




"""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():
					A				=							"""pt"""
elif is_tf_available():
					A				=							"""tf"""
else:
					A				=							"""jax"""



class   a__					(						__magic_name__				,     unittest.TestCase ):
			lowercase_		=						ByTaTokenizer
			lowercase_		=						False


			def    a_  (   self   :     Any):


						"""simple docstring"""




						super().setUp()
						__UpperCAmelCase						:			List[str]												=       ByTaTokenizer()
						tokenizer.save_pretrained(self.tmpdirname)


			@cached_property
			def    a_  (   self   :     List[str]):


						"""simple docstring"""




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


			def    a_  (   self   :     Any   ,	**UpperCamelCase_   :     str):


						"""simple docstring"""




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


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     List[str]=False   ,	UpperCamelCase_   :     Dict=20   ,	UpperCamelCase_   :     Dict=5):


						"""simple docstring"""




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

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

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


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




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


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Tuple												=       "Unicode €."
						__UpperCAmelCase						:			List[str]												=       tokenizer(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
						self.assertEqual(encoded["input_ids"]   ,	UpperCamelCase_)

						# decoding
						__UpperCAmelCase						:			Optional[Any]												=       tokenizer.decode(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	"Unicode €.</s>")

						__UpperCAmelCase						:			Any												=       tokenizer("e è é ê ë")
						__UpperCAmelCase						:			Any												=       [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
						self.assertEqual(encoded["input_ids"]   ,	UpperCamelCase_)
						# decoding
						__UpperCAmelCase						:			str												=       tokenizer.decode(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	"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    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Optional[int]												=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						# fmt: off
						__UpperCAmelCase						:			List[Any]												=       [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
						__UpperCAmelCase						:			Any												=       tokenizer(UpperCamelCase_   ,	padding=UpperCamelCase_   ,	return_tensors=UpperCamelCase_)
						self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)

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

						self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)

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


			def    a_  (   self   :     int):


						"""simple docstring"""




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


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Tuple												=       [
						    "Summary of the text.",
						    "Another summary.",
						]
						__UpperCAmelCase						:			Union[str, Any]												=       tokenizer(
						    text_target=UpperCamelCase_   ,	max_length=32   ,	padding="max_length"   ,	truncation=UpperCamelCase_   ,	return_tensors=UpperCamelCase_)
						self.assertEqual(32   ,	targets["input_ids"].shape[1])


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Any												=       ["A long paragraph for summarization. </s>"]
						__UpperCAmelCase						:			str												=       ["Summary of the text. </s>"]
						# fmt: off
						__UpperCAmelCase						:			Optional[Any]												=       [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]
						__UpperCAmelCase						:			Dict												=       [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
						# fmt: on

						__UpperCAmelCase						:			List[str]												=       tokenizer(UpperCamelCase_   ,	text_target=UpperCamelCase_)

						self.assertEqual(UpperCamelCase_   ,	batch["input_ids"][0])
						self.assertEqual(UpperCamelCase_   ,	batch["labels"][0])


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       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
						__UpperCAmelCase						:			str												=       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
												__UpperCAmelCase						:			List[Any]												=       tempfile.mkdtemp()

												__UpperCAmelCase						:			int												=       " He is very happy, UNwant\u00E9d,running"
												__UpperCAmelCase						:			str												=       tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												tokenizer.save_pretrained(UpperCamelCase_)

												__UpperCAmelCase						:			Tuple												=       tokenizer.__class__.from_pretrained(UpperCamelCase_)
												__UpperCAmelCase						:			Optional[Any]												=       after_tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)

												shutil.rmtree(UpperCamelCase_)

						__UpperCAmelCase						:			Tuple												=       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
												__UpperCAmelCase						:			Union[str, Any]												=       tempfile.mkdtemp()

												__UpperCAmelCase						:			Any												=       " He is very happy, UNwant\u00E9d,running"
												tokenizer.add_tokens(["bim", "bambam"])
												__UpperCAmelCase						:			Optional[int]												=       tokenizer.additional_special_tokens
												additional_special_tokens.append("new_additional_special_token")
												tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												tokenizer.save_pretrained(UpperCamelCase_)

												__UpperCAmelCase						:			Union[str, Any]												=       tokenizer.__class__.from_pretrained(UpperCamelCase_)
												__UpperCAmelCase						:			List[Any]												=       after_tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)
												self.assertIn("new_additional_special_token"   ,	after_tokenizer.additional_special_tokens)
												self.assertEqual(after_tokenizer.model_max_length   ,	42)

												__UpperCAmelCase						:			List[Any]												=       tokenizer.__class__.from_pretrained(UpperCamelCase_   ,	model_max_length=43)
												self.assertEqual(tokenizer.model_max_length   ,	43)

												shutil.rmtree(UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       []
						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(UpperCamelCase_)

												with open(os.path.join(UpperCamelCase_   ,	"special_tokens_map.json")   ,	encoding="utf-8") as json_file:
															__UpperCAmelCase						:			int												=       json.load(UpperCamelCase_)

												with open(os.path.join(UpperCamelCase_   ,	"tokenizer_config.json")   ,	encoding="utf-8") as json_file:
															__UpperCAmelCase						:			Dict												=       json.load(UpperCamelCase_)

												__UpperCAmelCase						:			Dict												=       [F"<extra_id_{i}>" for i in range(125)]

												__UpperCAmelCase						:			Dict												=       added_tokens_extra_ids + [
												    "an_additional_special_token"
												]
												__UpperCAmelCase						:			int												=       added_tokens_extra_ids + [
												    "an_additional_special_token"
												]

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

												# 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
												__UpperCAmelCase						:			List[Any]												=       tokenizer_class.from_pretrained(
												    UpperCamelCase_   ,	)
												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
												__UpperCAmelCase						:			Any												=       added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"   ,	lstrip=UpperCamelCase_)]
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer_class.from_pretrained(
												    UpperCamelCase_   ,	additional_special_tokens=UpperCamelCase_   ,	)

												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    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       []
						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(UpperCamelCase_)

												__UpperCAmelCase						:			Any												=       tokenizer_class.from_pretrained(UpperCamelCase_)

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


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Any):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.get_tokenizers(fast=UpperCamelCase_   ,	do_lower_case=UpperCamelCase_)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												__UpperCAmelCase						:			Optional[Any]												=       ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer.convert_tokens_to_string(UpperCamelCase_)

												self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												__UpperCAmelCase						:			List[Any]												=       [
												    "bos_token",
												    "eos_token",
												    "unk_token",
												    "sep_token",
												    "pad_token",
												    "cls_token",
												    "mask_token",
												]

												__UpperCAmelCase						:			Dict												=       0
												__UpperCAmelCase						:			str												=       tokenizer.convert_ids_to_tokens(
												    UpperCamelCase_   ,	skip_special_tokens=UpperCamelCase_)

												for attr in attributes_list:
															setattr(UpperCamelCase_   ,	attr + "_id"   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	UpperCamelCase_)   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	attr + "_id")   ,	UpperCamelCase_)

															setattr(UpperCamelCase_   ,	attr + "_id"   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	UpperCamelCase_)   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	attr + "_id")   ,	UpperCamelCase_)

												setattr(UpperCamelCase_   ,	"additional_special_tokens_ids"   ,	[])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens")   ,	[])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens_ids")   ,	[])

												setattr(UpperCamelCase_   ,	"additional_special_tokens_ids"   ,	[token_id_to_test_setters])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens")   ,	[token_to_test_setters])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"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


