






"""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 		snake_case						:





    def __init__(							self			:      Union[str, Any]       ,	A			:      Optional[Any]       ,	A			:      Dict=sys.maxsize    ):


      '''simple docstring'''




      a  :    Dict       =     'bilinear'
      a  :    Any       =     max_size
      a  :    Any       =     short_edge_length





    def __call__(							self			:      Dict       ,	A			:      Tuple    ):


      '''simple docstring'''




      a  :    str       =     []
      for img in imgs:
        a,  a  :    List[str]       =     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  :    int       =     size * 1.0 / min(A       ,	A    )
        if h < w:
          a,  a  :    Optional[int]       =     size, scale * w
        else:
          a,  a  :    Optional[int]       =     scale * h, size
        if max(A       ,	A    ) > self.max_size:
          a  :    List[Any]       =     self.max_size * 1.0 / max(A       ,	A    )
          a  :    List[Any]       =     newh * scale
          a  :    Dict       =     neww * scale
        a  :    int       =     int(neww + 0.5    )
        a  :    Any       =     int(newh + 0.5    )

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

      return img_augs






class 		snake_case						:





    def __init__(							self			:      int       ,	A			:      Dict    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      List[str]       ,	A			:      List[str]    ):


      '''simple docstring'''




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

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





    def __call__(							self			:      List[str]       ,	A			:      Any       ,	A			:      Optional[Any]=False    ):


      '''simple docstring'''




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

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




def     snake_case						(A_   :Optional[Any]			,    A_   :List[str]   ):







  '''simple docstring'''

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






def     snake_case						(A_   :List[str]			,    A_   :Tuple[int, int]   ):







  '''simple docstring'''

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












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					int									=		{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Optional[Any]									=		[
       'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TimeSeriesTransformerForPrediction',
       'TimeSeriesTransformerModel',
       'TimeSeriesTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_time_series_transformer import (
       TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TimeSeriesTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_time_series_transformer import (
          TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TimeSeriesTransformerForPrediction,
          TimeSeriesTransformerModel,
          TimeSeriesTransformerPreTrainedModel,
      )

else:
   import sys

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




