






'''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     _UpperCAmelCase  :




    def __init__(     self					:    Optional[int]					,       a					:    Any					,       a					:    Dict=sys.maxsize     ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       "bilinear"
       lowercase_		:      List[str]          =       max_size
       lowercase_		:      Tuple          =       short_edge_length





    def __call__(     self					:    List[str]					,       a					:    Tuple     ):






       '''simple docstring'''






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

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

       return img_augs


class     _UpperCAmelCase  :




    def __init__(     self					:    str					,       a					:    List[str]     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       tuple(max(a     ) for s in zip(*[img.shape for img in images]     )     )
       lowercase_		:      str          =       [im.shape[-2:] for im in images]
       lowercase_		:      Optional[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					:    Tuple					,       a					:    List[str]					,       a					:    Optional[int]=False     ):






       '''simple docstring'''






       with torch.no_grad():
          if not isinstance(a					,       a     ):
             lowercase_		:      Dict          =       [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
          lowercase_		:      List[Any]          =       torch.tensor([im.shape[:2] for im in images]     )
          lowercase_		:      str          =       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
          lowercase_		:      Optional[Any]          =       [self.normalizer(a     ) for x in images]
          # now pad them to do the following operations
          lowercase_      ,						lowercase_		:      Optional[Any]          =       self.pad(a     )
          # Normalize

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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




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





def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   assert torch.isfinite(_UpperCamelCase							).all(), "Box tensor contains infinite or NaN!"
   lowercase_      ,						lowercase_		:      Union[str, Any]          =       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							)












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




