





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       :str       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						return [
						    int(1_000 * (box[0] / width)     ),
						    int(1_000 * (box[1] / height)     ),
						    int(1_000 * (box[2] / width)     ),
						    int(1_000 * (box[3] / height)     ),
						]







def       UpperCAmelCase							(     _lowerCamelCase      :  np.ndarray			,   _lowerCamelCase      :  Optional[str]			,   _lowerCamelCase      :  Optional[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					to_pil_image(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pil_image.size
						SCREAMING_SNAKE_CASE__     :				Tuple   =					pytesseract.image_to_data(_lowerCamelCase			,   lang=_lowerCamelCase			,   output_type="dict"			,   config=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					data["text"], data["left"], data["top"], data["width"], data["height"]

						# filter empty words and corresponding coordinates
						SCREAMING_SNAKE_CASE__     :				List[str]   =					[idx for idx, word in enumerate(_lowerCamelCase     ) if not word.strip()]
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[word for idx, word in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				int   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				Dict   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				int   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						SCREAMING_SNAKE_CASE__     :				str   =					[]
						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    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     float = 1 / 2_55							,	a				:     bool = True							,	a				:     Union[float, Iterable[float]] = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     bool = True							,	a				:     Optional[str] = None							,	a				:     Optional[str] = ""							,	**a				:     Optional[int]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					size if size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					get_size_dict(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					size
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					resample
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					rescale_value
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				int   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_std if image_std is not None else IMAGENET_STANDARD_STD
													SCREAMING_SNAKE_CASE__     :				str   =					apply_ocr
													SCREAMING_SNAKE_CASE__     :				int   =					ocr_lang
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tesseract_config

							def      A_  (      self				:     Any							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					get_size_dict(a    )
													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()}"""    )
													SCREAMING_SNAKE_CASE__     :				Any   =					(size["height"], size["width"])
													return resize(a							,	size=a							,	resample=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Union[int, float]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Any							,	)	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     Union[float, Iterable[float]]							,	a				:     Union[float, Iterable[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Any							,	a				:     ImageInput							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     List[str]=None							,	a				:     bool = None							,	a				:     float = None							,	a				:     bool = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     bool = None							,	a				:     Optional[str] = None							,	a				:     Optional[str] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     ChannelDimension = ChannelDimension.FIRST							,	**a				:     int							,	)	->PIL.Image.Image:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Any   =					image_std if image_std is not None else self.image_std
													SCREAMING_SNAKE_CASE__     :				str   =					apply_ocr if apply_ocr is not None else self.apply_ocr
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ocr_lang if ocr_lang is not None else self.ocr_lang
													SCREAMING_SNAKE_CASE__     :				List[str]   =					tesseract_config if tesseract_config is not None else self.tesseract_config

													SCREAMING_SNAKE_CASE__     :				str   =					make_list_of_images(a    )

													if not valid_images(a    ):
																			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.
													SCREAMING_SNAKE_CASE__     :				int   =					[to_numpy_array(a    ) for image in images]

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																			requires_backends(self							,	"pytesseract"    )
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[]
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in images:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					apply_tesseract(a							,	a							,	a    )
																									words_batch.append(a    )
																									boxes_batch.append(a    )

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				str   =					[self.resize(image=a							,	size=a							,	resample=a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[self.normalize(image=a							,	mean=a							,	std=a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Any   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				List[str]   =					BatchFeature(data={"pixel_values": images}							,	tensor_type=a    )

													if apply_ocr:
																			SCREAMING_SNAKE_CASE__     :				Any   =					words_batch
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					boxes_batch
													return data






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_SNAKE_CASE__     :				Any   =					input_array

													# Random initial weights are assigned where first argument is the
													# number of nodes in previous layer and second argument is the
													# number of nodes in the next layer.

													# Random initial weights are assigned.
													# self.input_array.shape[1] is used to represent number of nodes in input layer.
													# First hidden layer consists of 4 nodes.
													SCREAMING_SNAKE_CASE__     :				int   =					numpy.random.rand(
													    self.input_array.shape[1]							,	4    )

													# Random initial values for the first hidden layer.
													# First hidden layer has 4 nodes.
													# Second hidden layer has 3 nodes.
													SCREAMING_SNAKE_CASE__     :				Dict   =					numpy.random.rand(
													    4							,	3    )

													# Random initial values for the second hidden layer.
													# Second hidden layer has 3 nodes.
													# Output layer has 1 node.
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

													# Predicted output values by the neural network.
													# Predicted_output array initially consists of zeroes.
													SCREAMING_SNAKE_CASE__     :				Tuple   =					numpy.zeros(output_array.shape    )

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					sigmoid(
													    numpy.dot(self.input_array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
													# connecting the first hidden set of nodes with the second hidden set of nodes.
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													# layer_between_second_hidden_layer_and_output is the layer connecting
													# second hidden layer with the output node.
													SCREAMING_SNAKE_CASE__     :				int   =					sigmoid(
													    numpy.dot(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer							,	self.second_hidden_layer_and_output_layer_weights							,	)    )

													return self.layer_between_second_hidden_layer_and_output

							def      A_  (      self				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					numpy.dot(
													    self.layer_between_first_hidden_layer_and_second_hidden_layer.T							,	2
													    * (self.output_array - self.predicted_output)
													    * sigmoid_derivative(self.predicted_output    )							,	)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					numpy.dot(
													    self.layer_between_input_and_first_hidden_layer.T							,	numpy.dot(
													        2
													        * (self.output_array - self.predicted_output)
													        * sigmoid_derivative(self.predicted_output    )							,	self.second_hidden_layer_and_output_layer_weights.T							,	)
													    * sigmoid_derivative(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer    )							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					numpy.dot(
													    self.input_array.T							,	numpy.dot(
													        numpy.dot(
													            2
													            * (self.output_array - self.predicted_output)
													            * sigmoid_derivative(self.predicted_output    )							,	self.second_hidden_layer_and_output_layer_weights.T							,	)
													        * sigmoid_derivative(
													            self.layer_between_first_hidden_layer_and_second_hidden_layer    )							,	self.first_hidden_layer_and_second_hidden_layer_weights.T							,	)
													    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )							,	)

													self.input_layer_and_first_hidden_layer_weights += (
													    updated_input_layer_and_first_hidden_layer_weights
													)
													self.first_hidden_layer_and_second_hidden_layer_weights += (
													    updated_first_hidden_layer_and_second_hidden_layer_weights
													)
													self.second_hidden_layer_and_output_layer_weights += (
													    updated_second_hidden_layer_and_output_layer_weights
													)

							def      A_  (      self				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sigmoid(
													    numpy.dot(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer							,	self.second_hidden_layer_and_output_layer_weights							,	)    )

													return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return 1 / (1 + numpy.exp(-value     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return (value) * (1 - (value))







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )			,   dtype=numpy.floataa			,   )

						# True output values for the given input values.
						SCREAMING_SNAKE_CASE__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					TwoHiddenLayerNeuralNetwork(
						    input_array=_lowerCamelCase			,   output_array=_lowerCamelCase     )

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=_lowerCamelCase			,   iterations=10			,   give_loss=_lowerCamelCase     )

						return neural_network.predict(numpy.array(([1, 1, 1])			,   dtype=numpy.floataa     )     )


if __name__ == "__main__":
				example()