





'''simple docstring'''




from typing import Dict, Iterable, 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, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					int					,				__snake_case    :					Optional[int]     )	->	Optional[int]:
     return [
         int(10_00 * (box[0] / width)     ),
         int(10_00 * (box[1] / height)     ),
         int(10_00 * (box[2] / width)     ),
         int(10_00 * (box[3] / height)     ),
     ]






def 			_lowerCAmelCase     (    __snake_case    :					np.ndarray					,				__snake_case    :					Optional[str]					,				__snake_case    :					Optional[str]     )	->	int:
     __A		:					Any						=			to_pil_image(__snake_case     )
     __A					,__A		:					str						=			pil_image.size
     __A		:					Any						=			pytesseract.image_to_data(__snake_case					,				lang=__snake_case					,				output_type='dict'					,				config=__snake_case     )
     __A					,__A					,__A					,__A					,__A		:					Any						=			data['text'], data['left'], data['top'], data['width'], data['height']

     # filter empty words and corresponding coordinates
     __A		:					Dict						=			[idx for idx, word in enumerate(__snake_case     ) if not word.strip()]
     __A		:					Dict						=			[word for idx, word in enumerate(__snake_case     ) if idx not in irrelevant_indices]
     __A		:					int						=			[coord for idx, coord in enumerate(__snake_case     ) if idx not in irrelevant_indices]
     __A		:					Optional[Any]						=			[coord for idx, coord in enumerate(__snake_case     ) if idx not in irrelevant_indices]
     __A		:					List[Any]						=			[coord for idx, coord in enumerate(__snake_case     ) if idx not in irrelevant_indices]
     __A		:					int						=			[coord for idx, coord in enumerate(__snake_case     ) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     __A		:					str						=			[]
     for x, y, w, h in zip(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     ):
          __A		:					Dict						=			[x, y, x + w, y + h]
          actual_boxes.append(__snake_case     )

     # finally, normalize the bounding boxes
     __A		:					Tuple						=			[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(__snake_case					,				__snake_case					,				__snake_case     )     )

     assert len(__snake_case     ) == len(__snake_case     ), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			['''pixel_values''']


     def __init__(		self		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = PILImageResampling.BILINEAR		,      _UpperCAmelCase = True		,      _UpperCAmelCase = 1 / 255		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = ""		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)
          __A		:					Union[str, Any]						=			size if size is not None else {'height': 224, 'width': 224}
          __A		:					Optional[Any]						=			get_size_dict(_UpperCAmelCase)

          __A		:					str						=			do_resize
          __A		:					List[str]						=			size
          __A		:					Any						=			resample
          __A		:					List[str]						=			do_rescale
          __A		:					Tuple						=			rescale_value
          __A		:					Tuple						=			do_normalize
          __A		:					int						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __A		:					List[str]						=			image_std if image_std is not None else IMAGENET_STANDARD_STD
          __A		:					Dict						=			apply_ocr
          __A		:					Optional[Any]						=			ocr_lang
          __A		:					Optional[int]						=			tesseract_config


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = PILImageResampling.BILINEAR		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			get_size_dict(_UpperCAmelCase)
          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()}')
          __A		:					List[str]						=			(size['height'], size['width'])
          return resize(_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase=None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = ChannelDimension.FIRST		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			do_resize if do_resize is not None else self.do_resize
          __A		:					Optional[Any]						=			size if size is not None else self.size
          __A		:					Optional[Any]						=			get_size_dict(_UpperCAmelCase)
          __A		:					Tuple						=			resample if resample is not None else self.resample
          __A		:					Tuple						=			do_rescale if do_rescale is not None else self.do_rescale
          __A		:					str						=			rescale_factor if rescale_factor is not None else self.rescale_factor
          __A		:					List[Any]						=			do_normalize if do_normalize is not None else self.do_normalize
          __A		:					str						=			image_mean if image_mean is not None else self.image_mean
          __A		:					Optional[Any]						=			image_std if image_std is not None else self.image_std
          __A		:					Union[str, Any]						=			apply_ocr if apply_ocr is not None else self.apply_ocr
          __A		:					Optional[int]						=			ocr_lang if ocr_lang is not None else self.ocr_lang
          __A		:					Dict						=			tesseract_config if tesseract_config is not None else self.tesseract_config

          __A		:					Optional[int]						=			make_list_of_images(_UpperCAmelCase)

          if not valid_images(_UpperCAmelCase):
               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.')

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('If do_normalize is True, image_mean and image_std must be specified.')

          # All transformations expect numpy arrays.
          __A		:					Any						=			[to_numpy_array(_UpperCAmelCase) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self		,      'pytesseract')
               __A		:					Optional[Any]						=			[]
               __A		:					Optional[Any]						=			[]
               for image in images:
                    __A					,__A		:					int						=			apply_tesseract(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
                    words_batch.append(_UpperCAmelCase)
                    boxes_batch.append(_UpperCAmelCase)

          if do_resize:
               __A		:					Optional[Any]						=			[self.resize(image=_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase) for image in images]

          if do_rescale:
               __A		:					Optional[int]						=			[self.rescale(image=_UpperCAmelCase		,      scale=_UpperCAmelCase) for image in images]

          if do_normalize:
               __A		:					Optional[int]						=			[self.normalize(image=_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase) for image in images]

          __A		:					List[str]						=			[to_channel_dimension_format(_UpperCAmelCase		,      _UpperCAmelCase) for image in images]

          __A		:					str						=			BatchFeature(data={'pixel_values': images}		,      tensor_type=_UpperCAmelCase)

          if apply_ocr:
               __A		:					List[Any]						=			words_batch
               __A		:					Optional[Any]						=			boxes_batch
          return data






'''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		,      _UpperCAmelCase		,      _UpperCAmelCase=sys.maxsize):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			'bilinear'
          __A		:					int						=			max_size
          __A		:					Optional[Any]						=			short_edge_length


     def __call__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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

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

          return img_augs




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






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

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


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






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

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






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					str     )	->	Dict:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					Tuple[int, int]     )	->	int:
     assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
     __A					,__A		:					int						=			box_size
     tensor[:, 0].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 1].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 2].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 3].clamp_(min=0					,				max=__snake_case     )