


"""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	:  Dict            =     logging.get_logger(__name__)


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     to_pil_image(_UpperCamelCase )
			__lowerCAmelCase       ,      __lowerCAmelCase							       =     pil_image.size
			__lowerCAmelCase							       =     pytesseract.image_to_data(_UpperCamelCase       ,					lang=_UpperCamelCase       ,					output_type="dict"       ,					config=_UpperCamelCase )
			__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     data["text"], data["left"], data["top"], data["width"], data["height"]

			# filter empty words and corresponding coordinates
			__lowerCAmelCase							       =     [idx for idx, word in enumerate(_UpperCamelCase ) if not word.strip()]
			__lowerCAmelCase							       =     [word for idx, word in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
			__lowerCAmelCase							       =     [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
			__lowerCAmelCase							       =     [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
			__lowerCAmelCase							       =     [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
			__lowerCAmelCase							       =     [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]

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

			# finally, normalize the bounding boxes
			__lowerCAmelCase							       =     []
			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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =["""pixel_values"""]

							def __init__(	self       ,  __a = True       ,  __a = None       ,  __a = PILImageResampling.BILINEAR       ,  __a = True       ,  __a = 1 / 2_55       ,  __a = True       ,  __a = None       ,  __a = None       ,  __a = True       ,  __a = None       ,  __a = ""       ,  **__a       ,  ):
										super().__init__(**__a      )
										__lowerCAmelCase							       =     size if size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase							       =     get_size_dict(__a      )

										__lowerCAmelCase							       =     do_resize
										__lowerCAmelCase							       =     size
										__lowerCAmelCase							       =     resample
										__lowerCAmelCase							       =     do_rescale
										__lowerCAmelCase							       =     rescale_value
										__lowerCAmelCase							       =     do_normalize
										__lowerCAmelCase							       =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__lowerCAmelCase							       =     image_std if image_std is not None else IMAGENET_STANDARD_STD
										__lowerCAmelCase							       =     apply_ocr
										__lowerCAmelCase							       =     ocr_lang
										__lowerCAmelCase							       =     tesseract_config

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a = PILImageResampling.BILINEAR       ,  __a = None       ,  **__a       ,  ):
										__lowerCAmelCase							       =     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							       =     (size["height"], size["width"])
										return resize(__a       ,  size=__a       ,  resample=__a       ,  data_format=__a       ,  **__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a = None       ,  **__a       ,  ):
										return rescale(__a       ,  scale=__a       ,  data_format=__a       ,  **__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  **__a       ,  ):
										return normalize(__a       ,  mean=__a       ,  std=__a       ,  data_format=__a       ,  **__a      )



							def      snake_case							(	self       ,  __a       ,  __a = None       ,  __a = None       ,  __a=None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = ChannelDimension.FIRST       ,  **__a       ,  ):
										__lowerCAmelCase							       =     do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase							       =     size if size is not None else self.size
										__lowerCAmelCase							       =     get_size_dict(__a      )
										__lowerCAmelCase							       =     resample if resample is not None else self.resample
										__lowerCAmelCase							       =     do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =     rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase							       =     do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase							       =     image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase							       =     image_std if image_std is not None else self.image_std
										__lowerCAmelCase							       =     apply_ocr if apply_ocr is not None else self.apply_ocr
										__lowerCAmelCase							       =     ocr_lang if ocr_lang is not None else self.ocr_lang
										__lowerCAmelCase							       =     tesseract_config if tesseract_config is not None else self.tesseract_config

										__lowerCAmelCase							       =     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							       =     [to_numpy_array(__a      ) for image in images]

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

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

										if do_rescale:
													__lowerCAmelCase							       =     [self.rescale(image=__a       ,  scale=__a      ) for image in images]

										if do_normalize:
													__lowerCAmelCase							       =     [self.normalize(image=__a       ,  mean=__a       ,  std=__a      ) for image in images]

										__lowerCAmelCase							       =     [to_channel_dimension_format(__a       ,  __a      ) for image in images]

										__lowerCAmelCase							       =     BatchFeature(data={"pixel_values": images}       ,  tensor_type=__a      )

										if apply_ocr:
													__lowerCAmelCase							       =     words_batch
													__lowerCAmelCase							       =     boxes_batch
										return data








"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase							       =     True
						for i in range(0       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False

						for i in range(1       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A	:  Tuple            =     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A	:  Dict            =     odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




