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_   : str						,	A_   : Any						,	A_   : str					)    ->  Union[str, 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]					)    ->  List[str]:
  __UpperCAmelCase						    =       to_pil_image(A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       pil_image.size
  __UpperCAmelCase						    =       pytesseract.image_to_data(A_						,	lang=A_						,	output_type="dict"						,	config=A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

  # finally, normalize the bounding boxes
  __UpperCAmelCase						    =       []
  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 			UpperCAmelCase__    (		snake_case					):



     """simple docstring"""






     lowerCAmelCase__		:	Optional[Any]    		= ['pixel_values']






     def __init__(				self:			Any				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Dict[str, int] = None				,				__lowerCAmelCase:			PILImageResampling = PILImageResampling.BILINEAR				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			float = 1 / 255				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Union[float, Iterable[float]] = None				,				__lowerCAmelCase:			Union[float, Iterable[float]] = None				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Optional[str] = None				,				__lowerCAmelCase:			Optional[str] = ""				,				**__lowerCAmelCase:			Optional[int]				,				) -> None:




       '''simple docstring'''




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

       __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:			Dict				,				__lowerCAmelCase:			np.ndarray				,				__lowerCAmelCase:			Dict[str, int]				,				__lowerCAmelCase:			PILImageResampling = PILImageResampling.BILINEAR				,				__lowerCAmelCase:			Optional[Union[str, ChannelDimension]] = None				,				**__lowerCAmelCase:			Dict				,				) -> np.ndarray:




       '''simple docstring'''




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






     def 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			np.ndarray				,				__lowerCAmelCase:			Union[int, float]				,				__lowerCAmelCase:			Optional[Union[str, ChannelDimension]] = None				,				**__lowerCAmelCase:			Tuple				,				) -> np.ndarray:




       '''simple docstring'''




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






     def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			np.ndarray				,				__lowerCAmelCase:			Union[float, Iterable[float]]				,				__lowerCAmelCase:			Union[float, Iterable[float]]				,				__lowerCAmelCase:			Optional[Union[str, ChannelDimension]] = None				,				**__lowerCAmelCase:			List[str]				,				) -> np.ndarray:




       '''simple docstring'''




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






     def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			ImageInput				,				__lowerCAmelCase:			bool = None				,				__lowerCAmelCase:			Dict[str, int] = None				,				__lowerCAmelCase:			List[str]=None				,				__lowerCAmelCase:			bool = None				,				__lowerCAmelCase:			float = None				,				__lowerCAmelCase:			bool = None				,				__lowerCAmelCase:			Union[float, Iterable[float]] = None				,				__lowerCAmelCase:			Union[float, Iterable[float]] = None				,				__lowerCAmelCase:			bool = None				,				__lowerCAmelCase:			Optional[str] = None				,				__lowerCAmelCase:			Optional[str] = None				,				__lowerCAmelCase:			Optional[Union[str, TensorType]] = None				,				__lowerCAmelCase:			ChannelDimension = ChannelDimension.FIRST				,				**__lowerCAmelCase:			Tuple				,				) -> PIL.Image.Image:




       '''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(__lowerCAmelCase   )
       __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(__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."   )

       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(__lowerCAmelCase   ) 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    ,		__UpperCAmelCase						    =       apply_tesseract(__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase   )
           words_batch.append(__lowerCAmelCase   )
           boxes_batch.append(__lowerCAmelCase   )

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

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

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

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

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

       if apply_ocr:
         __UpperCAmelCase						    =       words_batch
         __UpperCAmelCase						    =       boxes_batch
       return data




import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Union[str, Path]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= True
  lowerCAmelCase__		:	Optional[int]    		= None
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	Optional[Union[str, bool]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None






  def 	_UpperCAmelCase				(				self:			Any   ) -> "DownloadConfig":




    '''simple docstring'''




    return self.__class__(**{k: copy.deepcopy(__lowerCAmelCase   ) for k, v in self.__dict__.items()}   )



