

"""simple docstring"""




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		:       Optional[Any]						    =	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__ = None				):







							'''simple docstring'''






							snake_case_          =   tesseract_config if tesseract_config is not None else ''

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

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

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

							# finally, normalize the bounding boxes
							snake_case_          =   []
							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 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      ['''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	)
											snake_case_          =   size if size is not None else {'height': 224, 'width': 224}
											snake_case_          =   get_size_dict(snake_case	)

											snake_case_          =   do_resize
											snake_case_          =   size
											snake_case_          =   resample
											snake_case_          =   apply_ocr
											snake_case_          =   ocr_lang
											snake_case_          =   tesseract_config




				def    a							( self				,       snake_case				,       snake_case				,       snake_case = PILImageResampling.BILINEAR				,       snake_case = None				,       **snake_case				,       ):
											snake_case_          =   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()}'''	)
											snake_case_          =   (size['height'], size['width'])
											return resize(snake_case				,       size=snake_case				,       resample=snake_case				,       data_format=snake_case				,       **snake_case	)




				def    a							( 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				,       ):
											snake_case_          =   do_resize if do_resize is not None else self.do_resize
											snake_case_          =   size if size is not None else self.size
											snake_case_          =   get_size_dict(snake_case	)
											snake_case_          =   resample if resample is not None else self.resample
											snake_case_          =   apply_ocr if apply_ocr is not None else self.apply_ocr
											snake_case_          =   ocr_lang if ocr_lang is not None else self.ocr_lang
											snake_case_          =   tesseract_config if tesseract_config is not None else self.tesseract_config

											snake_case_          =   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.
											snake_case_          =   [to_numpy_array(snake_case	) for image in images]

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

											if do_resize:
																		snake_case_          =   [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)
											snake_case_          =   [flip_channel_order(snake_case	) for image in images]
											snake_case_          =   [to_channel_dimension_format(snake_case				,       snake_case	) for image in images]

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

											if apply_ocr:
																		snake_case_          =   words_batch
																		snake_case_          =   boxes_batch
											return data







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							create_all_state(1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							[]       ,							UpperCamelCase__				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):







							'''simple docstring'''






							if level == 0:
														total_list.append(current_list[:]				)
														return

							for i in range(UpperCamelCase__       ,							total_number - level + 2				):
														current_list.append(UpperCamelCase__				)
														create_all_state(i + 1       ,							UpperCamelCase__       ,							level - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														current_list.pop()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for i in total_list:
														print(*UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	4
					_UpperCAmelCase		:       Tuple						    =	2
					_UpperCAmelCase		:       Optional[int]						    =	generate_all_combinations(n, k)
					print_all_state(total_list)




