





import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCamelCase__      =random.Random()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=1.0,       __lowerCamelCase=None,       __lowerCamelCase=None	):
     if rng is None:
          _SCREAMING_SNAKE_CASE       :	Any						    =							global_rng

     _SCREAMING_SNAKE_CASE       :	int						    =							[]
     for batch_idx in range(shape[0]	):
          values.append([]	)
          for _ in range(shape[1]	):
               values[-1].append(rng.random() * scale	)

     return values





class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=7    ,						__lowerCamelCase=4_0_0    ,						__lowerCamelCase=2_0_0_0    ,						__lowerCamelCase=1    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=1_6_0_0_0    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							parent
            _SCREAMING_SNAKE_CASE       :	str						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							min_seq_length
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							max_seq_length
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							feature_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							padding_value
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							sampling_rate
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							return_attention_mask
            _SCREAMING_SNAKE_CASE       :	str						    =							do_normalize




       def     UpperCamelCase_			(      self							)			->     List[str]:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "return_attention_mask": self.return_attention_mask,
                "do_normalize": self.do_normalize,
            }







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False							)			->     List[Any]:
            def _flatten(__lowerCamelCase							):
                 return list(itertools.chain(*__lowerCamelCase							)							)

            if equal_length:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_list((self.batch_size, self.max_seq_length)							)
            else:
                 # make sure that inputs increase in size
                 _SCREAMING_SNAKE_CASE       :	str						    =							[
                     _flatten(floats_list((x, self.feature_size)							)							)
                     for x in range(self.min_seq_length    ,						self.max_seq_length    ,						self.seq_length_diff							)
                 ]

            if numpify:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[np.asarray(__lowerCamelCase							) for x in speech_inputs]

            return speech_inputs





class 			lowerCAmelCase__( __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    WavaVecaFeatureExtractor




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							WavaVecaFeatureExtractionTester(self							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Optional[Any]:
            self.assertTrue(np.all(np.mean(__lowerCamelCase    ,						axis=0							) < 1E-3							)							)
            self.assertTrue(np.all(np.abs(np.var(__lowerCamelCase    ,						axis=0							) - 1							) < 1E-3							)							)




       def     UpperCamelCase_			(      self							)			->     str:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            # create three inputs of length 800, 1000, and 1200
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]
            _SCREAMING_SNAKE_CASE       :	int						    =							[np.asarray(__lowerCamelCase							) for speech_input in speech_inputs]

            # Test not batched input
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							feat_extract(speech_inputs[0]    ,						return_tensors="np"							).input_values
            _SCREAMING_SNAKE_CASE       :	Any						    =							feat_extract(np_speech_inputs[0]    ,						return_tensors="np"							).input_values
            self.assertTrue(np.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-3							)							)

            # Test batched
            _SCREAMING_SNAKE_CASE       :	Dict						    =							feat_extract(__lowerCamelCase    ,						return_tensors="np"							).input_values
            _SCREAMING_SNAKE_CASE       :	str						    =							feat_extract(__lowerCamelCase    ,						return_tensors="np"							).input_values
            for enc_seq_a, enc_seq_a in zip(__lowerCamelCase    ,						__lowerCamelCase							):
                 self.assertTrue(np.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-3							)							)

            # Test 2-D numpy arrays are batched.
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							np.asarray(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							feat_extract(__lowerCamelCase    ,						return_tensors="np"							).input_values
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							feat_extract(__lowerCamelCase    ,						return_tensors="np"							).input_values
            for enc_seq_a, enc_seq_a in zip(__lowerCamelCase    ,						__lowerCamelCase							):
                 self.assertTrue(np.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-3							)							)




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							["longest", "max_length", "do_not_pad"]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[None, 1_6_0_0, None]
            for max_length, padding in zip(__lowerCamelCase    ,						__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							feat_extract(__lowerCamelCase    ,						padding=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						return_tensors="np"							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:8_0_0]							)
                 self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6							)
                 self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]							)
                 self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6							)
                 self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            _SCREAMING_SNAKE_CASE       :	str						    =							range(8_0_0    ,						1_4_0_0    ,						2_0_0							)
            _SCREAMING_SNAKE_CASE       :	int						    =							[floats_list((1, x)							)[0] for x in lengths]

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							["longest", "max_length", "do_not_pad"]
            _SCREAMING_SNAKE_CASE       :	str						    =							[None, 1_6_0_0, None]

            for max_length, padding in zip(__lowerCamelCase    ,						__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							feat_extract(__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						padding=__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:8_0_0]							)
                 self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]							)
                 self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							feat_extract(
                __lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=1_0_0_0    ,						padding="max_length"    ,						return_tensors="np"							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]							)
            self._check_zero_mean_unit_variance(input_values[1]							)
            self._check_zero_mean_unit_variance(input_values[2]							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]
            _SCREAMING_SNAKE_CASE       :	str						    =							feat_extract(
                __lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=1_0_0_0    ,						padding="longest"    ,						return_tensors="np"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]							)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]							)
            self._check_zero_mean_unit_variance(input_values[2]							)

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 1_0_0_0)							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							feat_extract(
                __lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=2_0_0_0    ,						padding="longest"    ,						return_tensors="np"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]							)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]							)
            self._check_zero_mean_unit_variance(input_values[2]							)

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 1_2_0_0)							)




       @require_torch
       def     UpperCamelCase_			(      self							)			->     List[Any]:
            import torch

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							np.random.rand(1_0_0							).astype(np.floataa							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							feature_extractor.pad([{"input_values": inputs}]    ,						return_tensors="np"							)
                 self.assertTrue(np_processed.input_values.dtype == np.floataa							)
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							feature_extractor.pad([{"input_values": inputs}]    ,						return_tensors="pt"							)
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)







       @slow
       @require_torch
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            # this test makes sure that models that are using
            # group norm don't have their feature extractor return the
            # attention_mask
            for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                 _SCREAMING_SNAKE_CASE       :	int						    =							WavaVecaConfig.from_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase							)

                 # only "layer" feature extraction norm should make use of
                 # attention_mask
                 self.assertEqual(feat_extract.return_attention_mask    ,						config.feat_extract_norm == "layer"							)






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