
'''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

lowercase_    =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:int		):
						return [
						    int(1000 * (box[0] / width)		),
						    int(1000 * (box[1] / height)		),
						    int(1000 * (box[2] / width)		),
						    int(1000 * (box[3] / height)		),
						]


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Union[str, Any]		):
						_lowerCAmelCase  						=			to_pil_image(_UpperCAmelCase		)
						_lowerCAmelCase  						=			pil_image.size
						_lowerCAmelCase  						=			pytesseract.image_to_data(_UpperCAmelCase			,     lang=_UpperCAmelCase			,     output_type="""dict"""			,     config=_UpperCAmelCase		)
						_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_					(		__UpperCamelCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       ['''pixel_values''']




						def __init__( self   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = PILImageResampling.BILINEAR   ,       _lowercase = True   ,       _lowercase = 1 / 255   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = ""   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)
												_lowerCAmelCase  						=			size if size is not None else {'height': 224, 'width': 224}
												_lowerCAmelCase  						=			get_size_dict(_lowercase				)

												_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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = PILImageResampling.BILINEAR   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			get_size_dict(_lowercase				)
												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(_lowercase   ,       size=_lowercase   ,       resample=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												return rescale(_lowercase   ,       scale=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												return normalize(_lowercase   ,       mean=_lowercase   ,       std=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




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

												"""simple docstring"""



												_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(_lowercase				)
												_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(_lowercase				)

												if not valid_images(_lowercase				):
																		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(_lowercase				) 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  						=			apply_tesseract(_lowercase   ,       _lowercase   ,       _lowercase				)
																								words_batch.append(_lowercase				)
																								boxes_batch.append(_lowercase				)

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

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

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

												_lowerCAmelCase  						=			[to_channel_dimension_format(_lowercase   ,       _lowercase				) for image in images]

												_lowerCAmelCase  						=			BatchFeature(data={"""pixel_values""": images}   ,       tensor_type=_lowercase				)

												if apply_ocr:
																		_lowerCAmelCase  						=			words_batch
																		_lowerCAmelCase  						=			boxes_batch
												return data


'''simple docstring'''



def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:list[int]		):

						# Check if the input is valid
						if not len(__lowerCamelCase		) == len(__lowerCamelCase		) == 3:
												raise ValueError("""Please enter a valid equation."""		)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""		)

						# Extract the coefficients
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa

						# Calculate the determinants of the matrices
						_lowerCAmelCase  						=			aa * ba - aa * ba
						_lowerCAmelCase  						=			ca * ba - ca * ba
						_lowerCAmelCase  						=			aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""		)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""		)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowerCAmelCase  						=			determinant_x / determinant
																		_lowerCAmelCase  						=			determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)
