






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

A__      :     Optional[int]       =					logging.get_logger(__name__)






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_ = None							):






				'''simple docstring'''
				lowercase__  					=     tesseract_config if tesseract_config is not None else ''''''

				# apply OCR
				lowercase__  					=     to_pil_image(lowerCamelCase_							)
				lowercase__					,		lowercase__  					=     pil_image.size
				lowercase__  					=     pytesseract.image_to_data(lowerCamelCase_						,  lang=lowerCamelCase_						,  output_type='''dict'''						,  config=lowerCamelCase_							)
				lowercase__					,		lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

				# finally, normalize the bounding boxes
				lowercase__  					=     []
				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   (			A__      ):

				"""simple docstring"""




				lowercase__											=			["""pixel_values"""]




				def __init__(	self		:       Tuple,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Dict[str, int] = None,    lowerCamelCase		:       PILImageResampling = PILImageResampling.BILINEAR,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Optional[str] = None,    lowerCamelCase		:       Optional[str] = "",    **lowerCamelCase		:       Optional[Any],    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )
								lowercase__  					=     size if size is not None else {'''height''': 224, '''width''': 224}
								lowercase__  					=     get_size_dict(lowerCamelCase    )

								lowercase__  					=     do_resize
								lowercase__  					=     size
								lowercase__  					=     resample
								lowercase__  					=     apply_ocr
								lowercase__  					=     ocr_lang
								lowercase__  					=     tesseract_config




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       np.ndarray,    lowerCamelCase		:       Dict[str, int],    lowerCamelCase		:       PILImageResampling = PILImageResampling.BILINEAR,    lowerCamelCase		:       Optional[Union[str, ChannelDimension]] = None,    **lowerCamelCase		:       Optional[int],    ):

								'''simple docstring'''
								lowercase__  					=     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()}"""    )
								lowercase__  					=     (size['''height'''], size['''width'''])
								return resize(lowerCamelCase,    size=lowerCamelCase,    resample=lowerCamelCase,    data_format=lowerCamelCase,    **lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str],    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		:       Tuple,    ):

								'''simple docstring'''
								lowercase__  					=     do_resize if do_resize is not None else self.do_resize
								lowercase__  					=     size if size is not None else self.size
								lowercase__  					=     get_size_dict(lowerCamelCase    )
								lowercase__  					=     resample if resample is not None else self.resample
								lowercase__  					=     apply_ocr if apply_ocr is not None else self.apply_ocr
								lowercase__  					=     ocr_lang if ocr_lang is not None else self.ocr_lang
								lowercase__  					=     tesseract_config if tesseract_config is not None else self.tesseract_config

								lowercase__  					=     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.
								lowercase__  					=     [to_numpy_array(lowerCamelCase    ) for image in images]

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

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

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

								lowercase__  					=     BatchFeature(data={'''pixel_values''': images},    tensor_type=lowerCamelCase    )

								if apply_ocr:
												lowercase__  					=     words_batch
												lowercase__  					=     boxes_batch
								return data












import heapq as hq
import math
from collections.abc import Iterator
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       str,    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     str(id_    )
								lowercase__  					=     None
								lowercase__  					=     None
								lowercase__  					=     []
								lowercase__  					=     {}  # {vertex:distance}




				def __lt__(	self		:       Dict,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								return self.key < other.key




				def __repr__(	self		:       Dict    ):

								'''simple docstring'''
								return self.id




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								self.neighbors.append(lowerCamelCase    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     weight






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				# add the neighbors:
				graph[a - 1].add_neighbor(graph[b - 1]							)
				graph[b - 1].add_neighbor(graph[a - 1]							)
				# add the edges:
				graph[a - 1].add_edge(graph[b - 1]						,  lowerCamelCase_							)
				graph[b - 1].add_edge(graph[a - 1]						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0
				lowercase__  					=     graph[:]
				while q:
								lowercase__  					=     min(lowerCamelCase_							)
								q.remove(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in q) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
				for i in range(1						,  len(lowerCamelCase_							)							):
								a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
				return a






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0

				lowercase__  					=     list(lowerCamelCase_							)
				hq.heapify(lowerCamelCase_							)

				while h:
								lowercase__  					=     hq.heappop(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in h) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
																hq.heapify(lowerCamelCase_							)

				for i in range(1						,  len(lowerCamelCase_							)							):
								yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)






def     a     (		):






				'''simple docstring'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()




