


import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


snake_case    :   str	    =					logging.get_logger(__name__)







class 		_snake_case  (				a_							):
 SCREAMING_SNAKE_CASE__				=	['''input_values''', '''attention_mask''']




 def __init__(		self ,  _lowerCamelCase = 1 ,  _lowerCamelCase = 1_6000 ,  _lowerCamelCase = 0.0 ,  _lowerCamelCase = False ,  _lowerCamelCase = 80 ,  _lowerCamelCase = 16 ,  _lowerCamelCase = 64 ,  _lowerCamelCase = "hann_window" ,  _lowerCamelCase = 1.0 ,  _lowerCamelCase = 80 ,  _lowerCamelCase = 7600 ,  _lowerCamelCase = 1e-10 ,  _lowerCamelCase = 2 ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
       super().__init__(feature_size=lowercase_ ,  sampling_rate=lowercase_ ,  padding_value=lowercase_ ,  **lowercase_   )
       a						:str										=	do_normalize
       a						:Union[str, Any]										=	return_attention_mask

       a						:int										=	num_mel_bins
       a						:List[Any]										=	hop_length
       a						:Dict										=	win_length
       a						:Union[str, Any]										=	win_function
       a						:Any										=	frame_signal_scale
       a						:Union[str, Any]										=	fmin
       a						:Any										=	fmax
       a						:Optional[int]										=	mel_floor
       a						:Any										=	reduction_factor

       a						:Dict										=	win_length * sampling_rate // 1000
       a						:int										=	hop_length * sampling_rate // 1000
       a						:Dict										=	optimal_fft_length(self.sample_size   )
       a						:Tuple										=	(self.n_fft // 2) + 1

       a						:Any										=	window_function(window_length=self.sample_size ,  name=self.win_function ,  periodic=lowercase_   )

       a						:Union[str, Any]										=	mel_filter_bank(
           num_frequency_bins=self.n_freqs ,  num_mel_filters=self.num_mel_bins ,  min_frequency=self.fmin ,  max_frequency=self.fmax ,  sampling_rate=self.sampling_rate ,  norm='''slaney''' ,  mel_scale='''slaney''' ,  )

       if frame_signal_scale != 1.0:
             warnings.warn(
                 '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers''' ,  lowercase_ ,  )
       if reduction_factor != 2.0:
             warnings.warn(
                 '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers''' ,  lowercase_ ,  )




 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 	SCREAMING_SNAKE_CASE__    (		_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = 0.0   ):

       if attention_mask is not None:
             a						:Dict										=	np.array(lowercase_ ,  np.intaa   )
             a						:Dict										=	[]

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

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

       return normed_input_values




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  ):
       a						:Optional[int]										=	spectrogram(
           lowercase_ ,  window=self.window ,  frame_length=self.sample_size ,  hop_length=self.sample_stride ,  fft_length=self.n_fft ,  mel_filters=self.mel_filters ,  mel_floor=self.mel_floor ,  log_mel='''log10''' ,  )
       return log_mel_spec.T




 def __call__(		self ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):

       if audio is None and audio_target is None:
             raise ValueError('''You must provide either `audio` or `audio_target` values.'''   )

       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 audio is not None:
             a						:Optional[int]										=	self._process_audio(
                 lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  **lowercase_ ,  )
       else:
             a						:Optional[int]										=	None

       if audio_target is not None:
             a						:Tuple										=	self._process_audio(
                 lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  **lowercase_ ,  )

             if inputs is None:
                   return inputs_target
             else:
                   a						:List[Any]										=	inputs_target['''input_values''']
                   a						:Tuple										=	inputs_target.get('''attention_mask'''   )
                   if decoder_attention_mask is not None:
                         a						:List[str]										=	decoder_attention_mask

       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:Optional[int]										=	isinstance(lowercase_ ,  np.ndarray   ) and len(speech.shape   ) > 1
       if is_batched_numpy and len(speech.shape   ) > 2:
             raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''   )
       a						:List[Any]										=	is_batched_numpy or (
           isinstance(lowercase_ ,  (list, tuple)   ) and (isinstance(speech[0] ,  (np.ndarray, tuple, list)   ))
       )

       if is_batched:
             a						:Any										=	[np.asarray(lowercase_ ,  dtype=np.floataa   ) for speech in speech]
       elif not is_batched and not isinstance(lowercase_ ,  np.ndarray   ):
             a						:Any										=	np.asarray(lowercase_ ,  dtype=np.floataa   )
       elif isinstance(lowercase_ ,  np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
             a						:Optional[Any]										=	speech.astype(np.floataa   )

       # always return batch
       if not is_batched:
             a						:Union[str, Any]										=	[speech]

       # needed to make pad() work on spectrogram inputs
       a						:Optional[int]										=	self.feature_size

       # convert into correct format for padding
       if is_target:
             a						:Any										=	[self._extract_mel_features(lowercase_   ) for waveform in speech]
             a						:Union[str, Any]										=	BatchFeature({'''input_values''': features}   )
             a						:str										=	self.num_mel_bins
       else:
             a						:Dict										=	BatchFeature({'''input_values''': speech}   )

       a						:Optional[int]										=	self.pad(
           lowercase_ ,  padding=lowercase_ ,  max_length=lowercase_ ,  truncation=lowercase_ ,  pad_to_multiple_of=lowercase_ ,  return_attention_mask=lowercase_ ,  **lowercase_ ,  )

       a						:int										=	feature_size_hack

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

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

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

       return padded_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	super().to_dict()

       # Don't serialize these as they are derived from the other properties.
       a						:Tuple										=	['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
       for name in names:
             if name in output:
                   del output[name]

       return output




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	MgpstrTokenizer
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	{}
 SCREAMING_SNAKE_CASE__				=	False




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

       # fmt: off
       a						:int										=	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       a						:List[str]										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )

       a						:Any										=	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['''vocab_file''']   )
       with open(self.vocab_file ,  '''w''' ,  encoding='''utf-8'''   ) as fp:
             fp.write(json.dumps(_lowerCamelCase   ) + '''\n'''   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       return MgpstrTokenizer.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	'''tester'''
       a						:Union[str, Any]										=	'''tester'''
       return input_text, output_text




 @unittest.skip('''MGP-STR always lower cases letters.'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.get_tokenizers(do_lower_case=_lowerCamelCase   )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   a						:Any										=	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}   )
                   a						:str										=	tokenizer.encode([special_token] ,  add_special_tokens=_lowerCamelCase   )
                   self.assertEqual(len(_lowerCamelCase   ) ,  1   )

                   a						:Tuple										=	tokenizer.decode(_lowerCamelCase ,  skip_special_tokens=_lowerCamelCase   )
                   self.assertTrue(special_token not in decoded   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   a							,    a						:Tuple										=	self.get_input_output_texts(_lowerCamelCase   )

                   a						:Tuple										=	tokenizer.tokenize(_lowerCamelCase   )
                   a						:int										=	tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
                   a						:Optional[int]										=	tokenizer.encode(_lowerCamelCase ,  add_special_tokens=_lowerCamelCase   )
                   self.assertListEqual(_lowerCamelCase ,  _lowerCamelCase   )

                   a						:Any										=	tokenizer.convert_ids_to_tokens(_lowerCamelCase   )
                   self.assertNotEqual(len(_lowerCamelCase   ) ,  0   )
                   a						:str										=	tokenizer.decode(_lowerCamelCase   )
                   self.assertIsInstance(_lowerCamelCase ,  _lowerCamelCase   )

                   self.assertEqual(text_a.replace(''' ''' ,  ''''''   ) ,  _lowerCamelCase   )




 @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass





