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

a_		       =   logging.get_logger(__name__)




def    __lowerCAmelCase   (    A_ :			Optional[Any]    ,						A_ :			List[Any]    ,						A_ :			Union[str, Any]       )     ->	Any:
  return [
      int(10_00 * (box[0] / width)       ),
      int(10_00 * (box[1] / height)       ),
      int(10_00 * (box[2] / width)       ),
      int(10_00 * (box[3] / height)       ),
  ]




def    __lowerCAmelCase   (    A_ :			np.ndarray    ,						A_ :			Optional[str]    ,						A_ :			Optional[str]       )     ->	Optional[int]:
  __UpperCAmelCase         =							to_pil_image(__lowercase       )
  __UpperCAmelCase         =							pil_image.size
  __UpperCAmelCase         =							pytesseract.image_to_data(__lowercase    ,						lang=__lowercase    ,						output_type="dict"    ,						config=__lowercase       )
  __UpperCAmelCase         =							data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

  return words, normalized_boxes





class       UpperCAmelCase__     (		__snake_case       ):



  """simple docstring"""






  lowerCAmelCase__		:						str     		=			["pixel_values"]






  def __init__(					self:      Tuple					,					__lowerCAmelCase:      Optional[int] = True					,					__lowerCAmelCase:      str = None					,					__lowerCAmelCase:      int = PILImageResampling.BILINEAR					,					__lowerCAmelCase:      Tuple = True					,					__lowerCAmelCase:      str = 1 / 255					,					__lowerCAmelCase:      Tuple = True					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      str = True					,					__lowerCAmelCase:      Tuple = None					,					__lowerCAmelCase:      int = ""					,					**__lowerCAmelCase:      List[str]					,					)			->			int:




    '''simple docstring'''




    super().__init__(**A__ )
    __UpperCAmelCase         =							size if size is not None else {'''height''': 224, '''width''': 224}
    __UpperCAmelCase         =							get_size_dict(A__ )

    __UpperCAmelCase         =							do_resize
    __UpperCAmelCase         =							size
    __UpperCAmelCase         =							resample
    __UpperCAmelCase         =							do_rescale
    __UpperCAmelCase         =							rescale_value
    __UpperCAmelCase         =							do_normalize
    __UpperCAmelCase         =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __UpperCAmelCase         =							image_std if image_std is not None else IMAGENET_STANDARD_STD
    __UpperCAmelCase         =							apply_ocr
    __UpperCAmelCase         =							ocr_lang
    __UpperCAmelCase         =							tesseract_config






  def 						_UpperCAmelCase					(					self:      str					,					__lowerCAmelCase:      Optional[Any]					,					__lowerCAmelCase:      str					,					__lowerCAmelCase:      Tuple = PILImageResampling.BILINEAR					,					__lowerCAmelCase:      Any = None					,					**__lowerCAmelCase:      Any					,					)			->			List[str]:




    '''simple docstring'''




    __UpperCAmelCase         =							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()}''' )
    __UpperCAmelCase         =							(size['''height'''], size['''width'''])
    return resize(A__					,					size=A__					,					resample=A__					,					data_format=A__					,					**A__ )






  def 						_UpperCAmelCase					(					self:      Optional[int]					,					__lowerCAmelCase:      Optional[Any]					,					__lowerCAmelCase:      List[str]					,					__lowerCAmelCase:      Optional[int] = None					,					**__lowerCAmelCase:      Tuple					,					)			->			Union[str, Any]:




    '''simple docstring'''




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






  def 						_UpperCAmelCase					(					self:      Any					,					__lowerCAmelCase:      str					,					__lowerCAmelCase:      str					,					__lowerCAmelCase:      Any					,					__lowerCAmelCase:      int = None					,					**__lowerCAmelCase:      Union[str, Any]					,					)			->			str:




    '''simple docstring'''




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






  def 						_UpperCAmelCase					(					self:      str					,					__lowerCAmelCase:      str					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      Optional[Any] = None					,					__lowerCAmelCase:      Tuple=None					,					__lowerCAmelCase:      str = None					,					__lowerCAmelCase:      Optional[int] = None					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      Any = None					,					__lowerCAmelCase:      Tuple = None					,					__lowerCAmelCase:      Union[str, Any] = None					,					__lowerCAmelCase:      str = None					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      List[str] = None					,					__lowerCAmelCase:      Dict = ChannelDimension.FIRST					,					**__lowerCAmelCase:      Optional[Any]					,					)			->			Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase         =							do_resize if do_resize is not None else self.do_resize
    __UpperCAmelCase         =							size if size is not None else self.size
    __UpperCAmelCase         =							get_size_dict(A__ )
    __UpperCAmelCase         =							resample if resample is not None else self.resample
    __UpperCAmelCase         =							do_rescale if do_rescale is not None else self.do_rescale
    __UpperCAmelCase         =							rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCAmelCase         =							do_normalize if do_normalize is not None else self.do_normalize
    __UpperCAmelCase         =							image_mean if image_mean is not None else self.image_mean
    __UpperCAmelCase         =							image_std if image_std is not None else self.image_std
    __UpperCAmelCase         =							apply_ocr if apply_ocr is not None else self.apply_ocr
    __UpperCAmelCase         =							ocr_lang if ocr_lang is not None else self.ocr_lang
    __UpperCAmelCase         =							tesseract_config if tesseract_config is not None else self.tesseract_config

    __UpperCAmelCase         =							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.
    __UpperCAmelCase         =							[to_numpy_array(A__ ) for image in images]

    # Tesseract OCR to get words + normalized bounding boxes
    if apply_ocr:
      requires_backends(self					,					"pytesseract" )
      __UpperCAmelCase         =							[]
      __UpperCAmelCase         =							[]
      for image in images:
        __UpperCAmelCase         =							apply_tesseract(A__					,					A__					,					A__ )
        words_batch.append(A__ )
        boxes_batch.append(A__ )

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

    if do_rescale:
      __UpperCAmelCase         =							[self.rescale(image=A__					,					scale=A__ ) for image in images]

    if do_normalize:
      __UpperCAmelCase         =							[self.normalize(image=A__					,					mean=A__					,					std=A__ ) for image in images]

    __UpperCAmelCase         =							[to_channel_dimension_format(A__					,					A__ ) for image in images]

    __UpperCAmelCase         =							BatchFeature(data={"pixel_values": images}					,					tensor_type=A__ )

    if apply_ocr:
      __UpperCAmelCase         =							words_batch
      __UpperCAmelCase         =							boxes_batch
    return data











import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)



