





from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowercase       :Dict       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["input_values", "padding_mask"]

							def __init__(      self				:     Optional[Any]							,	a				:     int = 1							,	a				:     int = 2_40_00							,	a				:     float = 0.0							,	a				:     float = None							,	a				:     float = None							,	**a				:     str							,	)	->Optional[Any]:
													super().__init__(feature_size=a							,	sampling_rate=a							,	padding_value=a							,	**a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					chunk_length_s
													SCREAMING_SNAKE_CASE__     :				Dict   =					overlap

							@property
							def      A_  (      self				:     int    )	->Optional[int]:
													if self.chunk_length_s is None:
																			return None
													else:
																			return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def      A_  (      self				:     Tuple    )	->Optional[int]:
													if self.chunk_length_s is None or self.overlap is None:
																			return None
													else:
																			return max(1							,	int((1.0 - self.overlap) * self.chunk_length    )    )






							def __call__(      self				:     Tuple							,	a				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,	a				:     Optional[Union[bool, str, PaddingStrategy]] = None							,	a				:     Optional[bool] = False							,	a				:     Optional[int] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Optional[int] = None							,	)	->BatchFeature:
													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																									    f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
																									    f""" {self.sampling_rate} and not {sampling_rate}."""    )
													else:
																			logger.warning(
																			    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."    )

													if padding and truncation:
																			raise ValueError("Both padding and truncation were set. Make sure you only set one."    )
													elif padding is None:
																			# by default let's pad the inputs
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					True

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					bool(
													    isinstance(a							,	(list, tuple)    ) and (isinstance(raw_audio[0]							,	(np.ndarray, tuple, list)    ))    )

													if is_batched:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[np.asarray(a							,	dtype=np.floataa    ).T for audio in raw_audio]
													elif not is_batched and not isinstance(a							,	np.ndarray    ):
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					np.asarray(a							,	dtype=np.floataa    )
													elif isinstance(a							,	np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					raw_audio.astype(np.floataa    )

													# always return batch
													if not is_batched:
																			SCREAMING_SNAKE_CASE__     :				str   =					[np.asarray(a    ).T]

													# verify inputs are valid
													for idx, example in enumerate(a    ):
																			if example.ndim > 2:
																									raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""    )
																			if self.feature_size == 1 and example.ndim != 1:
																									raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""    )
																			if self.feature_size == 2 and example.shape[-1] != 2:
																									raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""    )

													SCREAMING_SNAKE_CASE__     :				str   =					None
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					BatchFeature({"input_values": raw_audio}    )
													if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																			if truncation:
																									SCREAMING_SNAKE_CASE__     :				Any   =					min(array.shape[0] for array in raw_audio    )
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					int(np.floor(max_length / self.chunk_stride    )    )
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(nb_step - 1) * self.chunk_stride + self.chunk_length
																			elif padding:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max(array.shape[0] for array in raw_audio    )
																									SCREAMING_SNAKE_CASE__     :				Dict   =					int(np.ceil(max_length / self.chunk_stride    )    )
																									SCREAMING_SNAKE_CASE__     :				str   =					(nb_step - 1) * self.chunk_stride + self.chunk_length
																									SCREAMING_SNAKE_CASE__     :				Any   =					"max_length"
																			else:
																									SCREAMING_SNAKE_CASE__     :				Any   =					input_values

        # normal padding on batch
													if padded_inputs is None:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.pad(
																			    a							,	max_length=a							,	truncation=a							,	padding=a							,	return_attention_mask=a							,	)
																			if padding:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					padded_inputs.pop("attention_mask"    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[]
													for example in padded_inputs.pop("input_values"    ):
																			if self.feature_size == 1:
																									SCREAMING_SNAKE_CASE__     :				int   =					example[..., None]
																			input_values.append(example.T    )

													SCREAMING_SNAKE_CASE__     :				str   =					input_values
													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__     :				str   =					padded_inputs.convert_to_tensors(a    )

													return padded_inputs






def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


if __name__ == "__main__":
				print(f"{solution() = }")