





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	GPTaTokenizer
    lowerCamelCase__    :		Any				=	GPTaTokenizerFast
    lowerCamelCase__    :		Optional[int]				=	True
    lowerCamelCase__    :		int				=	{"""add_prefix_space""": True}
    lowerCamelCase__    :		List[str]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
            '''<|endoftext|>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        SCREAMING_SNAKE_CASE__											=			{'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			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(A_     ) + '''\n'''     )
        with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not self.test_rust_tokenizer:
            return

        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''

        # Testing tokenization
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids without special tokens
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_   ,				add_special_tokens=A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids with special tokens
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing the unknown token
        SCREAMING_SNAKE_CASE__											=			tokens + [rust_tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self   ,				A_=15     ):





        '''simple docstring'''

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                SCREAMING_SNAKE_CASE__											=			self.rust_tokenizer_class.from_pretrained(A_   ,				**A_     )

                # Simple input
                SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
                SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']
                SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
                SCREAMING_SNAKE_CASE__											=			[
                    ('''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(A_   ,				tokenizer_r.encode   ,				A_   ,				max_length=A_   ,				padding='''max_length'''     )

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

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

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

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				pad_token='''<pad>'''     )

        # Simple input
        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input looooooooong''', '''This is a simple input''']
        SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
        SCREAMING_SNAKE_CASE__											=			[
            ('''This is a simple input loooooong''', '''This is a simple input'''),
            ('''This is a simple pair loooooong''', '''This is a simple pair'''),
        ]

        SCREAMING_SNAKE_CASE__											=			tokenizer.pad_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding='''max_length'''   ,				max_length=30   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(*A_   ,				padding='''max_length'''   ,				max_length=60   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )

        # s
        # test single string max_length padding
        self.assertEqual(out_s['''input_ids'''].shape[-1]   ,				30     )
        self.assertTrue(pad_token_id in out_s['''input_ids''']     )
        self.assertTrue(0 in out_s['''attention_mask''']     )

        # s2
        # test automatic padding
        self.assertEqual(out_sa['''input_ids'''].shape[-1]   ,				33     )
        # long slice doesn't have padding
        self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]     )
        self.assertFalse(0 in out_sa['''attention_mask'''][0]     )
        # short slice does have padding
        self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]     )
        self.assertTrue(0 in out_sa['''attention_mask'''][1]     )

        # p
        # test single pair max_length padding
        self.assertEqual(out_p['''input_ids'''].shape[-1]   ,				60     )
        self.assertTrue(pad_token_id in out_p['''input_ids''']     )
        self.assertTrue(0 in out_p['''attention_mask''']     )

        # p2
        # test automatic padding pair
        self.assertEqual(out_pa['''input_ids'''].shape[-1]   ,				52     )
        # long slice pair doesn't have padding
        self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]     )
        self.assertFalse(0 in out_pa['''attention_mask'''][0]     )
        # short slice pair does have padding
        self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]     )
        self.assertTrue(0 in out_pa['''attention_mask'''][1]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''$$$'''
        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				bos_token=A_   ,				add_bos_token=A_     )

        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']

        SCREAMING_SNAKE_CASE__											=			tokenizer.bos_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )

        self.assertEqual(out_s.input_ids[0]   ,				A_     )
        self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.decode(out_s.input_ids     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(out_sa.input_ids     )

        self.assertEqual(decode_s.split()[0]   ,				A_     )
        self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[self.get_tokenizer(do_lower_case=A_   ,				add_bos_token=A_     )]
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
                SCREAMING_SNAKE_CASE__											=			'''Encode this.'''
                SCREAMING_SNAKE_CASE__											=			'''This one too please.'''
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_     )
                encoded_sequence += tokenizer.encode(A_   ,				add_special_tokens=A_     )
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode_plus(
                    A_   ,				A_   ,				add_special_tokens=A_   ,				return_special_tokens_mask=A_   ,				)
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''input_ids''']
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''special_tokens_mask''']
                self.assertEqual(len(A_     )   ,				len(A_     )     )

                SCREAMING_SNAKE_CASE__											=			[
                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_     )
                ]
                SCREAMING_SNAKE_CASE__											=			[x for x in filtered_sequence if x is not None]
                self.assertEqual(A_   ,				A_     )





@require_tokenizers
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''test_opt'''     )

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./test_opt'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				use_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # Same as above
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )




    @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )

        SCREAMING_SNAKE_CASE__											=			'''bos'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.get_vocab()['''bos''']

        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # We changed the bos token
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''./tok'''     )
        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./tok'''     )
        self.assertTrue(tokenizer.is_fast     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


"""simple docstring"""




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

import numpy as np
import torch

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		:       int     						=       logging.get_logger(__name__)







class     lowercase(_lowercase       ):
     __snake_case:	Any						=     ['input_features', 'is_longer']



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=6_4     ,				__SCREAMING_SNAKE_CASE=4_8_0_0_0     ,				__SCREAMING_SNAKE_CASE=4_8_0     ,				__SCREAMING_SNAKE_CASE=1_0     ,				__SCREAMING_SNAKE_CASE=1_0_2_4     ,				__SCREAMING_SNAKE_CASE=0.0     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE = 0     ,				__SCREAMING_SNAKE_CASE = 1_4_0_0_0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "fusion"     ,				__SCREAMING_SNAKE_CASE = "repeatpad"     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__(
             feature_size=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				padding_value=__SCREAMING_SNAKE_CASE     ,				return_attention_mask=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			top_db
         a__		=			truncation
         a__		=			padding
         a__		=			fft_window_size
         a__		=			(fft_window_size >> 1) + 1
         a__		=			hop_length
         a__		=			max_length_s
         a__		=			max_length_s * sampling_rate
         a__		=			sampling_rate
         a__		=			frequency_min
         a__		=			frequency_max
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm=__SCREAMING_SNAKE_CASE     ,				mel_scale='htk'     ,				)
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm='slaney'     ,				mel_scale='slaney'     ,				)



     def 			lowercase__  (							self   )		->     Dict[str, Any]:







         """simple docstring"""



         a__		=			copy.deepcopy(self.__dict__   )
         a__		=			self.__class__.__name__
         if "mel_filters" in output:
             del output["mel_filters"]
         if "mel_filters_slaney" in output:
             del output["mel_filters_slaney"]
         return output



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     np.ndarray:







         """simple docstring"""



         a__		=			spectrogram(
             __SCREAMING_SNAKE_CASE     ,				window_function(self.fft_window_size     ,				'hann'   )     ,				frame_length=self.fft_window_size     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=__SCREAMING_SNAKE_CASE     ,				log_mel='dB'     ,				)
         return log_mel_spectrogram.T



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			np.array_split(list(range(0     ,				total_frames - chunk_frames + 1   )   )     ,				3   )
         if len(ranges[1]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         if len(ranges[2]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         # randomly choose index for each part
         a__		=			np.random.choice(ranges[0]   )
         a__		=			np.random.choice(ranges[1]   )
         a__		=			np.random.choice(ranges[2]   )

         a__		=			mel[idx_front : idx_front + chunk_frames, :]
         a__		=			mel[idx_middle : idx_middle + chunk_frames, :]
         a__		=			mel[idx_back : idx_back + chunk_frames, :]

         a__		=			torch.tensor(mel[None, None, :]   )
         a__		=			torch.nn.functional.interpolate(
             __SCREAMING_SNAKE_CASE     ,				size=[chunk_frames, 6_4]     ,				mode='bilinear'     ,				align_corners=__SCREAMING_SNAKE_CASE   )
         a__		=			mel_shrink[0][0].numpy()
         a__		=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,				axis=0   )
         return mel_fusion



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     np.array:







         """simple docstring"""



         if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                 a__		=			True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 a__		=			len(__SCREAMING_SNAKE_CASE   ) - max_length
                 a__		=			np.random.randint(0     ,				overflow + 1   )
                 a__		=			waveform[idx : idx + max_length]
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]
             elif truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 a__		=			mel.shape[0]
                 if chunk_frames == total_frames:
                     # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                     # In this case, we just use the whole audio.
                     a__		=			np.stack([mel, mel, mel, mel]     ,				axis=0   )
                     a__		=			False
                 else:
                     a__		=			self._random_mel_fusion(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                     a__		=			True
             else:
                 raise NotImplementedError(f'data_truncating {truncation} not implemented'   )

         else:
             a__		=			False
             # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
             if waveform.shape[0] < max_length:
                 if padding == "repeat":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				n_repeat + 1   )   )[:max_length]
                 if padding == "repeatpad":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
                 a__		=			np.pad(__SCREAMING_SNAKE_CASE     ,				(0, max_length - waveform.shape[0])     ,				mode='constant'     ,				constant_values=0   )

             if truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			np.stack([input_mel, input_mel, input_mel, input_mel]     ,				axis=0   )
             else:
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]

         return input_mel, longer




     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     BatchFeature:







         """simple docstring"""



         a__		=			truncation if truncation is not None else self.truncation
         a__		=			padding if padding else self.padding

         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.'   )

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

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

         # always return batch
         if not is_batched:
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE   )]

         # convert to mel spectrogram, truncate and pad if needed.
         a__		=			[
             self._get_input_mel(__SCREAMING_SNAKE_CASE     ,				max_length if max_length else self.nb_max_samples     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
             for waveform in raw_speech
         ]

         a__		=			[]
         a__		=			[]
         for mel, longer in padded_inputs:
             input_mel.append(__SCREAMING_SNAKE_CASE   )
             is_longer.append(__SCREAMING_SNAKE_CASE   )

         if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE   ) == 0:
             # if no audio is longer than 10s, then randomly select one audio to be longer
             a__		=			np.random.randint(0     ,				len(__SCREAMING_SNAKE_CASE   )   )
             a__		=			True

         if isinstance(input_mel[0]     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   ) for feature in input_mel]

         # is_longer is a list of bool
         a__		=			[[longer] for longer in is_longer]

         a__		=			{'input_features': input_mel, 'is_longer': is_longer}
         a__		=			BatchFeature(__SCREAMING_SNAKE_CASE   )

         if return_tensors is not None:
             a__		=			input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE   )

         return input_features


