






'''simple docstring'''
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

UpperCamelCase__			=      logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return [
       int(1000 * (box[0] / width)				),
       int(1000 * (box[1] / height)				),
       int(1000 * (box[2] / width)				),
       int(1000 * (box[3] / height)				),
   ]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None				):


   """simple docstring"""




   lowercase_						:     str							     =  tesseract_config if tesseract_config is not None else ""

   # apply OCR
   lowercase_						:     Union[str, Any]							     =  to_pil_image(_UpperCamelCase				)
   lowercase_     ,		lowercase_						:     int							     =  pil_image.size
   lowercase_						:     int							     =  pytesseract.image_to_data(_UpperCamelCase    ,		lang=_UpperCamelCase    ,		output_type="dict"    ,		config=_UpperCamelCase				)
   lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Union[str, Any]							     =  data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

   # finally, normalize the bounding boxes
   lowercase_						:     str							     =  []
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)				)

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

   return words, normalized_boxes





class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: int									=     ['pixel_values']




    def __init__(      self	:							Dict	,  a	:							bool = True	,  a	:							Dict[str, int] = None	,  a	:							PILImageResampling = PILImageResampling.BILINEAR	,  a	:							bool = True	,  a	:							Optional[str] = None	,  a	:							Optional[str] = ""	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       super().__init__(**a      )
       lowercase_						:     Any							     =  size if size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_						:     Any							     =  get_size_dict(a      )

       lowercase_						:     Optional[Any]							     =  do_resize
       lowercase_						:     Union[str, Any]							     =  size
       lowercase_						:     Tuple							     =  resample
       lowercase_						:     Dict							     =  apply_ocr
       lowercase_						:     Any							     =  ocr_lang
       lowercase_						:     Any							     =  tesseract_config




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






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  get_size_dict(a      )
       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()}"""      )
       lowercase_						:     Optional[Any]							     =  (size["height"], size["width"])
       return resize(a	,  size=a	,  resample=a	,  data_format=a	,  **a      )





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






       '''simple docstring'''






       lowercase_						:     str							     =  do_resize if do_resize is not None else self.do_resize
       lowercase_						:     Optional[Any]							     =  size if size is not None else self.size
       lowercase_						:     str							     =  get_size_dict(a      )
       lowercase_						:     Tuple							     =  resample if resample is not None else self.resample
       lowercase_						:     Any							     =  apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase_						:     List[Any]							     =  ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase_						:     Optional[Any]							     =  tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase_						:     List[Any]							     =  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:
          raise ValueError("Size must be specified if do_resize is True."      )

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

       if apply_ocr:
          requires_backends(self	,  "pytesseract"      )
          lowercase_						:     Tuple							     =  []
          lowercase_						:     Optional[int]							     =  []
          for image in images:
             lowercase_     ,		lowercase_						:     Optional[int]							     =  apply_tesseract(a	,  a	,  a      )
             words_batch.append(a      )
             boxes_batch.append(a      )

       if do_resize:
          lowercase_						:     int							     =  [self.resize(image=a	,  size=a	,  resample=a      ) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       lowercase_						:     Dict							     =  [flip_channel_order(a      ) for image in images]
       lowercase_						:     Any							     =  [to_channel_dimension_format(a	,  a      ) for image in images]

       lowercase_						:     Tuple							     =  BatchFeature(data={"pixel_values": images}	,  tensor_type=a      )

       if apply_ocr:
          lowercase_						:     List[Any]							     =  words_batch
          lowercase_						:     Tuple							     =  boxes_batch
       return data












'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  2
   lowercase_						:     Tuple							     =  []
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.append(_UpperCamelCase				)
   if n > 1:
      factors.append(_UpperCamelCase				)
   return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()




