
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

lowerCamelCase__          =		logging.get_logger(__name__)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		int:
			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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			lowerCAmelCase__      :      List[Any]         =		to_pil_image(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      ,				lowerCAmelCase__      :      Dict         =		pil_image.size
			lowerCAmelCase__      :      Optional[int]         =		pytesseract.image_to_data(SCREAMING_SNAKE_CASE_       ,							lang=SCREAMING_SNAKE_CASE_       ,							output_type='dict'       ,							config=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

			return words, normalized_boxes







class      A__							(     __magic_name__	):
				lowercase			    =  ['pixel_values']






				def __init__(						self					:      List[Any]    ,     a					:      bool = True    ,     a					:      Dict[str, int] = None    ,     a					:      PILImageResampling = PILImageResampling.BILINEAR    ,     a					:      bool = True    ,     a					:      float = 1 / 255    ,     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					:      Tuple    ,     ):






							'''simple docstring'''







							super().__init__(**a   )
							lowerCAmelCase__      :      Dict         =		size if size is not None else {'height': 224, 'width': 224}
							lowerCAmelCase__      :      Union[str, Any]         =		get_size_dict(a   )

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






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






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		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()}'''   )
							lowerCAmelCase__      :      Union[str, Any]         =		(size['height'], size['width'])
							return resize(a    ,     size=a    ,     resample=a    ,     data_format=a    ,     **a   )






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






							'''simple docstring'''







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






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






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      ImageInput    ,     a					:      bool = None    ,     a					:      Dict[str, int] = None    ,     a					:      Union[str, 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					:      int    ,     ):






							'''simple docstring'''







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

							lowerCAmelCase__      :      int         =		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.
							lowerCAmelCase__      :      Optional[int]         =		[to_numpy_array(a   ) for image in images]

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

							if do_resize:
										lowerCAmelCase__      :      Optional[int]         =		[self.resize(image=a    ,     size=a    ,     resample=a   ) for image in images]

							if do_rescale:
										lowerCAmelCase__      :      int         =		[self.rescale(image=a    ,     scale=a   ) for image in images]

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

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

							lowerCAmelCase__      :      Union[str, Any]         =		BatchFeature(data={'pixel_values': images}    ,     tensor_type=a   )

							if apply_ocr:
										lowerCAmelCase__      :      int         =		words_batch
										lowerCAmelCase__      :      List[str]         =		boxes_batch
							return data

import re
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:

			if len(re.findall('[ATCG]'       ,							SCREAMING_SNAKE_CASE_     )     ) != len(SCREAMING_SNAKE_CASE_     ):
						raise ValueError('Invalid Strand'     )

			return dna.translate(dna.maketrans('ATCG'       ,							'TAGC'     )     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()