




'''simple docstring'''



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      UpperCamelCase_    	=				"pt"
elif is_tf_available():
      UpperCamelCase_    	=				"tf"
else:
      UpperCamelCase_    	=				"jax"






class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Optional[int]        =						ByTaTokenizer
       A						:				Tuple        =						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						ByTaTokenizer()
             tokenizer.save_pretrained(self.tmpdirname  )







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return ByTaTokenizer.from_pretrained('google/byt5-small'  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             return self.tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							A,							A=False,							A=20,							A=5  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             for i in range(len(A  )  ):
                   try:
                         SCREAMING_SNAKE_CASE   :      int    					=						tokenizer.decode([i],							clean_up_tokenization_spaces=A  )
                   except UnicodeDecodeError:
                         pass
                   toks.append((i, tok)  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						list(filter(lambda A  : re.match(r'^[ a-zA-Z]+$',							t[1]  ),							A  )  )
             SCREAMING_SNAKE_CASE   :      Any    					=						list(filter(lambda A  : [t[0]] == tokenizer.encode(t[1],							add_special_tokens=A  ),							A  )  )
             if max_length is not None and len(A  ) > max_length:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						toks[:max_length]
             if min_length is not None and len(A  ) < min_length and len(A  ) > 0:
                   while len(A  ) < min_length:
                         SCREAMING_SNAKE_CASE   :      str    					=						toks + toks
        # toks_str = [t[1] for t in toks]
             SCREAMING_SNAKE_CASE   :      str    					=						[t[0] for t in toks]

             # Ensure consistency
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer.decode(A,							clean_up_tokenization_spaces=A  )
             if " " not in output_txt and len(A  ) > 1:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(
                       tokenizer.decode([toks_ids[0]],							clean_up_tokenization_spaces=A  )
                       + ' '
                       + tokenizer.decode(toks_ids[1:],							clean_up_tokenization_spaces=A  )
                   )
             if with_prefix_space:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						' ' + output_txt
             SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer.encode(A,							add_special_tokens=A  )
             return output_txt, output_ids







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']  )
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer(['hi', 'I went to the gym', '']  )
             self.assertListEqual(batch_with_eos_added['input_ids'],							batch_without_eos_added['input_ids']  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      Dict    					=						'Unicode €.'
             SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
             self.assertEqual(encoded['input_ids'],							A  )

             # decoding
             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.decode(A  )
             self.assertEqual(A,							'Unicode €.</s>'  )

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer('e è é ê ë'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
             self.assertEqual(encoded['input_ids'],							A  )
             # decoding
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.decode(A  )
             self.assertEqual(A,							'e è é ê ë</s>'  )

             # encode/decode, but with `encode` instead of `__call__`
             self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'  )  ),							'e è é ê ë</s>'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      Tuple    					=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             # fmt: off
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
             # fmt: on
             SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer(A,							padding=A,							return_tensors=A  )
             self.assertIsInstance(A,							A  )

             if FRAMEWORK != "jax":
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						list(batch.input_ids.numpy()[0]  )
             else:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						list(batch.input_ids.tolist()[0]  )

             self.assertListEqual(A,							A  )

             self.assertEqual((2, 37),							batch.input_ids.shape  )
             self.assertEqual((2, 37),							batch.attention_mask.shape  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer(A,							padding=A,							return_tensors=A  )
             # check if input_ids are returned and no decoder_input_ids
             self.assertIn('input_ids',							A  )
             self.assertIn('attention_mask',							A  )
             self.assertNotIn('decoder_input_ids',							A  )
             self.assertNotIn('decoder_attention_mask',							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer(
                 text_target=A,							max_length=32,							padding='max_length',							truncation=A,							return_tensors=A  )
             self.assertEqual(32,							targets['input_ids'].shape[1]  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.ta_base_tokenizer
             SCREAMING_SNAKE_CASE   :      List[str]    					=						['A long paragraph for summarization. </s>']
             SCREAMING_SNAKE_CASE   :      Tuple    					=						['Summary of the text. </s>']
             # fmt: off
             SCREAMING_SNAKE_CASE   :      Dict    					=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
             # fmt: on

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer(A,							text_target=A  )

             self.assertEqual(A,							batch['input_ids'][0]  )
             self.assertEqual(A,							batch['labels'][0]  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                         self.assertNotEqual(tokenizer.model_max_length,							42  )

        # Now let's start the test
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                         # Isolate this from the other tests because we save additional tokens/etc
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tempfile.mkdtemp()

                         SCREAMING_SNAKE_CASE   :      int    					=						' He is very happy, UNwant\u00E9d,running'
                         SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer.encode(A,							add_special_tokens=A  )
                         tokenizer.save_pretrained(A  )

                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer.__class__.from_pretrained(A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						after_tokenizer.encode(A,							add_special_tokens=A  )
                         self.assertListEqual(A,							A  )

                         shutil.rmtree(A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_tokenizers(model_max_length=42  )
             for tokenizer in tokenizers:
                   with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                         # Isolate this from the other tests because we save additional tokens/etc
                         SCREAMING_SNAKE_CASE   :      str    					=						tempfile.mkdtemp()

                         SCREAMING_SNAKE_CASE   :      int    					=						' He is very happy, UNwant\u00E9d,running'
                         tokenizer.add_tokens(['bim', 'bambam']  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.additional_special_tokens
                         additional_special_tokens.append('new_additional_special_token'  )
                         tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}  )
                         SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.encode(A,							add_special_tokens=A  )
                         tokenizer.save_pretrained(A  )

                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.__class__.from_pretrained(A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						after_tokenizer.encode(A,							add_special_tokens=A  )
                         self.assertListEqual(A,							A  )
                         self.assertIn('new_additional_special_token',							after_tokenizer.additional_special_tokens  )
                         self.assertEqual(after_tokenizer.model_max_length,							42  )

                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.__class__.from_pretrained(A,							model_max_length=43  )
                         self.assertEqual(tokenizer.model_max_length,							43  )

                         shutil.rmtree(A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
             if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

             if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

             for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(A  )

                         with open(os.path.join(A,							'special_tokens_map.json'  ),							encoding='utf-8'  ) as json_file:
                               SCREAMING_SNAKE_CASE   :      List[Any]    					=						json.load(A  )

                         with open(os.path.join(A,							'tokenizer_config.json'  ),							encoding='utf-8'  ) as json_file:
                               SCREAMING_SNAKE_CASE   :      Any    					=						json.load(A  )

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[F"<extra_id_{i}>" for i in range(125  )]

                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						added_tokens_extra_ids + [
                             'an_additional_special_token'
                         ]
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						added_tokens_extra_ids + [
                             'an_additional_special_token'
                         ]

                         with open(os.path.join(A,							'special_tokens_map.json'  ),							'w',							encoding='utf-8'  ) as outfile:
                               json.dump(A,							A  )
                         with open(os.path.join(A,							'tokenizer_config.json'  ),							'w',							encoding='utf-8'  ) as outfile:
                               json.dump(A,							A  )

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer_class.from_pretrained(
                             A,							)
                         self.assertIn(
                             'an_additional_special_token',							tokenizer_without_change_in_init.additional_special_tokens  )
                         # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                         self.assertEqual(
                             ['an_additional_special_token'],							tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']  )  ),							)

                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         SCREAMING_SNAKE_CASE   :      Any    					=						added_tokens_extra_ids + [AddedToken('a_new_additional_special_token',							lstrip=A  )]
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer_class.from_pretrained(
                             A,							additional_special_tokens=A,							)

                         self.assertIn('a_new_additional_special_token',							tokenizer.additional_special_tokens  )
                         self.assertEqual(
                             ['a_new_additional_special_token'],							tokenizer.convert_ids_to_tokens(
                                 tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']  )  ),							)







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						[]
             if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

             if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

             for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(A  )

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_class.from_pretrained(A  )

                         self.assertTrue(tokenizer.decode([255]  ) == ''  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_tokenizers(fast=A,							do_lower_case=A  )
             for tokenizer in tokenizers:
                   with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.convert_tokens_to_string(A  )

                         self.assertIsInstance(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
                             'bos_token',
                             'eos_token',
                             'unk_token',
                             'sep_token',
                             'pad_token',
                             'cls_token',
                             'mask_token',
                         ]

                         SCREAMING_SNAKE_CASE   :      Any    					=						0
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.convert_ids_to_tokens(
                             A,							skip_special_tokens=A  )

                         for attr in attributes_list:
                               setattr(A,							attr + '_id',							A  )
                               self.assertEqual(getattr(A,							A  ),							A  )
                               self.assertEqual(getattr(A,							attr + '_id'  ),							A  )

                               setattr(A,							attr + '_id',							A  )
                               self.assertEqual(getattr(A,							A  ),							A  )
                               self.assertEqual(getattr(A,							attr + '_id'  ),							A  )

                         setattr(A,							'additional_special_tokens_ids',							[]  )
                         self.assertListEqual(getattr(A,							'additional_special_tokens'  ),							[]  )
                         self.assertListEqual(getattr(A,							'additional_special_tokens_ids'  ),							[]  )

                         setattr(A,							'additional_special_tokens_ids',							[token_id_to_test_setters]  )
                         self.assertListEqual(getattr(A,							'additional_special_tokens'  ),							[token_to_test_setters]  )
                         self.assertListEqual(getattr(A,							'additional_special_tokens_ids'  ),							[token_id_to_test_setters]  )








'''simple docstring'''



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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						spectrogram(
                 A,							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=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







       def __call__(      self,							A,							A = None,							A = True,							A = None,							A = False,							A = False,							**A,							):







             '''simple docstring'''







             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.'  )

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

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

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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:
                   SCREAMING_SNAKE_CASE   :      List[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
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

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

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


