
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_						:     int	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   Any  )   ->			Tuple:






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   np.ndarray ,			_UpperCAmelCase  :   Optional[str] ,			_UpperCAmelCase  :   Optional[str]  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   to_pil_image(_UpperCAmelCase  )
						_lowercase      ,    _lowercase       :	Optional[int]           =   pil_image.size
						_lowercase       :	Any           =   pytesseract.image_to_data(_UpperCAmelCase ,			lang=_UpperCAmelCase ,			output_type="dict" ,			config=_UpperCAmelCase  )
						_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

						# finally, normalize the bounding boxes
						_lowercase       :	Optional[Any]           =   []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )  )

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

						return words, normalized_boxes




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Optional[Any]      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BILINEAR      ,	snake_case				: bool = True      ,	snake_case				: float = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Union[float, Iterable[float]] = None      ,	snake_case				: Union[float, Iterable[float]] = None      ,	snake_case				: bool = True      ,	snake_case				: Optional[str] = None      ,	snake_case				: Optional[str] = ""      ,	**snake_case				: int      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	List[str]           =   size if size is not None else {"height": 224, "width": 224}
							_lowercase       :	Any           =   get_size_dict(snake_case    )

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






	def       _a(self				: Tuple      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BILINEAR      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[Any]      ,	) ->			np.ndarray:
							_lowercase       :	List[Any]           =   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()}"""    )
							_lowercase       :	Tuple           =   (size["height"], size["width"])
							return resize(snake_case      ,	size=snake_case      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Tuple      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, Iterable[float]]      ,	snake_case				: Union[float, Iterable[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: bool = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: Dict=None      ,	snake_case				: bool = None      ,	snake_case				: float = None      ,	snake_case				: bool = None      ,	snake_case				: Union[float, Iterable[float]] = None      ,	snake_case				: Union[float, Iterable[float]] = None      ,	snake_case				: bool = None      ,	snake_case				: Optional[str] = None      ,	snake_case				: Optional[str] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			PIL.Image.Image:
							_lowercase       :	List[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Union[str, Any]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case    )
							_lowercase       :	str           =   resample if resample is not None else self.resample
							_lowercase       :	List[str]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Tuple           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	Optional[int]           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	str           =   image_std if image_std is not None else self.image_std
							_lowercase       :	str           =   apply_ocr if apply_ocr is not None else self.apply_ocr
							_lowercase       :	int           =   ocr_lang if ocr_lang is not None else self.ocr_lang
							_lowercase       :	Optional[Any]           =   tesseract_config if tesseract_config is not None else self.tesseract_config

							_lowercase       :	str           =   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."    )

							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.
							_lowercase       :	Tuple           =   [to_numpy_array(snake_case    ) for image in images]

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

							if do_resize:
													_lowercase       :	List[str]           =   [self.resize(image=snake_case      ,	size=snake_case      ,	resample=snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	Dict           =   [self.rescale(image=snake_case      ,	scale=snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	int           =   [self.normalize(image=snake_case      ,	mean=snake_case      ,	std=snake_case    ) for image in images]

							_lowercase       :	str           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Optional[Any]           =   BatchFeature(data={"pixel_values": images}      ,	tensor_type=snake_case    )

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






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Dict      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,	**snake_case				: Optional[int]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	List[str]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Optional[int]           =   do_resize
							_lowercase       :	List[str]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Union[str, Any]           =   do_center_crop
							_lowercase       :	List[str]           =   crop_size
							_lowercase       :	Tuple           =   do_rescale
							_lowercase       :	Union[str, Any]           =   rescale_factor
							_lowercase       :	str           =   do_normalize
							_lowercase       :	List[Any]           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowercase       :	List[Any]           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_lowercase       :	List[str]           =   int((256 / 224) * size["shortest_edge"]    )
													_lowercase       :	Dict           =   get_resize_output_image_size(snake_case      ,	size=snake_case      ,	default_to_square=snake_case    )
													_lowercase       :	int           =   {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
							return resize(
							    snake_case      ,	size=(size_dict["height"], size_dict["width"])      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[TensorType] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			BatchFeature:
							_lowercase       :	Optional[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Any           =   resample if resample is not None else self.resample
							_lowercase       :	Optional[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	Union[str, Any]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Optional[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Dict           =   image_std if image_std is not None else self.image_std

							_lowercase       :	Optional[int]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Dict           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	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."    )

							if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop 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("Image mean and std must be specified if do_normalize is True."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Dict           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[Any]           =   [self.resize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	Union[str, Any]           =   [self.center_crop(snake_case      ,	snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	List[str]           =   [self.rescale(snake_case      ,	snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	str           =   [self.normalize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	str           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )




