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 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, logging


lowerCAmelCase__							= logging.get_logger(__name__)
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					['pixel_values']




 def __init__(			self					:  str			,    lowercase__					:  bool = True			,    lowercase__					:  Optional[Dict[str, int]] = None			,    lowercase__					:  PILImageResampling = PILImageResampling.BILINEAR			,    lowercase__					:  bool = True			,    lowercase__					:  Union[int, float] = 1 / 255			,    lowercase__					:  bool = True			,    lowercase__					:  Optional[Union[float, List[float]]] = None			,    lowercase__					:  Optional[Union[float, List[float]]] = None			,    **lowercase__					:  int			,    ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__     	=					size if size is not None else {'height': 224, 'width': 224}
        lowerCAmelCase__     	=					get_size_dict(lowercase__)
        lowerCAmelCase__     	=					do_resize
        lowerCAmelCase__     	=					do_rescale
        lowerCAmelCase__     	=					do_normalize
        lowerCAmelCase__     	=					size
        lowerCAmelCase__     	=					resample
        lowerCAmelCase__     	=					rescale_factor
        lowerCAmelCase__     	=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__     	=					image_std if image_std is not None else IMAGENET_STANDARD_STD




 def 		__snake_case						(			self					:  str			,    lowercase__					:  np.ndarray			,    lowercase__					:  Dict[str, int]			,    lowercase__					:  PILImageResampling = PILImageResampling.BILINEAR			,    lowercase__					:  Optional[Union[str, ChannelDimension]] = None			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        lowerCAmelCase__     	=					get_size_dict(lowercase__)
        if "height" not in size or "width" not in size:
               raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
        return resize(
            lowercase__			,    size=(size['height'], size['width'])			,    resample=lowercase__			,    data_format=lowercase__			,    **lowercase__)




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  np.ndarray			,    lowercase__					:  float			,    lowercase__					:  Optional[Union[str, ChannelDimension]] = None			,    **lowercase__					:  Optional[int]):







        '''simple docstring'''



        return rescale(lowercase__			,    scale=lowercase__			,    data_format=lowercase__			,    **lowercase__)




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  np.ndarray			,    lowercase__					:  Union[float, List[float]]			,    lowercase__					:  Union[float, List[float]]			,    lowercase__					:  Optional[Union[str, ChannelDimension]] = None			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        return normalize(lowercase__			,    mean=lowercase__			,    std=lowercase__			,    data_format=lowercase__			,    **lowercase__)




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  ImageInput			,    lowercase__					:  Optional[bool] = None			,    lowercase__					:  Dict[str, int] = None			,    lowercase__					:  PILImageResampling = None			,    lowercase__					:  Optional[bool] = None			,    lowercase__					:  Optional[float] = None			,    lowercase__					:  Optional[bool] = None			,    lowercase__					:  Optional[Union[float, List[float]]] = None			,    lowercase__					:  Optional[Union[float, List[float]]] = None			,    lowercase__					:  Optional[Union[str, TensorType]] = None			,    lowercase__					:  Union[str, ChannelDimension] = ChannelDimension.FIRST			,    **lowercase__					:  List[str]			,    ):







        '''simple docstring'''



        lowerCAmelCase__     	=					do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__     	=					do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__     	=					do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__     	=					resample if resample is not None else self.resample
        lowerCAmelCase__     	=					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__     	=					image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__     	=					image_std if image_std is not None else self.image_std

        lowerCAmelCase__     	=					size if size is not None else self.size
        lowerCAmelCase__     	=					get_size_dict(lowercase__)

        lowerCAmelCase__     	=					make_list_of_images(lowercase__)

        if not valid_images(lowercase__):
               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:
               raise ValueError('Size must be specified if do_resize is True.')

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

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

        if do_resize:
               lowerCAmelCase__     	=					[self.resize(image=lowercase__			,    size=lowercase__			,    resample=lowercase__) for image in images]

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

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

        lowerCAmelCase__     	=					[to_channel_dimension_format(lowercase__			,    lowercase__) for image in images]

        lowerCAmelCase__     	=					{'pixel_values': images}
        return BatchFeature(data=lowercase__			,    tensor_type=lowercase__)




'''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  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							str      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=sys.maxsize):
           _A     :	Tuple      =					'bilinear'
           _A     :	List[Any]      =					max_size
           _A     :	Optional[int]      =					short_edge_length


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

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

           return img_augs




class  __lowerCamelCase :
    """simple docstring"""


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


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

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


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

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






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			Union[str, Any]   ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def       lowerCAmelCase__       (				lowerCamelCase     :			Any  ,lowerCamelCase     :			Tuple[int, int]   ):
       assert torch.isfinite(lowerCamelCase   ).all(), "Box tensor contains infinite or NaN!"
       _A  , _A     :	Union[str, Any]      =					box_size
       tensor[:, 0].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 1].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 2].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 3].clamp_(min=0  ,max=lowerCamelCase   )






