
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


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Any 	=						["input_values", "attention_mask"]





     def __init__(self :       List[Any]							,       _A :       List[str]=1							,       _A :       int=1_6_0_0_0							,       _A :       Union[str, Any]=0.0							,       _A :       Dict=False							,       _A :       str=True							,       **_A :       Optional[int]							,       )		->		Union[str, Any]:
         super().__init__(feature_size=_A							,       sampling_rate=_A							,       padding_value=_A							,       **_A  )
         snake_case						=   return_attention_mask
         snake_case						=   do_normalize





     @staticmethod
     def 						UpperCAmelCase(_A :       List[np.ndarray]							,       _A :       List[np.ndarray]							,       _A :       float = 0.0  )		->		List[np.ndarray]:
         if attention_mask is not None:
             snake_case						=   np.array(_A							,       np.intaa  )
             snake_case						=   []

             for vector, length in zip(_A							,       attention_mask.sum(-1  )  ):
                 snake_case						=   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7  )
                 if length < normed_slice.shape[0]:
                     snake_case						=   padding_value

                 normed_input_values.append(_A  )
         else:
             snake_case						=   [(x - x.mean()) / np.sqrt(x.var() + 1E-7  ) for x in input_values]

         return normed_input_values





     def __call__(self :       str							,       _A :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,       _A :       Union[bool, str, PaddingStrategy] = False							,       _A :       Optional[int] = None							,       _A :       bool = False							,       _A :       Optional[int] = None							,       _A :       Optional[bool] = None							,       _A :       Optional[Union[str, TensorType]] = None							,       _A :       Optional[int] = None							,       **_A :       str							,       )		->		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 `raw_speech` 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."  )

         snake_case						=   isinstance(_A							,       np.ndarray  ) and len(raw_speech.shape  ) > 1
         if is_batched_numpy and len(raw_speech.shape  ) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
         snake_case						=   is_batched_numpy or (
             isinstance(_A							,       (list, tuple)  ) and (isinstance(raw_speech[0]							,       (np.ndarray, tuple, list)  ))
         )

         # always return batch
         if not is_batched:
             snake_case						=   [raw_speech]

         # convert into correct format for padding
         snake_case						=   BatchFeature({"input_values": raw_speech}  )

         snake_case						=   self.pad(
             _A							,       padding=_A							,       max_length=_A							,       truncation=_A							,       pad_to_multiple_of=_A							,       return_attention_mask=_A							,       )

         # convert input values to correct format
         snake_case						=   padded_inputs["input_values"]
         if not isinstance(input_values[0]							,       np.ndarray  ):
             snake_case						=   [np.asarray(_A							,       dtype=np.floataa  ) for array in input_values]
         elif (
             not isinstance(_A							,       np.ndarray  )
             and isinstance(input_values[0]							,       np.ndarray  )
             and input_values[0].dtype is np.dtype(np.floataa  )
         ):
             snake_case						=   [array.astype(np.floataa  ) for array in input_values]
         elif isinstance(_A							,       np.ndarray  ) and input_values.dtype is np.dtype(np.floataa  ):
             snake_case						=   input_values.astype(np.floataa  )

         # convert attention_mask to correct format
         snake_case						=   padded_inputs.get("attention_mask"  )
         if attention_mask is not None:
             snake_case						=   [np.asarray(_A							,       dtype=np.intaa  ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case						=   (
                 attention_mask
                 if self._get_padding_strategies(_A							,       max_length=_A  ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case						=   self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]							,       attention_mask=_A							,       padding_value=self.padding_value  )

         if return_tensors is not None:
             snake_case						=   padded_inputs.convert_to_tensors(_A  )

         return padded_inputs



_A       =					{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_A       =					{1_45: 1_45}

_A       =					{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    if n in CACHE_SUM_DIGIT_FACTORIALS:
        return CACHE_SUM_DIGIT_FACTORIALS[n]
    snake_case						=   sum(DIGIT_FACTORIALS[let] for let in str(A__						)						)
    snake_case						=   ret
    return ret



def 		lowercase_     (  A__					,						A__ = None						)				->				int:
    """simple docstring"""

    snake_case						=   previous or set()
    if n in CHAIN_LENGTH_CACHE:
        return CHAIN_LENGTH_CACHE[n]
    snake_case						=   sum_digit_factorials(A__						)
    if next_number in previous:
        snake_case						=   0
        return 0
    else:
        previous.add(A__						)
        snake_case						=   1 + chain_length(A__					,						A__						)
        snake_case						=   ret
        return ret



def 		lowercase_     (  A__ = 60					,						A__ = 100_0000						)				->				int:
    """simple docstring"""

    return sum(1 for i in range(1					,						A__						) if chain_length(A__						) == num_terms						)


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

