





"""simple docstring"""




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		:				List[str]  			=     logging.get_logger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					Dict					, _lowercase		:					Dict      )				->List[Any]:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					Tuple					, _lowercase		:					str      )				->List[str]:






	'''simple docstring'''






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

	# filter empty words and corresponding coordinates
	a   :	Union[str, Any]			=       [idx for idx, word in enumerate(_SCREAMING_SNAKE_CASE      ) if not word.strip()]
	a   :	Optional[int]			=       [word for idx, word in enumerate(_SCREAMING_SNAKE_CASE      ) if idx not in irrelevant_indices]
	a   :	Tuple			=       [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE      ) if idx not in irrelevant_indices]
	a   :	Any			=       [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE      ) if idx not in irrelevant_indices]
	a   :	int			=       [coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE      ) if idx not in irrelevant_indices]
	a   :	Optional[int]			=       [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
	a   :	List[str]			=       []
	for x, y, w, h in zip(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      ):
		a   :	Union[str, Any]			=       [x, y, x + w, y + h]
		actual_boxes.append(_SCREAMING_SNAKE_CASE      )

	# finally, normalize the bounding boxes
	a   :	str			=       []
	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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[str]         =['pixel_values']






							def __init__(     self   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = PILImageResampling.BILINEAR   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = 1 / 255   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = ""   ,      **lowerCAmelCase__   ,      )			->				None:
								super().__init__(**lowerCAmelCase__   )
								a   :	Union[str, Any]			=       size if size is not None else {"height": 224, "width": 224}
								a   :	List[str]			=       get_size_dict(lowerCAmelCase__   )

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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = PILImageResampling.BILINEAR   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								a   :	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()}"""   )
								a   :	Optional[Any]			=       (size["height"], size["width"])
								return resize(lowerCAmelCase__   ,      size=lowerCAmelCase__   ,      resample=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								return rescale(lowerCAmelCase__   ,      scale=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								return normalize(lowerCAmelCase__   ,      mean=lowerCAmelCase__   ,      std=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






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

								a   :	Union[str, 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."   )

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

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

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

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

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

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

								a   :	str			=       BatchFeature(data={"pixel_values": images}   ,      tensor_type=lowerCAmelCase__   )

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









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 10					, _lowercase		:					int = 1000					, _lowercase		:					bool = True      )				->int:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	), "Invalid type of value(s) specified to function!"

	if min_val > max_val:
		raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"      )
	return min_val if option else max_val






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->int:






	'''simple docstring'''






	return int((number_a + number_a) / 2      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					int      )				->None:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      )
	), 'argument values must be type of "int"'

	if lower > higher:
		raise ValueError("argument value for lower and higher must be(lower > higher)"      )

	if not lower < to_guess < higher:
		raise ValueError(
		    "guess value must be within the range of lower and higher value"      )

	def answer(_lowercase		:					int      ) -> str:
		if number > to_guess:
			return "high"
		elif number < to_guess:
			return "low"
		else:
			return "same"

	print("started..."      )

	a   :	Optional[Any]			=       lower
	a   :	List[Any]			=       higher

	a   :	Tuple			=       []

	while True:
		a   :	List[Any]			=       get_avg(_lowercase					, _lowercase      )
		last_numbers.append(_lowercase      )

		if answer(_lowercase      ) == "low":
			a   :	Optional[int]			=       number
		elif answer(_lowercase      ) == "high":
			a   :	Tuple			=       number
		else:
			break

	print(F"""guess the number : {last_numbers[-1]}"""      )
	print(F"""details : {last_numbers!s}"""      )






def    _SCREAMING_SNAKE_CASE	(			)				->None:






	'''simple docstring'''






	a   :	Tuple			=       int(input("Enter lower value : "      ).strip()      )
	a   :	Dict			=       int(input("Enter high value : "      ).strip()      )
	a   :	Optional[int]			=       int(input("Enter value to guess : "      ).strip()      )
	guess_the_number(_lowercase					, _lowercase					, _lowercase      )


if __name__ == "__main__":
		main()


