

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							['image_processor', 'tokenizer']
    lowerCamelCase		     =							'LayoutLMv3ImageProcessor'
    lowerCamelCase		     =							('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')


    def __init__(				self			:	List[str],lowercase_			:	Optional[Any]=None,lowercase_			:	int=None,**lowercase_			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       None
        if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.',lowercase_,)
            A__   				=       kwargs.pop('feature_extractor'		)

        A__   				=       image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'		)
        if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'		)

        super().__init__(lowercase_,lowercase_		)


    def __call__(				self			:	str,lowercase_			:	Optional[int],lowercase_			:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,lowercase_			:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,lowercase_			:	Union[List[List[int]], List[List[List[int]]]] = None,lowercase_			:	Optional[Union[List[int], List[List[int]]]] = None,lowercase_			:	bool = True,lowercase_			:	Union[bool, str, PaddingStrategy] = False,lowercase_			:	Union[bool, str, TruncationStrategy] = None,lowercase_			:	Optional[int] = None,lowercase_			:	int = 0,lowercase_			:	Optional[int] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	bool = False,lowercase_			:	bool = False,lowercase_			:	bool = False,lowercase_			:	bool = False,lowercase_			:	bool = True,lowercase_			:	Optional[Union[str, TensorType]] = None,**lowercase_			:	Tuple,)->   BatchEncoding:





        '''simple docstring'''







        if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

        if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

        # first, apply the image processor
        A__   				=       self.image_processor(images=lowercase_,return_tensors=lowercase_		)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(lowercase_,lowercase_		):
                A__   				=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
            A__   				=       features['words']

        A__   				=       self.tokenizer(
            text=text if text is not None else features['words'],text_pair=text_pair if text_pair is not None else None,boxes=boxes if boxes is not None else features['boxes'],word_labels=lowercase_,add_special_tokens=lowercase_,padding=lowercase_,truncation=lowercase_,max_length=lowercase_,stride=lowercase_,pad_to_multiple_of=lowercase_,return_token_type_ids=lowercase_,return_attention_mask=lowercase_,return_overflowing_tokens=lowercase_,return_special_tokens_mask=lowercase_,return_offsets_mapping=lowercase_,return_length=lowercase_,verbose=lowercase_,return_tensors=lowercase_,**lowercase_,)

        # add pixel values
        A__   				=       features.pop('pixel_values'		)
        if return_overflowing_tokens is True:
            A__   				=       self.get_overflowing_images(lowercase_,encoded_inputs['overflow_to_sample_mapping']		)
        A__   				=       images

        return encoded_inputs


    def 						snake_case__					(				self			:	Any,lowercase_			:	Optional[Any],lowercase_			:	List[str]		)->   Dict:





        '''simple docstring'''







        A__   				=       []
        for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]		)

        if len(lowercase_		) != len(lowercase_		):
            raise ValueError(
                'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                F' {len(lowercase_		)} and {len(lowercase_		)}'		)

        return images_with_overflow


    def 						snake_case__					(				self			:	Union[str, Any],*lowercase_			:	Optional[Any],**lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.tokenizer.batch_decode(*lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	Optional[int],*lowercase_			:	Tuple,**lowercase_			:	Tuple		)->   Dict:





        '''simple docstring'''







        return self.tokenizer.decode(*lowercase_,**lowercase_		)


    @property
    def 						snake_case__					(				self			:	List[str]		)->   Union[str, Any]:





        '''simple docstring'''







        return ["input_ids", "bbox", "attention_mask", "pixel_values"]


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',lowercase_,)
        return self.image_processor_class


    @property
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',lowercase_,)
        return self.image_processor




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       0.0
        for i in range(len(self.list_of_points		)		):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)


    def 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

        A__   				=       []  # x coordinates of points to plot
        A__   				=       []  # y coordinates of points to plot

        A__   				=       0.0
        while t <= 1:
            A__   				=       self.bezier_curve_function(lowercase_		)
            to_plot_x.append(value[0]		)
            to_plot_y.append(value[1]		)
            t += step_size

        A__   				=       [i[0] for i in self.list_of_points]
        A__   				=       [i[1] for i in self.list_of_points]

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,color='red',label='Control Points'		)
        plt.legend()
        plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
     BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
     BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

