
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

UpperCAmelCase       =					logging.get_logger(__name__)






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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = None				):
	lowercase	    =       tesseract_config if tesseract_config is not None else ''

	# apply OCR
	lowercase	    =       to_pil_image(__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       pil_image.size
	lowercase	    =       pytesseract.image_to_data(__SCREAMING_SNAKE_CASE					,				lang=__SCREAMING_SNAKE_CASE					,				output_type='dict'					,				config=__SCREAMING_SNAKE_CASE				)
	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(__SCREAMING_SNAKE_CASE				) if not word.strip()]
	lowercase	    =       [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [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
	lowercase	    =       []
	for x, y, w, h in zip(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
		lowercase	    =       [x, y, x + w, y + h]
		actual_boxes.append(__SCREAMING_SNAKE_CASE				)

	# finally, normalize the bounding boxes
	lowercase	    =       []
	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 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[str]									=			["""pixel_values"""]





	def __init__(			self						,       snake_case = True						,       snake_case = None						,       snake_case = PILImageResampling.BILINEAR						,       snake_case = True						,       snake_case = None						,       snake_case = ""						,       **snake_case						,       ):
		super().__init__(**snake_case			)
		lowercase	    =       size if size is not None else {'height': 224, 'width': 224}
		lowercase	    =       get_size_dict(snake_case			)

		lowercase	    =       do_resize
		lowercase	    =       size
		lowercase	    =       resample
		lowercase	    =       apply_ocr
		lowercase	    =       ocr_lang
		lowercase	    =       tesseract_config





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = PILImageResampling.BILINEAR						,       snake_case = None						,       **snake_case						,       ):
		lowercase	    =       get_size_dict(snake_case			)
		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(snake_case						,       size=snake_case						,       resample=snake_case						,       data_format=snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = ChannelDimension.FIRST						,       **snake_case						,       ):
		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(snake_case			)
		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(snake_case			)

		if not valid_images(snake_case			):
			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(snake_case			) for image in images]

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

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

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

		lowercase	    =       BatchFeature(data={'pixel_values': images}						,       tensor_type=snake_case			)

		if apply_ocr:
			lowercase	    =       words_batch
			lowercase	    =       boxes_batch
		return data









'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()