import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase    :				int							     =	get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCamelCase    :				Optional[Any]							     =	25_6047
__lowerCamelCase    :				List[str]							     =	25_6145







@require_sentencepiece
@require_tokenizers
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							NllbTokenizer
   lowerCAmelCase_														=							NllbTokenizerFast
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							{}




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE__    							=   NllbTokenizer(_lowercase ,			keep_accents=_lowercase   )
     tokenizer.save_pretrained(self.tmpdirname   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   NllbTokenizer(_lowercase ,			keep_accents=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize("""This is a test"""   )
     self.assertListEqual(_lowercase ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowercase   ) ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,			)

     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
     self.assertListEqual(
         _lowercase ,			[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ] ,			)
     SCREAMING_SNAKE_CASE__    							=   tokenizer.convert_tokens_to_ids(_lowercase   )
     self.assertListEqual(
         _lowercase ,			[
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ] ,			)

     SCREAMING_SNAKE_CASE__    							=   tokenizer.convert_ids_to_tokens(_lowercase   )
     self.assertListEqual(
         _lowercase ,			[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ] ,			)




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
     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(_lowercase ,			**_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   self.tokenizer_class.from_pretrained(_lowercase ,			**_lowercase   )

         SCREAMING_SNAKE_CASE__    							=   tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.save_pretrained(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.save_pretrained(_lowercase   )

         # Checks it save with the same files + the tokenizer.json file for the fast one
         self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )
         SCREAMING_SNAKE_CASE__    							=   tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
         self.assertSequenceEqual(_lowercase ,			_lowercase   )

         # Checks everything loads correctly in the same way
         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.from_pretrained(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.from_pretrained(_lowercase   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(_lowercase ,			_lowercase   )   )

         shutil.rmtree(_lowercase   )

         # Save tokenizer rust, legacy_format=True
         SCREAMING_SNAKE_CASE__    							=   tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.save_pretrained(_lowercase ,			legacy_format=_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.save_pretrained(_lowercase   )

         # Checks it save with the same files
         self.assertSequenceEqual(_lowercase ,			_lowercase   )

         # Checks everything loads correctly in the same way
         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.from_pretrained(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.from_pretrained(_lowercase   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(_lowercase ,			_lowercase   )   )

         shutil.rmtree(_lowercase   )

         # Save tokenizer rust, legacy_format=False
         SCREAMING_SNAKE_CASE__    							=   tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.save_pretrained(_lowercase ,			legacy_format=_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.save_pretrained(_lowercase   )

         # Checks it saved the tokenizer.json file
         self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )

         # Checks everything loads correctly in the same way
         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.from_pretrained(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_p.from_pretrained(_lowercase   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(_lowercase ,			_lowercase   )   )

         shutil.rmtree(_lowercase   )




   @require_torch
   def 			__a		(					self    :       int   ):



     """simple docstring"""






     if not self.test_seqaseq:
       return

     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizers()
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
         # Longer text that will definitely require truncation.
         SCREAMING_SNAKE_CASE__    							=   [
             """ UN Chief Says There Is No Military Solution in Syria""",
             """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
             """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
             """ will only worsen the violence and misery for millions of people.""",
         ]
         SCREAMING_SNAKE_CASE__    							=   [
             """Şeful ONU declară că nu există o soluţie militară în Siria""",
             """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
             """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
             """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
         ]
         try:
           SCREAMING_SNAKE_CASE__    							=   tokenizer.prepare_seqaseq_batch(
               src_texts=_lowercase ,			tgt_texts=_lowercase ,			max_length=3 ,			max_target_length=10 ,			return_tensors="""pt""" ,			src_lang="""eng_Latn""" ,			tgt_lang="""ron_Latn""" ,			)
         except NotImplementedError:
           return
         self.assertEqual(batch.input_ids.shape[1] ,			3   )
         self.assertEqual(batch.labels.shape[1] ,			10   )
         # max_target_length will default to max_length if not specified
         SCREAMING_SNAKE_CASE__    							=   tokenizer.prepare_seqaseq_batch(
             _lowercase ,			tgt_texts=_lowercase ,			max_length=3 ,			return_tensors="""pt"""   )
         self.assertEqual(batch.input_ids.shape[1] ,			3   )
         self.assertEqual(batch.labels.shape[1] ,			3   )

         SCREAMING_SNAKE_CASE__    							=   tokenizer.prepare_seqaseq_batch(
             src_texts=_lowercase ,			max_length=3 ,			max_target_length=10 ,			return_tensors="""pt"""   )
         self.assertEqual(batch_encoder_only.input_ids.shape[1] ,			3   )
         self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,			3   )
         self.assertNotIn("""decoder_input_ids""" ,			_lowercase   )




   @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""   )
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         SCREAMING_SNAKE_CASE__    							=   [AddedToken("""<special>""" ,			lstrip=_lowercase   )]

         SCREAMING_SNAKE_CASE__    							=   self.rust_tokenizer_class.from_pretrained(
             _lowercase ,			additional_special_tokens=_lowercase ,			**_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.encode("""Hey this is a <special> token"""   )

         SCREAMING_SNAKE_CASE__    							=   tokenizer_r.encode("""<special>""" ,			add_special_tokens=_lowercase   )[0]

         self.assertTrue(special_token_id in r_output   )

         if self.test_slow_tokenizer:
           SCREAMING_SNAKE_CASE__    							=   self.rust_tokenizer_class.from_pretrained(
               _lowercase ,			additional_special_tokens=_lowercase ,			**_lowercase ,			)
           SCREAMING_SNAKE_CASE__    							=   self.tokenizer_class.from_pretrained(
               _lowercase ,			additional_special_tokens=_lowercase ,			**_lowercase   )

           SCREAMING_SNAKE_CASE__    							=   tokenizer_p.encode("""Hey this is a <special> token"""   )

           SCREAMING_SNAKE_CASE__    							=   tokenizer_cr.encode("""Hey this is a <special> token"""   )

           self.assertEqual(_lowercase ,			_lowercase   )
           self.assertEqual(_lowercase ,			_lowercase   )
           self.assertTrue(special_token_id in p_output   )
           self.assertTrue(special_token_id in cr_output   )







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							"facebook/nllb-200-distilled-600M"
   lowerCAmelCase_														=							[
       " UN Chief Says There Is No Military Solution in Syria",
       " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
   ]
   lowerCAmelCase_														=							[
       "Şeful ONU declară că nu există o soluţie militară în Siria",
       "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
       " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
       " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
   ]
   lowerCAmelCase_														=							[
       25_60_47,
       1_62_97,
       13_44_08,
       81_65,
       24_80_66,
       1_47_34,
       9_50,
       11_35,
       10_57_21,
       35_73,
       83,
       2_73_52,
       1_08,
       4_94_86,
       2,
   ]




   @classmethod
   def 			__a		(					cls    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   NllbTokenizer.from_pretrained(
         cls.checkpoint_name ,			src_lang="""eng_Latn""" ,			tgt_lang="""ron_Latn"""   )
     SCREAMING_SNAKE_CASE__    							=   1
     return cls




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



     """simple docstring"""






     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] ,			25_60_01   )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] ,			25_60_02   )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] ,			25_60_57   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens ,			_lowercase   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     self.assertIn(_lowercase ,			self.tokenizer.all_special_ids   )
     # fmt: off
     SCREAMING_SNAKE_CASE__    							=   [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
     # fmt: on

     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.decode(_lowercase ,			skip_special_tokens=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.decode(generated_ids[1:] ,			skip_special_tokens=_lowercase   )
     self.assertEqual(_lowercase ,			_lowercase   )
     self.assertNotIn(self.tokenizer.eos_token ,			_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""this is gunna be a long sentence """ * 20]
     assert isinstance(src_text[0] ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   10
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			max_length=_lowercase ,			truncation=_lowercase   ).input_ids[0]
     self.assertEqual(ids[-1] ,			2   )
     self.assertEqual(ids[0] ,			_lowercase   )
     self.assertEqual(len(_lowercase   ) ,			_lowercase   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]   ) ,			[25_62_03, 3]   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   NllbTokenizer.from_pretrained(_lowercase   )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,			_lowercase   )




   @require_torch
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(
         self.src_text ,			text_target=self.tgt_text ,			padding=_lowercase ,			truncation=_lowercase ,			max_length=len(self.expected_src_tokens   ) ,			return_tensors="""pt""" ,			)
     SCREAMING_SNAKE_CASE__    							=   shift_tokens_right(
         batch["""labels"""] ,			self.tokenizer.pad_token_id ,			self.tokenizer.lang_code_to_id["""ron_Latn"""]   )

     self.assertIsInstance(_lowercase ,			_lowercase   )

     self.assertEqual((2, 15) ,			batch.input_ids.shape   )
     self.assertEqual((2, 15) ,			batch.attention_mask.shape   )
     SCREAMING_SNAKE_CASE__    							=   batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens ,			_lowercase   )
     self.assertEqual(_lowercase ,			batch.decoder_input_ids[0, 0]   )  # EOS
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens ,			[EN_CODE]   )
     self.assertEqual(self.tokenizer.suffix_tokens ,			[self.tokenizer.eos_token_id]   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(self.src_text ,			padding=_lowercase ,			truncation=_lowercase ,			max_length=3 ,			return_tensors="""pt"""   )
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(
         text_target=self.tgt_text ,			padding=_lowercase ,			truncation=_lowercase ,			max_length=10 ,			return_tensors="""pt"""   )
     SCREAMING_SNAKE_CASE__    							=   targets["""input_ids"""]
     SCREAMING_SNAKE_CASE__    							=   shift_tokens_right(
         _lowercase ,			self.tokenizer.pad_token_id ,			decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,			)

     self.assertEqual(batch.input_ids.shape[1] ,			3   )
     self.assertEqual(batch.decoder_input_ids.shape[1] ,			10   )




   @require_torch
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer._build_translation_inputs(
         """A test""" ,			return_tensors="""pt""" ,			src_lang="""eng_Latn""" ,			tgt_lang="""fra_Latn"""   )

     self.assertEqual(
         nested_simplify(_lowercase   ) ,			{
             # A, test, EOS, en_XX
             """input_ids""": [[25_60_47, 70, 73_56, 2]],
             """attention_mask""": [[1, 1, 1, 1]],
             # ar_AR
             """forced_bos_token_id""": 25_60_57,
         } ,			)




   @require_torch
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   True
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(
         """UN Chief says there is no military solution in Syria""" ,			src_lang="""eng_Latn""" ,			tgt_lang="""fra_Latn"""   )
     self.assertEqual(
         inputs.input_ids ,			[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]   )

     SCREAMING_SNAKE_CASE__    							=   False
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(
         """UN Chief says there is no military solution in Syria""" ,			src_lang="""eng_Latn""" ,			tgt_lang="""fra_Latn"""   )
     self.assertEqual(
         inputs.input_ids ,			[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]   )

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio is None and text is None:
       raise ValueError("""You need to specify either an `audio` or `text` input to process."""   )

     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
