

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_lowerCAmelCase         =						logging.get_logger(__name__)






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					["""pixel_values"""]



 def __init__(   self		:   str			,				a__		:   bool = True			,				a__		:   Dict[str, int] = None			,				a__		:   float = None			,				a__		:   PILImageResampling = PILImageResampling.BILINEAR			,				a__		:   bool = True			,				a__		:   Union[int, float] = 1 / 255			,				a__		:   bool = True			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				**a__		:   int			,				):
      super().__init__(**a__      )
      __magic_name__						       =    size if size is not None else {'''shortest_edge''': 384}
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )

      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      # Default value set here for backwards compatibility where the value in config is None
      __magic_name__						       =    crop_pct if crop_pct is not None else 224 / 256
      __magic_name__						       =    resample
      __magic_name__						       =    do_rescale
      __magic_name__						       =    rescale_factor
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __magic_name__						       =    image_std if image_std is not None else IMAGENET_STANDARD_STD



 def 			snake_case__  (   self		:   List[str]			,				a__		:   np.ndarray			,				a__		:   Dict[str, int]			,				a__		:   float			,				a__		:   PILImageResampling = PILImageResampling.BICUBIC			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   List[Any]			,				):
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )
      if "shortest_edge" not in size:
           raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
      __magic_name__						       =    size['''shortest_edge''']

      if shortest_edge < 384:
           # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
           __magic_name__						       =    int(shortest_edge / crop_pct      )
           __magic_name__						       =    get_resize_output_image_size(a__			,				size=a__			,				default_to_square=a__      )
           __magic_name__						       =    resize(image=a__			,				size=a__			,				resample=a__			,				data_format=a__			,				**a__      )
           # then crop to (shortest_edge, shortest_edge)
           return center_crop(image=a__			,				size=(shortest_edge, shortest_edge)			,				data_format=a__			,				**a__      )
      else:
           # warping (no cropping) when evaluated at 384 or larger
           return resize(
               a__			,				size=(shortest_edge, shortest_edge)			,				resample=a__			,				data_format=a__			,				**a__      )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   np.ndarray			,				a__		:   Union[int, float]			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   Tuple			,				):
      return rescale(a__			,				scale=a__			,				data_format=a__			,				**a__      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   np.ndarray			,				a__		:   Union[float, List[float]]			,				a__		:   Union[float, List[float]]			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   int			,				):
      return normalize(a__			,				mean=a__			,				std=a__			,				data_format=a__			,				**a__      )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   ImageInput			,				a__		:   bool = None			,				a__		:   Dict[str, int] = None			,				a__		:   float = None			,				a__		:   PILImageResampling = None			,				a__		:   bool = None			,				a__		:   float = None			,				a__		:   bool = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[str, TensorType]] = None			,				a__		:   ChannelDimension = ChannelDimension.FIRST			,				**a__		:   Optional[int]			,				):
      __magic_name__						       =    do_resize if do_resize is not None else self.do_resize
      __magic_name__						       =    crop_pct if crop_pct is not None else self.crop_pct
      __magic_name__						       =    resample if resample is not None else self.resample
      __magic_name__						       =    do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__						       =    do_normalize if do_normalize is not None else self.do_normalize
      __magic_name__						       =    image_mean if image_mean is not None else self.image_mean
      __magic_name__						       =    image_std if image_std is not None else self.image_std

      __magic_name__						       =    size if size is not None else self.size
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )

      __magic_name__						       =    make_list_of_images(a__      )

      if not valid_images(a__      ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

      if do_resize and size is None or resample is None:
           raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

      if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
           raise ValueError('''crop_pct must be specified if size < 384.'''      )

      if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

      # All transformations expect numpy arrays.
      __magic_name__						       =    [to_numpy_array(a__      ) for image in images]

      if do_resize:
           __magic_name__						       =    [self.resize(image=a__			,				size=a__			,				crop_pct=a__			,				resample=a__      ) for image in images]

      if do_rescale:
           __magic_name__						       =    [self.rescale(image=a__			,				scale=a__      ) for image in images]

      if do_normalize:
           __magic_name__						       =    [self.normalize(image=a__			,				mean=a__			,				std=a__      ) for image in images]

      __magic_name__						       =    [to_channel_dimension_format(a__			,				a__      ) for image in images]

      __magic_name__						       =    {'''pixel_values''': images}
      return BatchFeature(data=a__			,				tensor_type=a__      )









'''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   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



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

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

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



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



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

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




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






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




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    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			)






