge_length







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

        if img.dtype == np.uinta:
          a						: str					       =					Image.fromarray(SCREAMING_SNAKE_CASE_     )
          a						: Dict					       =					pil_image.resize((neww, newh)						,      PILImageResampling.BILINEAR     )
          a						: Union[str, Any]					       =					np.asarray(SCREAMING_SNAKE_CASE_     )
        else:
          a						: List[Any]					       =					img.permute(2						,      0						,      1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
          a						: Dict					       =					nn.functional.interpolate(
              SCREAMING_SNAKE_CASE_						,      (newh, neww)						,      mode=self.interp_method						,      align_corners=SCREAMING_SNAKE_CASE_     ).squeeze(0     )
        img_augs.append(SCREAMING_SNAKE_CASE_     )

      return img_augs

class 							a__:







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Union[str, Any]     ):
      a						: Optional[int]					       =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,      cfg.INPUT.MAX_SIZE_TEST     )
      a						: Union[str, Any]					       =					cfg.INPUT.FORMAT
      a						: List[Any]					       =					cfg.SIZE_DIVISIBILITY
      a						: str					       =					cfg.PAD_VALUE
      a						: Optional[Any]					       =					cfg.INPUT.MAX_SIZE_TEST
      a						: Optional[Any]					       =					cfg.MODEL.DEVICE
      a						: List[str]					       =					torch.tensor(cfg.MODEL.PIXEL_STD     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,      1						,      1     )
      a						: Union[str, Any]					       =					torch.tensor(cfg.MODEL.PIXEL_MEAN     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,      1						,      1     )
      a						: Tuple					       =					lambda __snake_case     : (x - self.pixel_mean) / self.pixel_std







    def       lowercase_	(				self			:  str						,      __snake_case			:  Any     ):
      a						: Optional[Any]					       =					tuple(max(SCREAMING_SNAKE_CASE_     ) for s in zip(*[img.shape for img in images]     )     )
      a						: List[Any]					       =					[im.shape[-2:] for im in images]
      a						: Optional[int]					       =					[
          nn.functional.pad(
              SCREAMING_SNAKE_CASE_						,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,      value=self.pad_value						,      )
          for size, im in zip(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_     )
      ]

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







    def __call__(				self			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[int]=False     ):
      with torch.no_grad():
        if not isinstance(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_     ):
          a						: str					       =					[images]
        if single_image:
          assert len(SCREAMING_SNAKE_CASE_     ) == 1
        for i in range(len(SCREAMING_SNAKE_CASE_     )     ):
          if isinstance(images[i]						,      torch.Tensor     ):
            images.insert(SCREAMING_SNAKE_CASE_						,      images.pop(SCREAMING_SNAKE_CASE_     ).to(self.device     ).float()     )
          elif not isinstance(images[i]						,      torch.Tensor     ):
            images.insert(
                SCREAMING_SNAKE_CASE_						,      torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE_     )						,      input_format=self.input_format     )     )
                .to(self.device     )
                .float()						,      )
            # resize smallest edge
        a						: List[Any]					       =					torch.tensor([im.shape[:2] for im in images]     )
        a						: Any					       =					self.aug(SCREAMING_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
        a						: List[Any]					       =					[self.normalizer(SCREAMING_SNAKE_CASE_     ) for x in images]
        # now pad them to do the following operations
        a						: Union[str, Any]					       =					self.pad(SCREAMING_SNAKE_CASE_     )
        # Normalize

        if self.size_divisibility > 0:
          raise NotImplementedError()
        # pad
        a						: Optional[int]					       =					torch.true_divide(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_     )
        if single_image:
          return images[0], sizes[0], scales_yx[0]
        else:
          return images, sizes, scales_yx
def 				lowerCamelCase__	(						_A			,      _A    ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes

def 				lowerCamelCase__	(						_A			,      _A    ):
  assert torch.isfinite(lowerCAmelCase__    ).all(), "Box tensor contains infinite or NaN!"
  a						: Dict					       =					box_size
  tensor[:, 0].clamp_(min=0			,      max=lowerCAmelCase__    )
  tensor[:, 1].clamp_(min=0			,      max=lowerCAmelCase__    )
  tensor[:, 2].clamp_(min=0			,      max=lowerCAmelCase__    )
  tensor[:, 3].clamp_(min=0			,      max=lowerCAmelCase__    )




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase: Optional[int]							=						{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: int							=						[
         'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GraphormerForGraphClassification',
         'GraphormerModel',
         'GraphormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_graphormer import (
              GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              GraphormerForGraphClassification,
              GraphormerModel,
              GraphormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: str							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)