
"""simple docstring"""





import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput


@dataclass
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		float
  SCREAMING_SNAKE_CASE__     :		Optional[float]    	=						None
  SCREAMING_SNAKE_CASE__     :		Optional[float]    	=						None




class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       ModelOutputTest(a=30    )
     self.assertEqual(x.a							,  30    )
     self.assertIsNone(x.b    )
     self.assertIsNone(x.c    )
     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   Dict     	=       x.d







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ModelOutputTest(a=30							,  b=10    )
     self.assertEqual(x[0]							,  30    )
     self.assertEqual(x[1]							,  10    )
     self.assertEqual(x[:2]							,  (30, 10)    )
     self.assertEqual(x[:]							,  (30, 10)    )

     UpperCAmelCase_   :   Any     	=       ModelOutputTest(a=30							,  c=10    )
     self.assertEqual(x[0]							,  30    )
     self.assertEqual(x[1]							,  10    )
     self.assertEqual(x[:2]							,  (30, 10)    )
     self.assertEqual(x[:]							,  (30, 10)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       ModelOutputTest(a=30							,  b=10    )
     self.assertEqual(x["a"]							,  30    )
     self.assertEqual(x["b"]							,  10    )
     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   List[str]     	=       x["c"]

     UpperCAmelCase_   :   Dict     	=       ModelOutputTest(a=30							,  c=10    )
     self.assertEqual(x["a"]							,  30    )
     self.assertEqual(x["c"]							,  10    )
     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   int     	=       x["b"]







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       ModelOutputTest(a=30    )
     self.assertEqual(list(x.keys()    )							,  ["a"]    )
     self.assertEqual(list(x.values()    )							,  [30]    )
     self.assertEqual(list(x.items()    )							,  [("a", 30)]    )
     self.assertEqual(list(lowercase_    )							,  ["a"]    )

     UpperCAmelCase_   :   List[Any]     	=       ModelOutputTest(a=30							,  b=10    )
     self.assertEqual(list(x.keys()    )							,  ["a", "b"]    )
     self.assertEqual(list(x.values()    )							,  [30, 10]    )
     self.assertEqual(list(x.items()    )							,  [("a", 30), ("b", 10)]    )
     self.assertEqual(list(lowercase_    )							,  ["a", "b"]    )

     UpperCAmelCase_   :   Tuple     	=       ModelOutputTest(a=30							,  c=10    )
     self.assertEqual(list(x.keys()    )							,  ["a", "c"]    )
     self.assertEqual(list(x.values()    )							,  [30, 10]    )
     self.assertEqual(list(x.items()    )							,  [("a", 30), ("c", 10)]    )
     self.assertEqual(list(lowercase_    )							,  ["a", "c"]    )

     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       x.update({"d": 20}    )
     with self.assertRaises(lowercase_    ):
        del x["a"]
     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   List[Any]     	=       x.pop("a"    )
     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       x.setdefault("d"							,  32    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ModelOutputTest(a=30    )
     UpperCAmelCase_   :   List[str]     	=       10
     self.assertEqual(x.a							,  10    )
     self.assertEqual(x["a"]							,  10    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       ModelOutputTest(a=30    )
     UpperCAmelCase_   :   str     	=       10
     self.assertEqual(x.a							,  10    )
     self.assertEqual(x["a"]							,  10    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       ModelOutputTest({"a": 30, "b": 10}    )
     self.assertEqual(list(x.keys()    )							,  ["a", "b"]    )
     self.assertEqual(x.a							,  30    )
     self.assertEqual(x.b							,  10    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       ModelOutputTest([("a", 30), ("b", 10)]    )
     self.assertEqual(list(x.keys()    )							,  ["a", "b"]    )
     self.assertEqual(x.a							,  30    )
     self.assertEqual(x.b							,  10    )

     with self.assertRaises(lowercase_    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       ModelOutputTest([("a", 30), (10, 10)]    )

     UpperCAmelCase_   :   int     	=       ModelOutputTest(a=(30, 30)    )
     self.assertEqual(list(x.keys()    )							,  ["a"]    )
     self.assertEqual(x.a							,  (30, 30)    )









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


__lowercase							:Optional[int]							=	logging.get_logger(__name__)



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["input_values", "attention_mask"]

							def __init__(   self					:  List[str]				,						a					:  Dict=1				,						a					:  Tuple=1_60_00				,						a					:  Union[str, Any]=0.0				,						a					:  Dict=False				,						a					:  List[Any]=True				,						**a					:  Any				,						)						->Optional[int]:
													super().__init__(feature_size=a				,						sampling_rate=a				,						padding_value=a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					int =       return_attention_mask
													SCREAMING_SNAKE_CASE__  :					str =       do_normalize

							@staticmethod
							def   A_	(   a					:  List[np.ndarray]				,						a					:  List[np.ndarray]				,						a					:  float = 0.0     )						->List[np.ndarray]:
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       np.array(a				,						np.intaa     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []

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

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

													return normed_input_values






							def __call__(   self					:  Union[str, Any]				,						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					:  Optional[Any]				,						)						->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."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       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}"""     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       is_batched_numpy or (
													    isinstance(a				,						(list, tuple)     ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)     ))
													)

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

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

													SCREAMING_SNAKE_CASE__  :					Tuple =       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
													SCREAMING_SNAKE_CASE__  :					Any =       padded_inputs["input_values"]
													if not isinstance(input_values[0]				,						np.ndarray     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [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     )
													):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [array.astype(np.floataa     ) for array in input_values]
													elif isinstance(a				,						np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       input_values.astype(np.floataa     )

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

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

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padded_inputs.convert_to_tensors(a     )

													return padded_inputs