





"""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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   ["""input_features"""]






       def __init__(       self       ,					__UpperCAmelCase=8_0       ,					__UpperCAmelCase=1_6_0_0_0       ,					__UpperCAmelCase=1_6_0       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=4_0_0       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=False       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             feature_size=__UpperCAmelCase       ,					sampling_rate=__UpperCAmelCase       ,					padding_value=__UpperCAmelCase       ,					return_attention_mask=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)
         lowerCAmelCase__     :Any											=				n_fft
         lowerCAmelCase__     :List[Any]											=				hop_length
         lowerCAmelCase__     :Tuple											=				chunk_length
         lowerCAmelCase__     :Tuple											=				chunk_length * sampling_rate
         lowerCAmelCase__     :Tuple											=				self.n_samples // hop_length
         lowerCAmelCase__     :List[Any]											=				sampling_rate
         lowerCAmelCase__     :Optional[int]											=				mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,					num_mel_filters=__UpperCAmelCase       ,					min_frequency=0.0       ,					max_frequency=80_00.0       ,					sampling_rate=__UpperCAmelCase       ,					norm='slaney'       ,					mel_scale='slaney'       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				spectrogram(
             __UpperCAmelCase       ,					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'       ,					)
         lowerCAmelCase__     :Dict											=				log_spec[:, :-1]
         lowerCAmelCase__     :List[Any]											=				np.maximum(__UpperCAmelCase       ,					log_spec.max() - 8.0 )
         lowerCAmelCase__     :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 	snake_case		(       __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = 0.0 ):



         '''simple docstring'''

         if attention_mask is not None:
           lowerCAmelCase__     :Tuple											=				np.array(__UpperCAmelCase       ,					np.intaa )
           lowerCAmelCase__     :Any											=				[]

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

             normed_input_values.append(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :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 = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "max_length"       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         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.' )

         lowerCAmelCase__     :Optional[int]											=				isinstance(__UpperCAmelCase       ,					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}" )
         lowerCAmelCase__     :Optional[int]											=				is_batched_numpy or (
             isinstance(__UpperCAmelCase       ,					(list, tuple) ) and (isinstance(raw_speech[0]       ,					(np.ndarray, tuple, list) ))
         )

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

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

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

         # convert into correct format for padding

         lowerCAmelCase__     :str											=				self.pad(
             __UpperCAmelCase       ,					padding=__UpperCAmelCase       ,					max_length=max_length if max_length else self.n_samples       ,					truncation=__UpperCAmelCase       ,					pad_to_multiple_of=__UpperCAmelCase       ,					return_attention_mask=return_attention_mask or do_normalize       ,					)

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

         # make sure list is in array format
         lowerCAmelCase__     :Tuple											=				padded_inputs.get('input_features' ).transpose(2       ,					0       ,					1 )

         lowerCAmelCase__     :Optional[Any]											=				[self._np_extract_fbank_features(__UpperCAmelCase ) for waveform in input_features[0]]

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

         if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowerCAmelCase__     :Union[str, Any]											=				padded_inputs['attention_mask'][:, :: self.hop_length]

         if return_tensors is not None:
           lowerCAmelCase__     :List[Any]											=				padded_inputs.convert_to_tensors(__UpperCAmelCase )

         return padded_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				copy.deepcopy(self.__dict__ )
         lowerCAmelCase__     :List[Any]											=				self.__class__.__name__
         if "mel_filters" in output:
           del output["mel_filters"]
         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 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[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      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_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(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

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

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


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

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


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



