
"""simple docstring"""

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Optional[Any] ) ->      Dict:






				"""simple docstring"""




				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__						(					lowerCAmelCase				:					np.ndarray ,				lowerCAmelCase				:					Optional[str] ,				lowerCAmelCase				:					Optional[str] = None ) ->      List[str]:






				"""simple docstring"""




				UpperCAmelCase									=					tesseract_config if tesseract_config is not None else ''

				# apply OCR
				UpperCAmelCase									=					to_pil_image(lowerCAmelCase )
				UpperCAmelCase			,							UpperCAmelCase									=					pil_image.size
				UpperCAmelCase									=					pytesseract.image_to_data(lowerCAmelCase ,				lang=lowerCAmelCase ,				output_type='dict' ,				config=lowerCAmelCase )
				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(lowerCAmelCase ) if not word.strip()]
				UpperCAmelCase									=					[word for idx, word in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
				UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
				UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
				UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
				UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]

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

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

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

				return words, normalized_boxes







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
						__SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	["pixel_values"]




						def __init__(							self   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = ""   ,       **lowercase_   ,       )						->					None:
										super().__init__(**lowercase_							)
										UpperCAmelCase									=					size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
										UpperCAmelCase									=					get_size_dict(lowercase_							)

										UpperCAmelCase									=					do_resize
										UpperCAmelCase									=					size
										UpperCAmelCase									=					resample
										UpperCAmelCase									=					apply_ocr
										UpperCAmelCase									=					ocr_lang
										UpperCAmelCase									=					tesseract_config




						def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					np.ndarray:
										UpperCAmelCase									=					get_size_dict(lowercase_							)
										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(lowercase_   ,       size=lowercase_   ,       resample=lowercase_   ,       data_format=lowercase_   ,       **lowercase_							)




						def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = ChannelDimension.FIRST   ,       **lowercase_   ,       )						->					PIL.Image.Image:
										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(lowercase_							)
										UpperCAmelCase									=					resample if resample is not None else self.resample
										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(lowercase_							)

										if not valid_images(lowercase_							):
														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.'							)

										# All transformations expect numpy arrays.
										UpperCAmelCase									=					[to_numpy_array(lowercase_							) for image in images]

										if apply_ocr:
														requires_backends(self   ,       'pytesseract'							)
														UpperCAmelCase									=					[]
														UpperCAmelCase									=					[]
														for image in images:
																		UpperCAmelCase			,							UpperCAmelCase									=					apply_tesseract(lowercase_   ,       lowercase_   ,       lowercase_							)
																		words_batch.append(lowercase_							)
																		boxes_batch.append(lowercase_							)

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

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										UpperCAmelCase									=					[flip_channel_order(lowercase_							) for image in images]
										UpperCAmelCase									=					[to_channel_dimension_format(lowercase_   ,       lowercase_							) for image in images]

										UpperCAmelCase									=					BatchFeature(data={'pixel_values': images}   ,       tensor_type=lowercase_							)

										if apply_ocr:
														UpperCAmelCase									=					words_batch
														UpperCAmelCase									=					boxes_batch
										return data





"""simple docstring"""

from abc import ABC, abstractmethod
from argparse import ArgumentParser
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      @staticmethod
      @abstractmethod
      def     a_      (							lowercase_							)						->					Optional[Any]:
          raise NotImplementedError()




      @abstractmethod
      def     a_      (							self							)						->					Any:
          raise NotImplementedError()



