


'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    ["image_processor", "tokenizer"]
    a													=    "AutoImageProcessor"
    a													=    "AutoTokenizer"


    def __init__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							int=None      ,							**SCREAMING_SNAKE_CASE			:							str):
           _A     :	Optional[Any]      =					None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                      ' instead.'      ,							SCREAMING_SNAKE_CASE      ,							)
                  _A     :	Tuple      =					kwargs.pop('feature_extractor')

           _A     :	int      =					image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError('You have to specify an image_processor.')
           if tokenizer is None:
                  raise ValueError('You have to specify a tokenizer.')

           super().__init__(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	List[str]      =					self.image_processor


    def __call__(  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=None      ,							SCREAMING_SNAKE_CASE			:							str=None      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							**SCREAMING_SNAKE_CASE			:							int):

           if text is None and images is None:
                  raise ValueError('You have to specify either text or images. Both cannot be none.')

           if text is not None:
                  _A     :	str      =					self.tokenizer(SCREAMING_SNAKE_CASE      ,							return_tensors=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)

           if images is not None:
                  _A     :	Tuple      =					self.image_processor(SCREAMING_SNAKE_CASE      ,							return_tensors=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)

           if text is not None and images is not None:
                  _A     :	Dict      =					image_features.pixel_values
                  return encoding
           elif text is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE)      ,							tensor_type=SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Union[str, Any]      ,							*SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							Tuple):
           return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)


    def    A    (  self			:							int      ,							*SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							**SCREAMING_SNAKE_CASE			:							Any):
           return self.tokenizer.decode(*SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)


    @property
    def    A    (  self			:							Union[str, Any]):
           _A     :	Any      =					self.tokenizer.model_input_names
           _A     :	str      =					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


    @property
    def    A    (  self			:							Tuple):
           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,							SCREAMING_SNAKE_CASE      ,							)
           return self.image_processor_class


    @property
    def    A    (  self			:							Tuple):
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,							SCREAMING_SNAKE_CASE      ,							)
           return self.image_processor










'''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   )






