





"""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_									=logging.get_logger(__name__)





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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase = None       ):
						_UpperCamelCase      : Tuple         =   tesseract_config if tesseract_config is not None else ''''''

						# apply OCR
						_UpperCamelCase      : Union[str, Any]         =   to_pil_image(_lowercase       )
						_UpperCamelCase       ,							_UpperCamelCase      : Any         =   pil_image.size
						_UpperCamelCase      : Optional[Any]         =   pytesseract.image_to_data(_lowercase	,						lang=_lowercase	,						output_type='''dict'''	,						config=_lowercase       )
						_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Optional[Any]         =   data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

						# filter empty words and corresponding coordinates
						_UpperCamelCase      : List[str]         =   [idx for idx, word in enumerate(_lowercase       ) if not word.strip()]
						_UpperCamelCase      : List[Any]         =   [word for idx, word in enumerate(_lowercase       ) if idx not in irrelevant_indices]
						_UpperCamelCase      : Tuple         =   [coord for idx, coord in enumerate(_lowercase       ) if idx not in irrelevant_indices]
						_UpperCamelCase      : str         =   [coord for idx, coord in enumerate(_lowercase       ) if idx not in irrelevant_indices]
						_UpperCamelCase      : Optional[Any]         =   [coord for idx, coord in enumerate(_lowercase       ) if idx not in irrelevant_indices]
						_UpperCamelCase      : List[Any]         =   [coord for idx, coord in enumerate(_lowercase       ) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						_UpperCamelCase      : Optional[int]         =   []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(_lowercase	,						_lowercase	,						_lowercase       )       )

						assert len(_lowercase       ) == len(_lowercase       ), "Not as many words as there are bounding boxes"

						return words, normalized_boxes






class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''pixel_values''']






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




										'''simple docstring'''



										super().__init__(**lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										_UpperCamelCase      : List[str]         =   get_size_dict(lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   do_resize
										_UpperCamelCase      : Optional[Any]         =   size
										_UpperCamelCase      : List[Any]         =   resample
										_UpperCamelCase      : Any         =   apply_ocr
										_UpperCamelCase      : Optional[Any]         =   ocr_lang
										_UpperCamelCase      : List[str]         =   tesseract_config






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   np.ndarray,			lowerCAmelCase__		:   Dict[str, int],			lowerCAmelCase__		:   PILImageResampling = PILImageResampling.BILINEAR,			lowerCAmelCase__		:   Optional[Union[str, ChannelDimension]] = None,			**lowerCAmelCase__		:   Optional[int],			)     ->				np.ndarray:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   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()}"""							)
										_UpperCamelCase      : int         =   (size['''height'''], size['''width'''])
										return resize(lowerCAmelCase__,			size=lowerCAmelCase__,			resample=lowerCAmelCase__,			data_format=lowerCAmelCase__,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Any,			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__		:   str,			)     ->				PIL.Image.Image:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase      : Any         =   size if size is not None else self.size
										_UpperCamelCase      : str         =   get_size_dict(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   resample if resample is not None else self.resample
										_UpperCamelCase      : Tuple         =   apply_ocr if apply_ocr is not None else self.apply_ocr
										_UpperCamelCase      : Union[str, Any]         =   ocr_lang if ocr_lang is not None else self.ocr_lang
										_UpperCamelCase      : Tuple         =   tesseract_config if tesseract_config is not None else self.tesseract_config

										_UpperCamelCase      : str         =   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.
										_UpperCamelCase      : Tuple         =   [to_numpy_array(lowerCAmelCase__							) for image in images]

										if apply_ocr:
																requires_backends(self,			'''pytesseract'''							)
																_UpperCamelCase      : str         =   []
																_UpperCamelCase      : Any         =   []
																for image in images:
																						_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   apply_tesseract(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)
																						words_batch.append(lowerCAmelCase__							)
																						boxes_batch.append(lowerCAmelCase__							)

										if do_resize:
																_UpperCamelCase      : List[str]         =   [self.resize(image=lowerCAmelCase__,			size=lowerCAmelCase__,			resample=lowerCAmelCase__							) for image in images]

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										_UpperCamelCase      : Any         =   [flip_channel_order(lowerCAmelCase__							) for image in images]
										_UpperCamelCase      : Dict         =   [to_channel_dimension_format(lowerCAmelCase__,			lowerCAmelCase__							) for image in images]

										_UpperCamelCase      : Optional[Any]         =   BatchFeature(data={'''pixel_values''': images},			tensor_type=lowerCAmelCase__							)

										if apply_ocr:
																_UpperCamelCase      : str         =   words_batch
																_UpperCamelCase      : Optional[int]         =   boxes_batch
										return data










"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)



