'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''input_features''']







     def __init__(		self     ,			_lowerCamelCase=8_0     ,			_lowerCamelCase=1_6_0_0_0     ,			_lowerCamelCase=1_6_0     ,			_lowerCamelCase=3_0     ,			_lowerCamelCase=4_0_0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=False     ,			**_lowerCamelCase     ,			):
         super().__init__(
             feature_size=_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			padding_value=_lowerCamelCase     ,			return_attention_mask=_lowerCamelCase     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Any		    =  n_fft
         __UpperCamelCase	:	Any		    =  hop_length
         __UpperCamelCase	:	Any		    =  chunk_length
         __UpperCamelCase	:	Union[str, Any]		    =  chunk_length * sampling_rate
         __UpperCamelCase	:	Optional[Any]		    =  self.n_samples // hop_length
         __UpperCamelCase	:	int		    =  sampling_rate
         __UpperCamelCase	:	Tuple		    =  mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2     ,			num_mel_filters=_lowerCamelCase     ,			min_frequency=0.0     ,			max_frequency=8_0_0_0.0     ,			sampling_rate=_lowerCamelCase     ,			norm='slaney'     ,			mel_scale='slaney'     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  spectrogram(
             _lowerCamelCase     ,			window_function(self.n_fft     ,			'hann'    )     ,			frame_length=self.n_fft     ,			hop_length=self.hop_length     ,			power=2.0     ,			mel_filters=self.mel_filters     ,			log_mel='log10'     ,			)
         __UpperCamelCase	:	List[str]		    =  log_spec[:, :-1]
         __UpperCamelCase	:	Dict		    =  np.maximum(_lowerCamelCase     ,			log_spec.max() - 8.0    )
         __UpperCamelCase	:	Optional[Any]		    =  (log_spec + 4.0) / 4.0
         return log_spec







     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def lowerCAmelCase     (		_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 0.0    ):
         if attention_mask is not None:
             __UpperCamelCase	:	Optional[int]		    =  np.array(_lowerCamelCase     ,			np.intaa    )
             __UpperCamelCase	:	Union[str, Any]		    =  []

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

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

         return normed_input_values







     def __call__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "max_length"     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                     f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                     f""" was sampled with {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.'    )

         __UpperCamelCase	:	Optional[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}"""    )
         __UpperCamelCase	:	Optional[Any]		    =  is_batched_numpy or (
             isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(raw_speech[0]     ,			(np.ndarray, tuple, list)    ))
         )

         if is_batched:
             __UpperCamelCase	:	Any		    =  [np.asarray([speech]     ,			dtype=np.floataa    ).T for speech in raw_speech]
         elif not is_batched and not isinstance(_lowerCamelCase     ,			np.ndarray    ):
             __UpperCamelCase	:	List[Any]		    =  np.asarray(_lowerCamelCase     ,			dtype=np.floataa    )
         elif isinstance(_lowerCamelCase     ,			np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             __UpperCamelCase	:	Union[str, Any]		    =  raw_speech.astype(np.floataa    )

         # always return batch
         if not is_batched:
             __UpperCamelCase	:	int		    =  [np.asarray([raw_speech]    ).T]

         __UpperCamelCase	:	List[str]		    =  BatchFeature({'input_features': raw_speech}    )

         # convert into correct format for padding

         __UpperCamelCase	:	Tuple		    =  self.pad(
             _lowerCamelCase     ,			padding=_lowerCamelCase     ,			max_length=max_length if max_length else self.n_samples     ,			truncation=_lowerCamelCase     ,			pad_to_multiple_of=_lowerCamelCase     ,			return_attention_mask=return_attention_mask or do_normalize     ,			)

         # zero-mean and unit-variance normalization
         if do_normalize:
             __UpperCamelCase	:	Union[str, Any]		    =  self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']     ,			attention_mask=padded_inputs['attention_mask']     ,			padding_value=self.padding_value     ,			)
             __UpperCamelCase	:	Union[str, Any]		    =  np.stack(padded_inputs['input_features']     ,			axis=0    )

         # make sure list is in array format
         __UpperCamelCase	:	Optional[Any]		    =  padded_inputs.get('input_features'    ).transpose(2     ,			0     ,			1    )

         __UpperCamelCase	:	Any		    =  [self._np_extract_fbank_features(_lowerCamelCase    ) for waveform in input_features[0]]

         if isinstance(input_features[0]     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  [np.asarray(_lowerCamelCase     ,			dtype=np.floataa    ) for feature in input_features]
         else:
             __UpperCamelCase	:	Any		    =  input_features

         if return_attention_mask:
             # rescale from sample (48000) to feature (3000)
             __UpperCamelCase	:	Optional[int]		    =  padded_inputs['attention_mask'][:, :: self.hop_length]

         if return_tensors is not None:
             __UpperCamelCase	:	str		    =  padded_inputs.convert_to_tensors(_lowerCamelCase    )

         return padded_inputs







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  copy.deepcopy(self.__dict__    )
         __UpperCamelCase	:	Any		    =  self.__class__.__name__
         if "mel_filters" in output:
             del output["mel_filters"]
         return output




'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



