




'''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

a_      :		str							      =       logging.get_logger(__name__)





def      a_							(      __snake_case							:			Any   ,       __snake_case							:			Any   ,       __snake_case							:			int   )							->						Any:



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





def      a_							(      __snake_case							:			int   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Any = None   )							->						int:



       """simple docstring"""
       lowerCamelCase_							=tesseract_config if tesseract_config is not None else """"""

       # apply OCR
       lowerCamelCase_							=to_pil_image(_lowerCAmelCase   )
       lowerCamelCase_							=pil_image.size
       lowerCamelCase_							=pytesseract.image_to_data(_lowerCAmelCase   ,       lang=_lowerCAmelCase   ,       output_type='''dict'''   ,       config=_lowerCAmelCase   )
       lowerCamelCase_							=data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       lowerCamelCase_							=[idx for idx, word in enumerate(_lowerCAmelCase   ) if not word.strip()]
       lowerCamelCase_							=[word for idx, word in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
       lowerCamelCase_							=[coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
       lowerCamelCase_							=[coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
       lowerCamelCase_							=[coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
       lowerCamelCase_							=[coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]

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

       # finally, normalize the bounding boxes
       lowerCamelCase_							=[]
       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       ( SCREAMING_SNAKE_CASE__				):
       lowercase	:    Any      						=["pixel_values"]






       def __init__(							self,		lowerCAmelCase = True,		lowerCAmelCase = None,		lowerCAmelCase = PILImageResampling.BILINEAR,		lowerCAmelCase = True,		lowerCAmelCase = None,		lowerCAmelCase = "",		**lowerCAmelCase,		):



              """simple docstring"""







              super().__init__(**lowerCamelCase__  )
              lowerCamelCase_							=size if size is not None else {"""height""": 224, """width""": 224}
              lowerCamelCase_							=get_size_dict(lowerCamelCase__  )

              lowerCamelCase_							=do_resize
              lowerCamelCase_							=size
              lowerCamelCase_							=resample
              lowerCamelCase_							=apply_ocr
              lowerCamelCase_							=ocr_lang
              lowerCamelCase_							=tesseract_config






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = PILImageResampling.BILINEAR,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              lowerCamelCase_							=get_size_dict(lowerCamelCase__  )
              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()}'''  )
              lowerCamelCase_							=(size["""height"""], size["""width"""])
              return resize(lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = ChannelDimension.FIRST,		**lowerCAmelCase,		):



              """simple docstring"""







              lowerCamelCase_							=do_resize if do_resize is not None else self.do_resize
              lowerCamelCase_							=size if size is not None else self.size
              lowerCamelCase_							=get_size_dict(lowerCamelCase__  )
              lowerCamelCase_							=resample if resample is not None else self.resample
              lowerCamelCase_							=apply_ocr if apply_ocr is not None else self.apply_ocr
              lowerCamelCase_							=ocr_lang if ocr_lang is not None else self.ocr_lang
              lowerCamelCase_							=tesseract_config if tesseract_config is not None else self.tesseract_config

              lowerCamelCase_							=make_list_of_images(lowerCamelCase__  )

              if not valid_images(lowerCamelCase__  ):
                     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.
              lowerCamelCase_							=[to_numpy_array(lowerCamelCase__  ) for image in images]

              if apply_ocr:
                     requires_backends(self,		'''pytesseract'''  )
                     lowerCamelCase_							=[]
                     lowerCamelCase_							=[]
                     for image in images:
                            lowerCamelCase_							=apply_tesseract(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__  )
                            words_batch.append(lowerCamelCase__  )
                            boxes_batch.append(lowerCamelCase__  )

              if do_resize:
                     lowerCamelCase_							=[self.resize(image=lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__  ) for image in images]

              # flip color channels from RGB to BGR (as Detectron2 requires this)
              lowerCamelCase_							=[flip_channel_order(lowerCamelCase__  ) for image in images]
              lowerCamelCase_							=[to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__  ) for image in images]

              lowerCamelCase_							=BatchFeature(data={'''pixel_values''': images},		tensor_type=lowerCamelCase__  )

              if apply_ocr:
                     lowerCamelCase_							=words_batch
                     lowerCamelCase_							=boxes_batch
              return data









def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  list:



    """simple docstring"""






    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(_lowerCAmelCase		)		)
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
     __import__("""doctest""").testmod()


