




from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase   :						Dict				=			logging.get_logger(__name__)


class    lowerCamelCase    (		UpperCamelCase_						):






   '''simple docstring'''





   UpperCamelCase__								=['''audio_values''', '''audio_mask''']

   def __init__( self			:   int       ,						lowerCamelCase_			:   Dict=2048       ,						lowerCamelCase_			:   Optional[int]=1       ,						lowerCamelCase_			:   List[Any]=[16, 16]       ,						lowerCamelCase_			:   Any=128       ,						lowerCamelCase_			:   Any=44100       ,						lowerCamelCase_			:   str=86       ,						lowerCamelCase_			:   List[str]=2048       ,						lowerCamelCase_			:   Optional[int]=0.0       ,						**lowerCamelCase_			:   Tuple       ,						)						->   Any:
        super().__init__(
            feature_size=lowerCamelCase_       ,						sampling_rate=lowerCamelCase_       ,						padding_value=lowerCamelCase_       ,						**lowerCamelCase_       ,						)

        __magic_name__							:	Optional[int]    							=							spectrogram_length
        __magic_name__							:	Optional[int]    							=							num_channels
        __magic_name__							:	Any    							=							patch_size
        __magic_name__							:	Any    							=							feature_size // self.patch_size[1]
        __magic_name__							:	List[str]    							=							n_fft
        __magic_name__							:	Optional[Any]    							=							sampling_rate // hop_length_to_sampling_rate
        __magic_name__							:	List[str]    							=							sampling_rate
        __magic_name__							:	Any    							=							padding_value
        __magic_name__							:	Dict    							=							mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2       ,						num_mel_filters=lowerCamelCase_       ,						min_frequency=0.0       ,						max_frequency=2_2_0_5_0.0       ,						sampling_rate=lowerCamelCase_       ,						norm='''slaney'''       ,						mel_scale='''slaney'''       ,						).T

   def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Tuple  )						->   np.ndarray:
        __magic_name__							:	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.T       ,						log_mel='''dB'''       ,						db_range=8_0.0       ,						)
        __magic_name__							:	Optional[Any]    							=							log_spec[:, :-1]
        __magic_name__							:	Any    							=							log_spec - 2_0.0
        __magic_name__							:	Any    							=							np.clip(log_spec / 4_0.0       ,						-2.0       ,						0.0  ) + 1.0
        return log_spec


   def __call__( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Any = None       ,						lowerCamelCase_			:   List[str] = True       ,						lowerCamelCase_			:   List[Any] = None       ,						lowerCamelCase_			:   List[Any] = False       ,						lowerCamelCase_			:   Tuple = False       ,						**lowerCamelCase_			:   int       ,						)						->   BatchFeature:

        if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      '''This feature extractor is set to support sampling rate'''
                      F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                      F''' 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.'''  )

        __magic_name__							:	int    							=							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}'''  )
        __magic_name__							:	Optional[int]    							=							is_batched_numpy or (
            isinstance(lowerCamelCase_       ,						(list, tuple)  ) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)  ))
        )
        if is_batched:
             __magic_name__							:	List[str]    							=							[np.asarray([speech]       ,						dtype=np.floataa  ).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowerCamelCase_       ,						np.ndarray  ):
             __magic_name__							:	List[Any]    							=							np.asarray(lowerCamelCase_       ,						dtype=np.floataa  )
        elif isinstance(lowerCamelCase_       ,						np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             __magic_name__							:	str    							=							raw_speech.astype(np.floataa  )
        # always return batch
        if not is_batched:
             __magic_name__							:	List[Any]    							=							[np.asarray([raw_speech]  ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        __magic_name__							:	Optional[int]    							=							[
            self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0]       ,						lowerCamelCase_  ):
             __magic_name__							:	Optional[Any]    							=							[np.asarray(lowerCamelCase_       ,						dtype=np.floataa  ) for feature in audio_features]

        # Create audio attention mask
        __magic_name__							:	Optional[int]    							=							max(
            [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
        if return_attention_mask:
             __magic_name__							:	Union[str, Any]    							=							[
                 (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             __magic_name__							:	List[Any]    							=							np.array(lowerCamelCase_  ).astype(np.floataa  )

        # convert into correct format for padding
        __magic_name__							:	Optional[Any]    							=							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        __magic_name__							:	int    							=							np.ones([len(lowerCamelCase_  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
        __magic_name__							:	List[str]    							=							padded_audio_features * self.padding_value
        for i in range(len(lowerCamelCase_  )  ):
             __magic_name__							:	str    							=							audio_features[i]
             __magic_name__							:	str    							=							feature

        # return as BatchFeature
        if return_attention_mask:
             __magic_name__							:	List[Any]    							=							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
        else:
             __magic_name__							:	Union[str, Any]    							=							{'''audio_values''': padded_audio_features}

        __magic_name__							:	Dict    							=							BatchFeature(data=lowerCamelCase_       ,						tensor_type=lowerCamelCase_  )
        return encoded_inputs









import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



