




'''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   :





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a=sys.maxsize       )			->							Any:
          snake_case :			Optional[int]					   = """bilinear"""
          snake_case :			Tuple					   = max_size
          snake_case :			str					   = short_edge_length







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

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

          return img_augs







class 				_a   :





     '''simple docstring'''



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







     def  snake_case_			(							self       ,__a       )			->							Dict:
          snake_case :			int					   = tuple(max(__a       ) for s in zip(*[img.shape for img in images]       )       )
          snake_case :			Tuple					   = [im.shape[-2:] for im in images]
          snake_case :			Any					   = [
              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       ,__a       ,__a=False       )			->							Any:
          with torch.no_grad():
               if not isinstance(__a       ,__a       ):
                    snake_case :			int					   = [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
               snake_case :			Union[str, Any]					   = torch.tensor([im.shape[:2] for im in images]       )
               snake_case :			Union[str, 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
               snake_case :			List[Any]					   = [self.normalizer(__a       ) for x in images]
               # now pad them to do the following operations
               snake_case   ,		snake_case :			Dict					   = self.pad(__a       )
               # Normalize

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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







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









'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase			:      Tuple			=					{"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Tuple			=					["""BertGenerationTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      List[Any]			=					[
          """BertGenerationDecoder""",
          """BertGenerationEncoder""",
          """BertGenerationPreTrainedModel""",
          """load_tf_weights_in_bert_generation""",
      ]


if TYPE_CHECKING:
      from .configuration_bert_generation import BertGenerationConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_generation import BertGenerationTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert_generation import (
                BertGenerationDecoder,
                BertGenerationEncoder,
                BertGenerationPreTrainedModel,
                load_tf_weights_in_bert_generation,
            )

else:
      import sys

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



