



"""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


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _UpperCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     ['input_values', 'padding_mask']


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = 1						,	SCREAMING_SNAKE_CASE__ = 24000						,	SCREAMING_SNAKE_CASE__ = 0.0						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	str:
				super().__init__(feature_size=SCREAMING_SNAKE_CASE_						,	sampling_rate=SCREAMING_SNAKE_CASE_						,	padding_value=SCREAMING_SNAKE_CASE_						,	**SCREAMING_SNAKE_CASE_		)
				A__											=					chunk_length_s
				A__											=					overlap


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


			@property
			def   snake_case__	(      self		)      ->	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						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 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
					A__											=					True

				A__											=					bool(
				    isinstance(SCREAMING_SNAKE_CASE_						,	(list, tuple)		) and (isinstance(raw_audio[0]						,	(np.ndarray, tuple, list)		))		)

				if is_batched:
					A__											=					[np.asarray(SCREAMING_SNAKE_CASE_						,	dtype=np.floataa		).T for audio in raw_audio]
				elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_						,	np.ndarray		):
					A__											=					np.asarray(SCREAMING_SNAKE_CASE_						,	dtype=np.floataa		)
				elif isinstance(SCREAMING_SNAKE_CASE_						,	np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
					A__											=					raw_audio.astype(np.floataa		)

				# always return batch
				if not is_batched:
					A__											=					[np.asarray(SCREAMING_SNAKE_CASE_		).T]

				# verify inputs are valid
				for idx, example in enumerate(SCREAMING_SNAKE_CASE_		):
					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"""		)

				A__											=					None
				A__											=					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:
						A__											=					min(array.shape[0] for array in raw_audio		)
						A__											=					int(np.floor(max_length / self.chunk_stride		)		)
						A__											=					(nb_step - 1) * self.chunk_stride + self.chunk_length
					elif padding:
						A__											=					max(array.shape[0] for array in raw_audio		)
						A__											=					int(np.ceil(max_length / self.chunk_stride		)		)
						A__											=					(nb_step - 1) * self.chunk_stride + self.chunk_length
						A__											=					'max_length'
					else:
						A__											=					input_values

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

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

				A__											=					input_values
				if return_tensors is not None:
					A__											=					padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_		)

				return padded_inputs








'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


if __name__ == "__main__":
      print(solution())






