





'''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      :   List[str]								=     logging.get_logger(__name__)



class  lowerCamelCase__				(						lowerCAmelCase_						):


	"""simple docstring"""





	__magic_name__							   =		["input_values", "attention_mask"]




	def __init__(					self   ,     UpperCAmelCase__=1   ,     UpperCAmelCase__=1_6_0_0_0   ,     UpperCAmelCase__=0.0   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=True   ,     **UpperCAmelCase__   ,     )       ->				Dict:
					super().__init__(feature_size=lowerCamelCase__   ,     sampling_rate=lowerCamelCase__   ,     padding_value=lowerCamelCase__   ,     **lowerCamelCase__			)
					_A :				str				=  return_attention_mask
					_A :				str				=  do_normalize




	@staticmethod
	def 				_lowerCamelCase				(					UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = 0.0			)       ->				List[np.ndarray]:


					if attention_mask is not None:
									_A :				List[str]				=  np.array(lowerCamelCase__   ,     np.intaa			)
									_A :				Optional[int]				=  []

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

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

					return normed_input_values




	def __call__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = False   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = False   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     **UpperCAmelCase__   ,     )       ->				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.'''			)

					_A :				Union[str, Any]				=  isinstance(lowerCamelCase__   ,     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}"""			)
					_A :				str				=  is_batched_numpy or (
					    isinstance(lowerCamelCase__   ,     (list, tuple)			) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list)			))
					)

					# always return batch
					if not is_batched:
									_A :				Optional[int]				=  [raw_speech]

					# convert into correct format for padding
					_A :				str				=  BatchFeature({'''input_values''': raw_speech}			)

					_A :				List[str]				=  self.pad(
					    lowerCamelCase__   ,     padding=lowerCamelCase__   ,     max_length=lowerCamelCase__   ,     truncation=lowerCamelCase__   ,     pad_to_multiple_of=lowerCamelCase__   ,     return_attention_mask=lowerCamelCase__   ,     )

					# convert input values to correct format
					_A :				Union[str, Any]				=  padded_inputs['''input_values''']
					if not isinstance(input_values[0]   ,     np.ndarray			):
									_A :				Tuple				=  [np.asarray(lowerCamelCase__   ,     dtype=np.floataa			) for array in input_values]
					elif (
					    not isinstance(lowerCamelCase__   ,     np.ndarray			)
					    and isinstance(input_values[0]   ,     np.ndarray			)
					    and input_values[0].dtype is np.dtype(np.floataa			)
					):
									_A :				List[Any]				=  [array.astype(np.floataa			) for array in input_values]
					elif isinstance(lowerCamelCase__   ,     np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
									_A :				Tuple				=  input_values.astype(np.floataa			)

					# convert attention_mask to correct format
					_A :				Optional[int]				=  padded_inputs.get('''attention_mask'''			)
					if attention_mask is not None:
									_A :				List[str]				=  [np.asarray(lowerCamelCase__   ,     dtype=np.intaa			) for array in attention_mask]

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

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

					return padded_inputs







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


