'''simple docstring'''





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


lowerCAmelCase_	:		str			=     logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							['''input_values''', '''padding_mask''']





				def __init__(						self					:						Any       ,		lowercase__					:						int = 1       ,		lowercase__					:						int = 24_000       ,		lowercase__					:						float = 0.0       ,		lowercase__					:						float = None       ,		lowercase__					:						float = None       ,		**lowercase__					:						Optional[Any]       ,		)							->Dict:


										'''simple docstring'''




										super().__init__(feature_size=lowercase__       ,		sampling_rate=lowercase__       ,		padding_value=lowercase__       ,		**lowercase__					)
										_UpperCamelCase			:			str   			=						chunk_length_s
										_UpperCamelCase			:			Union[str, Any]   			=						overlap





				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->Optional[int]:


										'''simple docstring'''




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate					)





				@property
				def  snake_case__				(						self					:						str					)							->Optional[int]:


										'''simple docstring'''




										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					:						Dict       ,		lowercase__					:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,		lowercase__					:						Optional[Union[bool, str, PaddingStrategy]] = None       ,		lowercase__					:						Optional[bool] = False       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						Optional[Union[str, TensorType]] = None       ,		lowercase__					:						Optional[int] = None       ,		)							->BatchFeature:


										'''simple docstring'''




										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
																_UpperCamelCase			:			str   			=						True

										_UpperCamelCase			:			Tuple   			=						bool(
										    isinstance(lowercase__       ,		(list, tuple)					) and (isinstance(raw_audio[0]       ,		(np.ndarray, tuple, list)					))					)

										if is_batched:
																_UpperCamelCase			:			List[str]   			=						[np.asarray(lowercase__       ,		dtype=np.floataa					).T for audio in raw_audio]
										elif not is_batched and not isinstance(lowercase__       ,		np.ndarray					):
																_UpperCamelCase			:			str   			=						np.asarray(lowercase__       ,		dtype=np.floataa					)
										elif isinstance(lowercase__       ,		np.ndarray					) and raw_audio.dtype is np.dtype(np.floataa					):
																_UpperCamelCase			:			List[Any]   			=						raw_audio.astype(np.floataa					)

										# always return batch
										if not is_batched:
																_UpperCamelCase			:			Optional[int]   			=						[np.asarray(lowercase__					).T]

										# verify inputs are valid
										for idx, example in enumerate(lowercase__					):
																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'''					)

										_UpperCamelCase			:			str   			=						None
										_UpperCamelCase			:			Dict   			=						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:
																						_UpperCamelCase			:			int   			=						min(array.shape[0] for array in raw_audio					)
																						_UpperCamelCase			:			Optional[int]   			=						int(np.floor(max_length / self.chunk_stride					)					)
																						_UpperCamelCase			:			Optional[int]   			=						(nb_step - 1) * self.chunk_stride + self.chunk_length
																elif padding:
																						_UpperCamelCase			:			List[str]   			=						max(array.shape[0] for array in raw_audio					)
																						_UpperCamelCase			:			Optional[Any]   			=						int(np.ceil(max_length / self.chunk_stride					)					)
																						_UpperCamelCase			:			List[Any]   			=						(nb_step - 1) * self.chunk_stride + self.chunk_length
																						_UpperCamelCase			:			Dict   			=						"max_length"
																else:
																						_UpperCamelCase			:			List[Any]   			=						input_values

        # normal padding on batch
										if padded_inputs is None:
																_UpperCamelCase			:			int   			=						self.pad(
																    lowercase__       ,		max_length=lowercase__       ,		truncation=lowercase__       ,		padding=lowercase__       ,		return_attention_mask=lowercase__       ,		)
																if padding:
																						_UpperCamelCase			:			int   			=						padded_inputs.pop("attention_mask"					)

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

										_UpperCamelCase			:			Optional[int]   			=						input_values
										if return_tensors is not None:
																_UpperCamelCase			:			int   			=						padded_inputs.convert_to_tensors(lowercase__					)

										return padded_inputs



'''simple docstring'''





from __future__ import annotations

from cmath import sqrt
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[complex, complex]:




						'''simple docstring'''

						if a == 0:
												raise ValueError("Coefficient 'a' must not be zero."					)
						_UpperCamelCase			:			List[str]   			=						b * b - 4 * a * c

						_UpperCamelCase			:			Dict   			=						(-b + sqrt(UpperCAmelCase					)) / (2 * a)
						_UpperCamelCase			:			Optional[int]   			=						(-b - sqrt(UpperCAmelCase					)) / (2 * a)

						return (
						    root_a.real if not root_a.imag else root_a,
						    root_a.real if not root_a.imag else root_a,
						)
def 						__A							(     )     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						quadratic_roots(a=5     ,b=6     ,c=1					)
						print(f'''The solutions are: {solutiona} and {solutiona}'''					)


if __name__ == "__main__":
			main()


