






"""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

UpperCamelCase_	:				List[Any]										= logging.get_logger(__name__)



def     __lowercase					(    a :				Any       ,		a :				Dict       ,		a :				Union[str, Any] )					->					Optional[Any]:
      return [
          int(1_000 * (box[0] / width) ),
          int(1_000 * (box[1] / height) ),
          int(1_000 * (box[2] / width) ),
          int(1_000 * (box[3] / height) ),
      ]



def     __lowercase					(    a :				np.ndarray       ,		a :				Optional[str]       ,		a :				Optional[str] )					->					int:
      __snake_case      :    List[Any]  			=to_pil_image(a )
      __snake_case			,				__snake_case      :    int  			=pil_image.size
      __snake_case      :    Optional[int]  			=pytesseract.image_to_data(a       ,		lang=a       ,		output_type='''dict'''       ,		config=a )
      __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      :    List[Any]  			=[idx for idx, word in enumerate(a ) if not word.strip()]
      __snake_case      :    int  			=[word for idx, word in enumerate(a ) if idx not in irrelevant_indices]
      __snake_case      :    Union[str, Any]  			=[coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
      __snake_case      :    Optional[Any]  			=[coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
      __snake_case      :    Optional[int]  			=[coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
      __snake_case      :    int  			=[coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]

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

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

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

      return words, normalized_boxes


class      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   ['''pixel_values''']


      def __init__(    self				:	Optional[int]							,   a				:	bool = True							,   a				:	Dict[str, int] = None							,   a				:	PILImageResampling = PILImageResampling.BILINEAR							,   a				:	bool = True							,   a				:	float = 1 / 2_5_5							,   a				:	bool = True							,   a				:	Union[float, Iterable[float]] = None							,   a				:	Union[float, Iterable[float]] = None							,   a				:	bool = True							,   a				:	Optional[str] = None							,   a				:	Optional[str] = ""							,   **a				:	Any							,   ):





            """simple docstring"""







            super().__init__(**a						)
            __snake_case      :    Optional[Any]  			=size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
            __snake_case      :    Any  			=get_size_dict(a						)

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


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





            """simple docstring"""







            __snake_case      :    Dict  			=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()}'''						)
            __snake_case      :    List[str]  			=(size['''height'''], size['''width'''])
            return resize(a							,   size=a							,   resample=a							,   data_format=a							,   **a						)


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	np.ndarray							,   a				:	Union[int, float]							,   a				:	Optional[Union[str, ChannelDimension]] = None							,   **a				:	Optional[int]							,   ):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Any							,   a				:	np.ndarray							,   a				:	Union[float, Iterable[float]]							,   a				:	Union[float, Iterable[float]]							,   a				:	Optional[Union[str, ChannelDimension]] = None							,   **a				:	Any							,   ):





            """simple docstring"""







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







      def _UpperCamelCase						(    self				:	Tuple							,   a				:	ImageInput							,   a				:	bool = None							,   a				:	Dict[str, int] = None							,   a				:	Optional[Any]=None							,   a				:	bool = None							,   a				:	float = None							,   a				:	bool = None							,   a				:	Union[float, Iterable[float]] = None							,   a				:	Union[float, Iterable[float]] = 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"""







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

            __snake_case      :    Optional[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.'''						)

            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.
            __snake_case      :    List[Any]  			=[to_numpy_array(a						) for image in images]

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

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

            if do_rescale:
                  __snake_case      :    List[Any]  			=[self.rescale(image=a							,   scale=a						) for image in images]

            if do_normalize:
                  __snake_case      :    Tuple  			=[self.normalize(image=a							,   mean=a							,   std=a						) for image in images]

            __snake_case      :    str  			=[to_channel_dimension_format(a							,   a						) for image in images]

            __snake_case      :    Any  			=BatchFeature(data={'''pixel_values''': images}							,   tensor_type=a						)

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










"""simple docstring"""





from __future__ import annotations



def     __lowercase					(    a :				int       ,		a :				int )					->					list[str]:
      if partitions <= 0:
            raise ValueError('''partitions must be a positive number!''' )
      if partitions > number_of_bytes:
            raise ValueError('''partitions can not > number_of_bytes!''' )
      __snake_case      :    List[str]  			=number_of_bytes // partitions
      __snake_case      :    str  			=[]
      for i in range(a ):
            __snake_case      :    Optional[Any]  			=i * bytes_per_partition + 1
            __snake_case      :    Any  			=(
                number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
            )
            allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
      return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()


