





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		feature_size
             _lowerCamelCase				:  Dict						=		sampling_rate
             _lowerCamelCase				:  Dict						=		padding_value

             _lowerCamelCase				:  int						=		kwargs.pop('''padding_side'''   ,				'''right''')
             _lowerCamelCase				:  List[str]						=		kwargs.pop('''return_attention_mask'''   ,				a__)

             super().__init__(**a__)


      def 					__snake_case      (		self   ,				a__   ,				a__ = True   ,				a__ = None   ,				a__ = False   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				):



             """simple docstring"""





             if isinstance(a__   ,				(list, tuple)) and isinstance(processed_features[0]   ,				(dict, BatchFeature)):
                    _lowerCamelCase				:  Union[str, Any]						=		{
                        key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                    }

             # The model's main input name, usually `input_values`, has be passed for padding
             if self.model_input_names[0] not in processed_features:
                    raise ValueError(
                        '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
                        F""" to this method that includes {self.model_input_names[0]}, but you provided"""
                        F""" {list(processed_features.keys())}""")

             _lowerCamelCase				:  Dict						=		processed_features[self.model_input_names[0]]
             _lowerCamelCase				:  Any						=		(
                 return_attention_mask if return_attention_mask is not None else self.return_attention_mask
             )

             if len(a__) == 0:
                    if return_attention_mask:
                           _lowerCamelCase				:  Optional[int]						=		[]
                    return processed_features

             # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
             # and rebuild them afterwards if no return_tensors is specified
             # Note that we lose the specific device the tensor may be on for PyTorch

             _lowerCamelCase				:  int						=		required_input[0]
             if isinstance(a__   ,				(list, tuple)):
                    # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                    _lowerCamelCase				:  Dict						=		0
                    while len(required_input[index]) == 0:
                           index += 1
                    if index < len(a__):
                           _lowerCamelCase				:  Union[str, Any]						=		required_input[index][0]

             if return_tensors is None:
                    if is_tf_tensor(a__):
                           _lowerCamelCase				:  Optional[Any]						=		'''tf'''
                    elif is_torch_tensor(a__):
                           _lowerCamelCase				:  Tuple						=		'''pt'''
                    elif isinstance(a__   ,				(int, float, list, tuple, np.ndarray)):
                           _lowerCamelCase				:  str						=		'''np'''
                    else:
                           raise ValueError(
                               F"""type of {first_element} unknown: {type(a__)}. """
                               '''Should be one of a python, numpy, pytorch or tensorflow object.''')

             for key, value in processed_features.items():
                    if isinstance(value[0]   ,				(int, float)):
                           _lowerCamelCase				:  Dict						=		to_numpy(a__)
                    else:
                           _lowerCamelCase				:  Optional[int]						=		[to_numpy(a__) for v in value]

        # Convert padding_strategy in PaddingStrategy
             _lowerCamelCase				:  Optional[int]						=		self._get_padding_strategies(padding=a__   ,				max_length=a__)

             _lowerCamelCase				:  List[str]						=		processed_features[self.model_input_names[0]]

             _lowerCamelCase				:  str						=		len(a__)
             if not all(len(a__) == batch_size for v in processed_features.values()):
                    raise ValueError('''Some items in the output dictionary have a different batch size than others.''')

             _lowerCamelCase				:  Any						=		[]
             for i in range(a__):
                    _lowerCamelCase				:  List[Any]						=		{k: v[i] for k, v in processed_features.items()}
                    # truncation
                    _lowerCamelCase				:  Optional[int]						=		self._truncate(
                        a__   ,				max_length=a__   ,				pad_to_multiple_of=a__   ,				truncation=a__   ,				)
                    truncated_inputs.append(a__)

             if padding_strategy == PaddingStrategy.LONGEST:
                    # make sure that `max_length` cannot be longer than the longest truncated length
                    _lowerCamelCase				:  Optional[int]						=		max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
                    _lowerCamelCase				:  Tuple						=		PaddingStrategy.MAX_LENGTH

             _lowerCamelCase				:  str						=		{}
             for i in range(a__):
                    # padding
                    _lowerCamelCase				:  Optional[Any]						=		self._pad(
                        truncated_inputs[i]   ,				max_length=a__   ,				padding_strategy=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=a__   ,				)

                    for key, value in outputs.items():
                           if key not in batch_outputs:
                                  _lowerCamelCase				:  str						=		[]
                           if value.dtype is np.dtype(np.floataa):
                                  _lowerCamelCase				:  Tuple						=		value.astype(np.floataa)
                           batch_outputs[key].append(a__)

             return BatchFeature(a__   ,				tensor_type=a__)


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = PaddingStrategy.DO_NOT_PAD   ,				a__ = None   ,				a__ = None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		processed_features[self.model_input_names[0]]

             if padding_strategy == PaddingStrategy.LONGEST:
                    _lowerCamelCase				:  Union[str, Any]						=		len(a__)

             if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                    _lowerCamelCase				:  int						=		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

             _lowerCamelCase				:  str						=		padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a__) < max_length

             if return_attention_mask and "attention_mask" not in processed_features:
                    _lowerCamelCase				:  int						=		np.ones(len(a__)   ,				dtype=np.intaa)

             if needs_to_be_padded:
                    _lowerCamelCase				:  Dict						=		max_length - len(a__)
                    if self.padding_side == "right":
                           if return_attention_mask:
                                  _lowerCamelCase				:  List[str]						=		np.pad(
                                      processed_features['''attention_mask''']   ,				(0, difference))
                           _lowerCamelCase				:  List[Any]						=		((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                           _lowerCamelCase				:  Union[str, Any]						=		np.pad(
                               a__   ,				a__   ,				'''constant'''   ,				constant_values=self.padding_value)
                    elif self.padding_side == "left":
                           if return_attention_mask:
                                  _lowerCamelCase				:  List[str]						=		np.pad(
                                      processed_features['''attention_mask''']   ,				(difference, 0))
                           _lowerCamelCase				:  Tuple						=		((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                           _lowerCamelCase				:  Optional[int]						=		np.pad(
                               a__   ,				a__   ,				'''constant'''   ,				constant_values=self.padding_value)
                    else:
                           raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

             return processed_features


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				):



             """simple docstring"""





             if not truncation:
                    return processed_features
             elif truncation and max_length is None:
                    raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''')

             _lowerCamelCase				:  Dict						=		processed_features[self.model_input_names[0]]

             # find `max_length` that fits `pad_to_multiple_of`
             if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                    _lowerCamelCase				:  Tuple						=		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

             _lowerCamelCase				:  Union[str, Any]						=		len(a__) > max_length

             if needs_to_be_truncated:
                    _lowerCamelCase				:  int						=		processed_features[self.model_input_names[0]][:max_length]
                    if "attention_mask" in processed_features:
                           _lowerCamelCase				:  Optional[int]						=		processed_features['''attention_mask'''][:max_length]

             return processed_features



      def 					__snake_case      (		self   ,				a__=False   ,				a__=None):



             """simple docstring"""





             if padding is not False:
                    if padding is True:
                           _lowerCamelCase				:  Union[str, Any]						=		PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                    elif not isinstance(a__   ,				a__):
                           _lowerCamelCase				:  List[Any]						=		PaddingStrategy(a__)
                    elif isinstance(a__   ,				a__):
                           _lowerCamelCase				:  Optional[Any]						=		padding
             else:
                    _lowerCamelCase				:  Union[str, Any]						=		PaddingStrategy.DO_NOT_PAD

             # Set max length if needed
             if max_length is None:
                    if padding_strategy == PaddingStrategy.MAX_LENGTH:
                           raise ValueError(
                               F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""")

        # Test if we have a padding value
             if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                    raise ValueError(
                        '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
                        ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''')

             return padding_strategy








from __future__ import annotations

from scipy.special import comb  # type: ignore







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             _lowerCamelCase				:  List[Any]						=		len(a__) - 1


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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

             _lowerCamelCase				:  Any						=		self.basis_function(a__)
             _lowerCamelCase				:  List[Any]						=		0.0
             _lowerCamelCase				:  List[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   ,				a__ = 0.01):



             """simple docstring"""





             from matplotlib import pyplot as plt  # type: ignore

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

             _lowerCamelCase				:  List[Any]						=		0.0
             while t <= 1:
                    _lowerCamelCase				:  Tuple						=		self.bezier_curve_function(a__)
                    to_plot_x.append(value[0])
                    to_plot_y.append(value[1])
                    t += step_size

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

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

