


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					: Union[str, Any]       ,    _a					: Optional[Any]       ,    _a					: int=sys.maxsize				)    ->			Dict:
      UpperCAmelCase						:				Optional[Any]		  =			"bilinear"
      UpperCAmelCase						:				Dict		  =			max_size
      UpperCAmelCase						:				List[Any]		  =			short_edge_length

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

           if img.dtype == np.uinta:
                UpperCAmelCase						:				Optional[Any]		  =			Image.fromarray(_a				)
                UpperCAmelCase						:				str		  =			pil_image.resize((neww, newh)       ,    PILImageResampling.BILINEAR				)
                UpperCAmelCase						:				str		  =			np.asarray(_a				)
           else:
                UpperCAmelCase						:				List[Any]		  =			img.permute(2       ,    0       ,    1				).unsqueeze(0				)  # 3, 0, 1)  # hw(c) -> nchw
                UpperCAmelCase						:				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					: List[str]       ,    _a					: str				)    ->			List[str]:
      UpperCAmelCase						:				str		  =			ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,    cfg.INPUT.MAX_SIZE_TEST				)
      UpperCAmelCase						:				str		  =			cfg.INPUT.FORMAT
      UpperCAmelCase						:				Optional[int]		  =			cfg.SIZE_DIVISIBILITY
      UpperCAmelCase						:				List[str]		  =			cfg.PAD_VALUE
      UpperCAmelCase						:				Tuple		  =			cfg.INPUT.MAX_SIZE_TEST
      UpperCAmelCase						:				Union[str, Any]		  =			cfg.MODEL.DEVICE
      UpperCAmelCase						:				Any		  =			torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,    1       ,    1				)
      UpperCAmelCase						:				int		  =			torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,    1       ,    1				)
      UpperCAmelCase						:				Dict		  =			lambda _a				: (x - self.pixel_mean) / self.pixel_std
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: Any				)    ->			int:
      UpperCAmelCase						:				int		  =			tuple(max(_a				) for s in zip(*[img.shape for img in images]				)				)
      UpperCAmelCase						:				Tuple		  =			[im.shape[-2:] for im in images]
      UpperCAmelCase						:				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					: Optional[Any]       ,    _a					: Dict       ,    _a					: List[Any]=False				)    ->			Optional[Any]:
      with torch.no_grad():
           if not isinstance(_a       ,    _a				):
                UpperCAmelCase						:				Union[str, Any]		  =			[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
           UpperCAmelCase						:				Union[str, Any]		  =			torch.tensor([im.shape[:2] for im in images]				)
           UpperCAmelCase						:				Optional[int]		  =			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
           UpperCAmelCase						:				Optional[int]		  =			[self.normalizer(_a				) for x in images]
           # now pad them to do the following operations
           UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			self.pad(_a				)
           # Normalize

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







def   A   (		A__		,		A__ ):
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







def   A   (		A__		,		A__ ):
     assert torch.isfinite(A__ ).all(), "Box tensor contains infinite or NaN!"
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			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__ )







from typing import TYPE_CHECKING

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


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

      _snake_case   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



