


'''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_		:   str							   =		logging.get_logger(__name__)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__ = None     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      tesseract_config if tesseract_config is not None else """"""

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

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

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

				# finally, normalize the bounding boxes
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      []
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )     )

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

				return words, normalized_boxes







class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      List[str]      	=  ["""pixel_values"""]






							def __init__(    self    ,					__snake_case = True    ,					__snake_case = None    ,					__snake_case = PILImageResampling.BILINEAR    ,					__snake_case = True    ,					__snake_case = None    ,					__snake_case = ""    ,					**__snake_case    ,					):
											super().__init__(**__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      size if size is not None else {"""height""": 224, """width""": 224}
											_SCREAMING_SNAKE_CASE :       int     		=      get_size_dict(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Dict     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      resample
											_SCREAMING_SNAKE_CASE :       int     		=      apply_ocr
											_SCREAMING_SNAKE_CASE :       int     		=      ocr_lang
											_SCREAMING_SNAKE_CASE :       Any     		=      tesseract_config






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case = PILImageResampling.BILINEAR    ,					__snake_case = None    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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()}"""  )
											_SCREAMING_SNAKE_CASE :       int     		=      (size["""height"""], size["""width"""])
											return resize(__snake_case    ,					size=__snake_case    ,					resample=__snake_case    ,					data_format=__snake_case    ,					**__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = ChannelDimension.FIRST    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE :       int     		=      size if size is not None else self.size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      get_size_dict(__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      apply_ocr if apply_ocr is not None else self.apply_ocr
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ocr_lang if ocr_lang is not None else self.ocr_lang
											_SCREAMING_SNAKE_CASE :       List[str]     		=      tesseract_config if tesseract_config is not None else self.tesseract_config

											_SCREAMING_SNAKE_CASE :       Dict     		=      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.
											_SCREAMING_SNAKE_CASE :       int     		=      [to_numpy_array(__snake_case  ) for image in images]

											if apply_ocr:
															requires_backends(self    ,					"""pytesseract"""  )
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      []
															_SCREAMING_SNAKE_CASE :       int     		=      []
															for image in images:
																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      apply_tesseract(__snake_case    ,					__snake_case    ,					__snake_case  )
																			words_batch.append(__snake_case  )
																			boxes_batch.append(__snake_case  )

											if do_resize:
															_SCREAMING_SNAKE_CASE :       Dict     		=      [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)
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      [flip_channel_order(__snake_case  ) for image in images]
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      [to_channel_dimension_format(__snake_case    ,					__snake_case  ) for image in images]

											_SCREAMING_SNAKE_CASE :       Any     		=      BatchFeature(data={"""pixel_values""": images}    ,					tensor_type=__snake_case  )

											if apply_ocr:
															_SCREAMING_SNAKE_CASE :       Any     		=      words_batch
															_SCREAMING_SNAKE_CASE :       str     		=      boxes_batch
											return data








'''simple docstring'''


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa





				class     lowercase__  (						ctypes.Structure					):





											'''simple docstring'''


											A_							:      Optional[Any]      	=  [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]







def 			snake_case_   (  ):







				"""simple docstring"""





				if os.name == "nt":
								_SCREAMING_SNAKE_CASE :       Tuple     		=      CursorInfo()
								_SCREAMING_SNAKE_CASE :       Tuple     		=      ctypes.windll.kernelaa.GetStdHandle(-11     )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
				elif os.name == "posix":
								sys.stdout.write("""\033[?25l"""     )
								sys.stdout.flush()







def 			snake_case_   (  ):







				"""simple docstring"""





				if os.name == "nt":
								_SCREAMING_SNAKE_CASE :       int     		=      CursorInfo()
								_SCREAMING_SNAKE_CASE :       List[str]     		=      ctypes.windll.kernelaa.GetStdHandle(-11     )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
								_SCREAMING_SNAKE_CASE :       Tuple     		=      True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
				elif os.name == "posix":
								sys.stdout.write("""\033[?25h"""     )
								sys.stdout.flush()







@contextmanager
def 			snake_case_   (  ):







				"""simple docstring"""





				try:
								hide_cursor()
								yield
				finally:
								show_cursor()




