


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

__lowerCAmelCase	: int  				=logging.get_logger(__name__)


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = None      ):
						__SCREAMING_SNAKE_CASE						:     int					=		tesseract_config if tesseract_config is not None else ''''''

						# apply OCR
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		to_pil_image(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		pil_image.size
						__SCREAMING_SNAKE_CASE						:     Tuple					=		pytesseract.image_to_data(lowercase__    ,       lang=lowercase__    ,       output_type='''dict'''    ,       config=lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

						# finally, normalize the bounding boxes
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						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        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		['''pixel_values''']


				def __init__(       self			:Optional[int]			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:PILImageResampling = PILImageResampling.BILINEAR			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Optional[str] = None			,			lowerCAmelCase__			:Optional[str] = ""			,			**lowerCAmelCase__			:str			,			)		->			None:
										super().__init__(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		size if size is not None else {'''height''': 224, '''width''': 224}
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		get_size_dict(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		do_resize
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		size
										__SCREAMING_SNAKE_CASE						:     int					=		resample
										__SCREAMING_SNAKE_CASE						:     int					=		apply_ocr
										__SCREAMING_SNAKE_CASE						:     Tuple					=		ocr_lang
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tesseract_config


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



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:ImageInput			,			lowerCAmelCase__			:bool = None			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:PILImageResampling = None			,			lowerCAmelCase__			:bool = None			,			lowerCAmelCase__			:Optional[str] = None			,			lowerCAmelCase__			:Optional[str] = None			,			lowerCAmelCase__			:Optional[Union[str, TensorType]] = None			,			lowerCAmelCase__			:ChannelDimension = ChannelDimension.FIRST			,			**lowerCAmelCase__			:Dict			,			)		->			PIL.Image.Image:
										__SCREAMING_SNAKE_CASE						:     Any					=		do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size if size is not None else self.size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_size_dict(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		apply_ocr if apply_ocr is not None else self.apply_ocr
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		ocr_lang if ocr_lang is not None else self.ocr_lang
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tesseract_config if tesseract_config is not None else self.tesseract_config

										__SCREAMING_SNAKE_CASE						:     Any					=		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.'''	)

										# All transformations expect numpy arrays.
										__SCREAMING_SNAKE_CASE						:     Any					=		[to_numpy_array(lowerCAmelCase__	) for image in images]

										if apply_ocr:
																requires_backends(self			,			'''pytesseract'''	)
																__SCREAMING_SNAKE_CASE						:     Tuple					=		[]
																__SCREAMING_SNAKE_CASE						:     Any					=		[]
																for image in images:
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		apply_tesseract(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)
																						words_batch.append(lowerCAmelCase__	)
																						boxes_batch.append(lowerCAmelCase__	)

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

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										__SCREAMING_SNAKE_CASE						:     Any					=		[flip_channel_order(lowerCAmelCase__	) for image in images]
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		[to_channel_dimension_format(lowerCAmelCase__			,			lowerCAmelCase__	) for image in images]

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BatchFeature(data={'''pixel_values''': images}			,			tensor_type=lowerCAmelCase__	)

										if apply_ocr:
																__SCREAMING_SNAKE_CASE						:     Any					=		words_batch
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		boxes_batch
										return data




from __future__ import annotations

import bisect


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Any					=		lo + (hi - lo) // 2
												if sorted_collection[mid] < item:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		lo + (hi - lo) // 2
												if sorted_collection[mid] <= item:
																		__SCREAMING_SNAKE_CASE						:     Any					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_left(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_right(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      ) - 1

						while left <= right:
												__SCREAMING_SNAKE_CASE						:     str					=		left + (right - left) // 2
												__SCREAMING_SNAKE_CASE						:     List[str]					=		sorted_collection[midpoint]
												if current_item == item:
																		return midpoint
												elif item < current_item:
																		__SCREAMING_SNAKE_CASE						:     int					=		midpoint - 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		midpoint + 1
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		bisect.bisect_left(lowercase__    ,       lowercase__      )
						if index != len(lowercase__      ) and sorted_collection[index] == item:
												return index
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if right < left:
												return None

						__SCREAMING_SNAKE_CASE						:     int					=		left + (right - left) // 2

						if sorted_collection[midpoint] == item:
												return midpoint
						elif sorted_collection[midpoint] > item:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       lowercase__    ,       midpoint - 1      )
						else:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       midpoint + 1    ,       lowercase__      )


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=input('Enter numbers separated by comma:\n').strip()
						__lowerCAmelCase	: str  				=sorted(int(item) for item in user_input.split(','))
						__lowerCAmelCase	: Tuple  				=int(input('Enter a single number to be found in the list:\n'))
						__lowerCAmelCase	: Tuple  				=binary_search(collection, target)
						if result is None:
												print(f"""{target} was not found in {collection}.""")
						else:
												print(f"""{target} was found at position {result} in {collection}.""")
