





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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


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




       def __init__(      self    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						**__lowerCamelCase							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."    ,						__lowerCamelCase    ,						)
                 _SCREAMING_SNAKE_CASE       :	Any						    =							kwargs.pop("feature_extractor"							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							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__(__lowerCamelCase    ,						__lowerCamelCase							)




       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = True    ,						__lowerCamelCase = False    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     BatchEncoding:
            # verify input
            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
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.image_processor(images=__lowerCamelCase    ,						return_tensors=__lowerCamelCase							)

            # second, apply the tokenizer
            if text is not None and self.image_processor.apply_ocr and text_pair is None:
                 if isinstance(__lowerCamelCase    ,						__lowerCamelCase							):
                      _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							features["words"]

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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=__lowerCamelCase    ,						add_special_tokens=__lowerCamelCase    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						stride=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_overflowing_tokens=__lowerCamelCase    ,						return_special_tokens_mask=__lowerCamelCase    ,						return_offsets_mapping=__lowerCamelCase    ,						return_length=__lowerCamelCase    ,						verbose=__lowerCamelCase    ,						return_tensors=__lowerCamelCase    ,						**__lowerCamelCase    ,						)

            # add pixel values
            _SCREAMING_SNAKE_CASE       :	Any						    =							features.pop("pixel_values"							)
            if return_overflowing_tokens is True:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_overflowing_images(__lowerCamelCase    ,						encoded_inputs["overflow_to_sample_mapping"]							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							images

            return encoded_inputs




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Dict:
            # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[]
            for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]							)

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

            return images_with_overflow




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     Any:
            return self.tokenizer.batch_decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[str]:
            return self.tokenizer.decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       @property
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            return ["input_ids", "bbox", "attention_mask", "pixel_values"]




       @property
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,						__lowerCamelCase    ,						)
            return self.image_processor_class







       @property
       def     UpperCamelCase_			(      self							)			->     int:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,						__lowerCamelCase    ,						)
            return self.image_processor






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							len(__lowerCamelCase							) - 1




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     list[float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]
            for i in range(len(self.list_of_points							)							):
                 # basis function for each i
                 output_values.append(
                     comb(self.degree    ,						__lowerCamelCase							) * ((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(__lowerCamelCase							)    ,						5							) == 1
            return output_values




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     tuple[float, float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.basis_function(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							0.0
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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     UpperCamelCase_			(      self    ,						__lowerCamelCase = 0.01							)			->     int:
            from matplotlib import pyplot as plt  # type: ignore

            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # x coordinates of points to plot
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # y coordinates of points to plot

            _SCREAMING_SNAKE_CASE       :	Dict						    =							0.0
            while t <= 1:
                 _SCREAMING_SNAKE_CASE       :	str						    =							self.bezier_curve_function(__lowerCamelCase							)
                 to_plot_x.append(value[0]							)
                 to_plot_y.append(value[1]							)
                 t += step_size

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[i[0] for i in self.list_of_points]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[i[1] for i in self.list_of_points]

            plt.plot(
                __lowerCamelCase    ,						__lowerCamelCase    ,						color="blue"    ,						label="Curve of Degree " + str(self.degree							)    ,						)
            plt.scatter(__lowerCamelCase    ,						__lowerCamelCase    ,						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