
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

__lowerCamelCase				=			logging.get_logger(__name__)


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					int       ):
       return [
           int(1000 * (box[0] / width)       ),
           int(1000 * (box[1] / height)       ),
           int(1000 * (box[2] / width)       ),
           int(1000 * (box[3] / height)       ),
       ]


def      UpperCamelCase   (       __lowerCamelCase	:					np.ndarray							,   __lowerCamelCase	:					Optional[str]							,   __lowerCamelCase	:					Optional[str] = None       ):
       snake_case			:  Tuple     							= tesseract_config if tesseract_config is not None else ""

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

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

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

       # finally, normalize the bounding boxes
       snake_case			:  Optional[Any]     							= []
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )       )

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

       return words, normalized_boxes






class     UpperCAmelCase						(		A_					):
   A__   :						Union[str, Any]			=	["pixel_values"]



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


          '''simple docstring'''


          super().__init__(**snake_case__					)
          snake_case			:  Tuple     							= size if size is not None else {"height": 2_24, "width": 2_24}
          snake_case			:  str     							= get_size_dict(snake_case__					)

          snake_case			:  List[Any]     							= do_resize
          snake_case			:  Dict     							= size
          snake_case			:  Dict     							= resample
          snake_case			:  List[Any]     							= apply_ocr
          snake_case			:  str     							= ocr_lang
          snake_case			:  List[str]     							= tesseract_config



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   np.ndarray       ,		snake_case__						:   Dict[str, int]       ,		snake_case__						:   PILImageResampling = PILImageResampling.BILINEAR       ,		snake_case__						:   Optional[Union[str, ChannelDimension]] = None       ,		**snake_case__						:   Optional[Any]       ,		)						->      np.ndarray:


          '''simple docstring'''


          snake_case			:  str     							= get_size_dict(snake_case__					)
          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()}"""					)
          snake_case			:  Optional[int]     							= (size["height"], size["width"])
          return resize(snake_case__       ,		size=snake_case__       ,		resample=snake_case__       ,		data_format=snake_case__       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   ImageInput       ,		snake_case__						:   bool = None       ,		snake_case__						:   Dict[str, int] = None       ,		snake_case__						:   PILImageResampling = None       ,		snake_case__						:   bool = None       ,		snake_case__						:   Optional[str] = None       ,		snake_case__						:   Optional[str] = None       ,		snake_case__						:   Optional[Union[str, TensorType]] = None       ,		snake_case__						:   ChannelDimension = ChannelDimension.FIRST       ,		**snake_case__						:   Union[str, Any]       ,		)						->      PIL.Image.Image:


          '''simple docstring'''


          snake_case			:  Any     							= do_resize if do_resize is not None else self.do_resize
          snake_case			:  List[Any]     							= size if size is not None else self.size
          snake_case			:  Any     							= get_size_dict(snake_case__					)
          snake_case			:  Union[str, Any]     							= resample if resample is not None else self.resample
          snake_case			:  List[str]     							= apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:  Union[str, Any]     							= ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:  int     							= tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:  int     							= make_list_of_images(snake_case__					)

          if not valid_images(snake_case__					):
                 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.
          snake_case			:  int     							= [to_numpy_array(snake_case__					) for image in images]

          if apply_ocr:
                 requires_backends(self       ,		"pytesseract"					)
                 snake_case			:  str     							= []
                 snake_case			:  Dict     							= []
                 for image in images:
                        snake_case							,   snake_case			:  Any     							= apply_tesseract(snake_case__       ,		snake_case__       ,		snake_case__					)
                        words_batch.append(snake_case__					)
                        boxes_batch.append(snake_case__					)

          if do_resize:
                 snake_case			:  Optional[int]     							= [self.resize(image=snake_case__       ,		size=snake_case__       ,		resample=snake_case__					) for image in images]

          # flip color channels from RGB to BGR (as Detectron2 requires this)
          snake_case			:  Any     							= [flip_channel_order(snake_case__					) for image in images]
          snake_case			:  List[str]     							= [to_channel_dimension_format(snake_case__       ,		snake_case__					) for image in images]

          snake_case			:  Optional[Any]     							= BatchFeature(data={"pixel_values": images}       ,		tensor_type=snake_case__					)

          if apply_ocr:
                 snake_case			:  List[Any]     							= words_batch
                 snake_case			:  Union[str, Any]     							= boxes_batch
          return data







def      UpperCamelCase   (       __lowerCamelCase	:					int = 1							,   __lowerCamelCase	:					int = 1000       ):
       snake_case			:  int     							= 1
       snake_case			:  int     							= 0

       for divide_by_number in range(__lowerCamelCase							,   digit + 1       ):
              snake_case			:  list[int]     							= []
              snake_case			:  Optional[int]     							= numerator
              for _ in range(1							,   digit + 1       ):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(__lowerCamelCase       ):
                                   snake_case			:  List[Any]     							= len(__lowerCamelCase       )
                                   snake_case			:  List[str]     							= divide_by_number
                     else:
                            has_been_divided.append(__lowerCamelCase       )
                            snake_case			:  Any     							= now_divide * 10 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()





