






"""simple docstring"""



from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):


							def __init__(     self :  List[str]     ,      snake_case :  Callable     ,      snake_case :  Optional[Features] = None     ,      snake_case :  str = None     ,      snake_case :  bool = False     ,      snake_case :  bool = False     ,      snake_case :  Optional[dict] = None     ,      snake_case :  Optional[int] = None     ,      **snake_case :  Optional[int]     ,      ):
													'''simple docstring'''





													super().__init__(
													    features=snake_case     ,      cache_dir=snake_case     ,      keep_in_memory=snake_case     ,      streaming=snake_case     ,      num_proc=snake_case     ,      **snake_case     ,      )
													A__  :					List[Any]											=					Generator(
													    cache_dir=snake_case     ,      features=snake_case     ,      generator=snake_case     ,      gen_kwargs=snake_case     ,      **snake_case     ,      )


							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





													if self.streaming:
																			A__  :					List[str]											=					self.builder.as_streaming_dataset(split="""train"""							)
													# Build regular (map-style) dataset
													else:
																			A__  :					Tuple											=					None
																			A__  :					List[str]											=					None
																			A__  :					List[str]											=					None
																			A__  :					Any											=					None

																			self.builder.download_and_prepare(
																			    download_config=snake_case     ,      download_mode=snake_case     ,      verification_mode=snake_case     ,      base_path=snake_case     ,      num_proc=self.num_proc     ,      )
																			A__  :					Optional[Any]											=					self.builder.as_dataset(
																			    split="""train"""     ,      verification_mode=snake_case     ,      in_memory=self.keep_in_memory							)
													return dataset














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  Dict     ,      snake_case :  int							):
													'''simple docstring'''





													A__  :					List[Any]											=					order

													# a_{0} ... a_{k}
													A__  :					List[Any]											=					[1.0] + [0.0] * order
													# b_{0} ... b_{k}
													A__  :					str											=					[1.0] + [0.0] * order

													# x[n-1] ... x[n-k]
													A__  :					Union[str, Any]											=					[0.0] * self.order
													# y[n-1] ... y[n-k]
													A__  :					List[str]											=					[0.0] * self.order


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  list[float]     ,      snake_case :  list[float]							):
													'''simple docstring'''





													if len(snake_case							) < self.order:
																			A__  :					Any											=					[1.0, *a_coeffs]

													if len(snake_case							) != self.order + 1:
																			A__  :					str											=					(
																			    F'Expected a_coeffs to have {self.order + 1} elements '
																			    F'for {self.order}-order filter, got {len(snake_case							)}'
																			)
																			raise ValueError(snake_case							)

													if len(snake_case							) != self.order + 1:
																			A__  :					Union[str, Any]											=					(
																			    F'Expected b_coeffs to have {self.order + 1} elements '
																			    F'for {self.order}-order filter, got {len(snake_case							)}'
																			)
																			raise ValueError(snake_case							)

													A__  :					Dict											=					a_coeffs
													A__  :					Any											=					b_coeffs


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  float							):
													'''simple docstring'''





													A__  :					str											=					0.0

													# Start at index 1 and do index 0 at the end.
													for i in range(1     ,      self.order + 1							):
																			result += (
																			    self.b_coeffs[i] * self.input_history[i - 1]
																			    - self.a_coeffs[i] * self.output_history[i - 1]
																			)

													A__  :					Dict											=					(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

													A__  :					Tuple											=					self.input_history[:-1]
													A__  :					int											=					self.output_history[:-1]

													A__  :					Dict											=					sample
													A__  :					Tuple											=					result

													return result






