




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_       (					lowerCamelCase_							):
				__a     :       Tuple						=     ["image_processor", "tokenizer"]
				__a     :       str						=     "LayoutLMv3ImageProcessor"
				__a     :       Optional[int]						=     ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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__(snake_case__      ,snake_case__ )






				def __call__(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = True      ,snake_case__ = False      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = 0      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = True      ,snake_case__ = None      ,**snake_case__      ,):
							# 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=snake_case__      ,return_tensors=snake_case__ )

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

							SCREAMING_SNAKE_CASE_   : Optional[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=snake_case__      ,add_special_tokens=snake_case__      ,padding=snake_case__      ,truncation=snake_case__      ,max_length=snake_case__      ,stride=snake_case__      ,pad_to_multiple_of=snake_case__      ,return_token_type_ids=snake_case__      ,return_attention_mask=snake_case__      ,return_overflowing_tokens=snake_case__      ,return_special_tokens_mask=snake_case__      ,return_offsets_mapping=snake_case__      ,return_length=snake_case__      ,verbose=snake_case__      ,return_tensors=snake_case__      ,**snake_case__      ,)

							# add pixel values
							SCREAMING_SNAKE_CASE_   : List[str]					  =      features.pop('pixel_values' )
							if return_overflowing_tokens is True:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_overflowing_images(snake_case__      ,encoded_inputs['overflow_to_sample_mapping'] )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      images

							return encoded_inputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							SCREAMING_SNAKE_CASE_   : int					  =      []
							for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx] )

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

							return images_with_overflow






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.batch_decode(*snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.decode(*snake_case__      ,**snake_case__ )






				@property
				def 		snake_case	(			self ):
							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






				@property
				def 		snake_case	(			self ):
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,snake_case__      ,)
							return self.image_processor_class






				@property
				def 		snake_case	(			self ):
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,snake_case__      ,)
							return self.image_processor









from __future__ import annotations

from scipy.special import comb  # type: ignore







class 				lowerCAmelCase_       :






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






				def 		snake_case	(			self      ,snake_case__ ):
							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      ,snake_case__ ) * ((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(snake_case__ )      ,5 ) == 1
							return output_values






				def 		snake_case	(			self      ,snake_case__ ):
							assert 0 <= t <= 1, "Time t must be between 0 and 1."

							SCREAMING_SNAKE_CASE_   : int					  =      self.basis_function(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      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 		snake_case	(			self      ,snake_case__ = 0.01 ):
							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_   : List[str]					  =      0.0
							while t <= 1:
										SCREAMING_SNAKE_CASE_   : int					  =      self.bezier_curve_function(snake_case__ )
										to_plot_x.append(value[0] )
										to_plot_y.append(value[1] )
										t += step_size

							SCREAMING_SNAKE_CASE_   : Dict					  =      [i[0] for i in self.list_of_points]
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [i[1] for i in self.list_of_points]

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



