"""simple docstring"""


from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


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






class    a__    (   _a	):
   snake_case_							=      ["input_features", "attention_mask"]


   def __init__(     self,				_UpperCAmelCase=80,				_UpperCAmelCase=1_6000,				_UpperCAmelCase=0.0,				_UpperCAmelCase=10,				_UpperCAmelCase=25,				_UpperCAmelCase="hamming_window",				_UpperCAmelCase=3_2768.0,				_UpperCAmelCase=0.97,				_UpperCAmelCase=1.0,				_UpperCAmelCase=True,				_UpperCAmelCase=True,				_UpperCAmelCase=False,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(feature_size=_UpperCAmelCase,				sampling_rate=_UpperCAmelCase,				padding_value=_UpperCAmelCase,				**_UpperCAmelCase  )

        lowercase__				 =							feature_size
        lowercase__				 =							sampling_rate
        lowercase__				 =							padding_value
        lowercase__				 =							hop_length
        lowercase__				 =							win_length
        lowercase__				 =							frame_signal_scale
        lowercase__				 =							preemphasis_coeff
        lowercase__				 =							mel_floor
        lowercase__				 =							normalize_means
        lowercase__				 =							normalize_vars
        lowercase__				 =							win_function
        lowercase__				 =							return_attention_mask

        lowercase__				 =							win_length * sampling_rate // 1000
        lowercase__				 =							hop_length * sampling_rate // 1000

        lowercase__				 =							optimal_fft_length(self.sample_size  )
        lowercase__				 =							(self.n_fft // 2) + 1


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if self.win_function == "hamming_window":
             lowercase__				 =							window_function(window_length=self.sample_size,				name=self.win_function,				periodic=_UpperCAmelCase  )
        else:
             lowercase__				 =							window_function(window_length=self.sample_size,				name=self.win_function  )

        lowercase__				 =							mel_filter_bank(
            num_frequency_bins=self.n_freqs,				num_mel_filters=self.feature_size,				min_frequency=0.0,				max_frequency=self.sampling_rate / 2.0,				sampling_rate=self.sampling_rate,				)

        lowercase__				 =							spectrogram(
            one_waveform * self.frame_signal_scale,				window=_UpperCAmelCase,				frame_length=self.sample_size,				hop_length=self.sample_stride,				fft_length=self.n_fft,				center=_UpperCAmelCase,				preemphasis=self.preemphasis_coeff,				mel_filters=_UpperCAmelCase,				mel_floor=self.mel_floor,				log_mel="log",				)
        return msfc_features.T


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        if self.normalize_means:
             lowercase__				 =							x[:input_length].mean(axis=0  )
             lowercase__				 =							np.subtract(_UpperCAmelCase,				_UpperCAmelCase  )
        if self.normalize_vars:
             lowercase__				 =							x[:input_length].std(axis=0  )
             lowercase__				 =							np.divide(_UpperCAmelCase,				_UpperCAmelCase  )

        if input_length < x.shape[0]:
             lowercase__				 =							padding_value

        # make sure array is in float32
        lowercase__				 =							x.astype(np.floataa  )

        return x


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        lowercase__				 =							attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [self._normalize_one(_UpperCAmelCase,				_UpperCAmelCase,				self.padding_value  ) for x, n in zip(_UpperCAmelCase,				_UpperCAmelCase  )]


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				_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} 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."  )

        lowercase__				 =							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}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )

        if is_batched:
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCAmelCase,				np.ndarray  ):
             lowercase__				 =							np.asarray(_UpperCAmelCase,				dtype=np.floataa  )
        elif isinstance(_UpperCAmelCase,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             lowercase__				 =							raw_speech.astype(np.floataa  )

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

        # extract fbank features
        lowercase__				 =							[self._extract_mfsc_features(_UpperCAmelCase  ) for one_waveform in raw_speech]

        # convert into correct format for padding
        lowercase__				 =							BatchFeature({"input_features": features}  )

        lowercase__				 =							self.pad(
            _UpperCAmelCase,				padding=_UpperCAmelCase,				max_length=_UpperCAmelCase,				truncation=_UpperCAmelCase,				pad_to_multiple_of=_UpperCAmelCase,				return_attention_mask=_UpperCAmelCase,				**_UpperCAmelCase,				)
        # make sure list is in array format
        lowercase__				 =							padded_inputs.get("input_features"  )
        if isinstance(input_features[0],				_UpperCAmelCase  ):
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for feature in input_features]

        lowercase__				 =							padded_inputs.get("attention_mask"  )
        if attention_mask is not None:
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.intaa  ) for array in attention_mask]

        if self.normalize_means or self.normalize_vars:
             lowercase__				 =							(
                 np.array(_UpperCAmelCase,				dtype=np.intaa  )
                 if self._get_padding_strategies(_UpperCAmelCase,				max_length=_UpperCAmelCase  ) is not PaddingStrategy.DO_NOT_PAD
                 and padding
                 else None
             )
             lowercase__				 =							self.normalize(
                 padded_inputs["input_features"],				attention_mask=_UpperCAmelCase  )

        if return_tensors is not None:
             lowercase__				 =							padded_inputs.convert_to_tensors(_UpperCAmelCase  )

        return padded_inputs





"""simple docstring"""


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    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      MgpstrTokenizer
   snake_case_							=      False
   snake_case_							=      {}
   snake_case_							=      False


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()

        # fmt: off
        lowercase__				 =							["[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
        lowercase__				 =							dict(zip(_UpperCAmelCase,				range(len(_UpperCAmelCase  )  )  )  )

        lowercase__				 =							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(_UpperCAmelCase  ) + "\n"  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return MgpstrTokenizer.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							"tester"
        lowercase__				 =							"tester"
        return input_text, output_text


   @unittest.skip("MGP-STR always lower cases letters."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers(do_lower_case=_UpperCAmelCase  )
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__				 =							"[SPECIAL_TOKEN]"

                  tokenizer.add_special_tokens({"cls_token": special_token}  )
                  lowercase__				 =							tokenizer.encode([special_token],				add_special_tokens=_UpperCAmelCase  )
                  self.assertEqual(len(_UpperCAmelCase  ),				1  )

                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase,				skip_special_tokens=_UpperCAmelCase  )
                  self.assertTrue(special_token not in decoded  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__ ,						lowercase__				 =							self.get_input_output_texts(_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.tokenize(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
                  self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.convert_ids_to_tokens(_UpperCAmelCase  )
                  self.assertNotEqual(len(_UpperCAmelCase  ),				0  )
                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  self.assertEqual(text_a.replace(" ",				""  ),				_UpperCAmelCase  )


   @unittest.skip("MGP-STR tokenizer only handles one sequence."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




