
'''simple docstring'''





import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece






class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[Any] ,							snake_case__		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       list(tokenizer_class.max_model_input_sizes.keys()   )
   for model_name in sa_models[:1]:
     UpperCAmelCase__			:				Dict												=       tokenizer_class.from_pretrained(snake_case__   )
     self.assertIsNotNone(snake_case__   )
     self.assertIsInstance(snake_case__ ,							snake_case__   )
     self.assertIsInstance(snake_case__ ,							snake_case__   )

     for special_tok in tokenizer.all_special_tokens:
       self.assertIsInstance(snake_case__ ,							snake_case__   )
       UpperCAmelCase__			:				List[Any]												=       tokenizer.convert_tokens_to_ids(snake_case__   )
       self.assertIsInstance(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							snake_case__		:     BatchEncoding ,							snake_case__		:     Optional[Callable] = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       pickle.dumps(snake_case__   )
   self.assertIsNotNone(snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       pickle.loads(snake_case__   )

   # Ensure is_fast is correctly restored
   self.assertEqual(be_restored.is_fast ,							be_original.is_fast   )

   # Ensure encodings are potentially correctly restored
   if be_original.is_fast:
     self.assertIsNotNone(be_restored.encodings   )
   else:
     self.assertIsNone(be_restored.encodings   )

   # Ensure the keys are the same
   for original_v, restored_v in zip(be_original.values() ,							be_restored.values()   ):
     if equal_op:
       self.assertTrue(equal_op(snake_case__ ,							snake_case__   )   )
     else:
       self.assertEqual(snake_case__ ,							snake_case__   )







 @slow
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   self.check_tokenizer_from_pretrained(snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   self.assertEqual(TensorType("tf"   ) ,							TensorType.TENSORFLOW   )
   self.assertEqual(TensorType("pt"   ) ,							TensorType.PYTORCH   )
   self.assertEqual(TensorType("np"   ) ,							TensorType.NUMPY   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   import numpy as np

   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Tuple												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   # Python no tensor
   with self.subTest("BatchEncoding (Python, return_tensors=None)"   ):
     self.assert_dump_and_restore(tokenizer_p("Small example to encode"   )   )

   with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.NUMPY   ) ,							np.array_equal   )

   with self.subTest("BatchEncoding (Rust, return_tensors=None)"   ):
     self.assert_dump_and_restore(tokenizer_r("Small example to encode"   )   )

   with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.NUMPY   ) ,							np.array_equal   )







 @require_tf
 @require_tokenizers
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   import tensorflow as tf

   def tf_array_equals(snake_case__		:     int ,							snake_case__		:     str   ):
     return tf.reduce_all(tf.equal(snake_case__ ,							snake_case__   )   )

   UpperCAmelCase__			:				Dict												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.TENSORFLOW   ) ,							snake_case__   )

   with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.TENSORFLOW   ) ,							snake_case__   )







 @require_torch
 @require_tokenizers
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   import torch

   UpperCAmelCase__			:				Any												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				str												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.PYTORCH   ) ,							torch.equal   )

   with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.PYTORCH   ) ,							torch.equal   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				int												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("Python Tokenizer"   ):
     self.assertFalse(tokenizer_p("Small example to_encode"   ).is_fast   )

   with self.subTest("Rust Tokenizer"   ):
     self.assertTrue(tokenizer_r("Small example to_encode"   ).is_fast   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer_r(["Test", "\xad", "test"] ,							is_split_into_words=snake_case__   )

   self.assertEqual(encoded.word_to_tokens(0   ) ,							TokenSpan(start=1 ,							end=2   )   )
   self.assertEqual(encoded.word_to_tokens(1   ) ,							snake_case__   )
   self.assertEqual(encoded.word_to_tokens(2   ) ,							TokenSpan(start=2 ,							end=3   )   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Dict												=       batch.convert_to_tensors(tensor_type="np"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				List[Any]												=       batch.convert_to_tensors(tensor_type="np"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				Tuple												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				str												=       batch.convert_to_tensors(tensor_type="np" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 @require_torch
 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Union[str, Any]												=       batch.convert_to_tensors(tensor_type="pt"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				List[Any]												=       batch.convert_to_tensors(tensor_type="pt"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				int												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="pt" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 @require_tf
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="tf"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				Any												=       batch.convert_to_tensors(tensor_type="tf"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				List[str]												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Union[str, Any]												=       batch.convert_to_tensors(tensor_type="tf" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 @require_flax
 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Optional[int]												=       batch.convert_to_tensors(tensor_type="jax"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				Tuple												=       batch.convert_to_tensors(tensor_type="jax"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				Optional[Any]												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="jax" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       [{"input_ids": np.array([0, 1, 2]   )}, {"input_ids": np.array([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				str												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							np.ndarray   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				int												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="np"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							np.ndarray   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_torch
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   import torch

   UpperCAmelCase__			:				Optional[int]												=       [{"input_ids": torch.tensor([0, 1, 2]   )}, {"input_ids": torch.tensor([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				Dict												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Any												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							torch.Tensor   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				str												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="pt"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							torch.Tensor   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_tf
 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   import tensorflow as tf

   UpperCAmelCase__			:				Any												=       [{"input_ids": tf.constant([0, 1, 2]   )}, {"input_ids": tf.constant([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				List[str]												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							tf.Tensor   )   )
   self.assertEqual(batch["input_ids"].numpy().tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				Union[str, Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="tf"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							tf.Tensor   )   )
   self.assertEqual(batch["input_ids"].numpy().tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       Tokenizer(WordPiece(unk_token="[UNK]"   )   )
   PreTrainedTokenizerFast(tokenizer_object=snake_case__   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       Tokenizer(WordPiece(unk_token="[UNK]"   )   )
   with tempfile.TemporaryDirectory() as tmpdirname:
     bert_tokenizer.save(os.path.join(snake_case__ ,							"tokenizer.json"   )   )
     PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case__ ,							"tokenizer.json"   )   )





'''simple docstring'''





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE    =					random.Random()







def snake_case_    (				lowercase__      ,					lowercase__=1.0      ,					lowercase__=None      ,					lowercase__=None		):
  if rng is None:
    UpperCAmelCase__			:				Optional[int]												=       global_rng

  UpperCAmelCase__			:				List[str]												=       []
  for batch_idx in range(shape[0]		):
    values.append([]		)
    for _ in range(shape[1]		):
      values[-1].append(rng.random() * scale		)

  return values






@require_torch
@require_sentencepiece
class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       "facebook/musicgen-small"
   UpperCAmelCase__			:				Union[str, Any]												=       tempfile.mkdtemp()







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     Dict   ):



   '''simple docstring'''




   return TaTokenizer.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     str   ):



   '''simple docstring'''




   return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   shutil.rmtree(self.tmpdirname   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_tokenizer()
   UpperCAmelCase__			:				Tuple												=       self.get_feature_extractor()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   processor.save_pretrained(self.tmpdirname   )
   UpperCAmelCase__			:				Dict												=       MusicgenProcessor.from_pretrained(self.tmpdirname   )

   self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer.get_vocab()   )
   self.assertIsInstance(processor.tokenizer ,							snake_case__   )

   self.assertEqual(processor.feature_extractor.to_json_string() ,							feature_extractor.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"   )
   UpperCAmelCase__			:				int												=       self.get_feature_extractor(do_normalize=snake_case__ ,							padding_value=1.0   )

   UpperCAmelCase__			:				Optional[Any]												=       MusicgenProcessor.from_pretrained(
       self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=snake_case__ ,							padding_value=1.0   )

   self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer_add_kwargs.get_vocab()   )
   self.assertIsInstance(processor.tokenizer ,							snake_case__   )

   self.assertEqual(processor.feature_extractor.to_json_string() ,							feature_extractor_add_kwargs.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor ,							snake_case__   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor()
   UpperCAmelCase__			:				int												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[str]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       floats_list((3, 10_00)   )

   UpperCAmelCase__			:				List[Any]												=       feature_extractor(snake_case__ ,							return_tensors="np"   )
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__ ,							return_tensors="np"   )

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum() ,							input_processor[key].sum() ,							delta=1e-2   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				Union[str, Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Optional[int]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       "This is a test string"

   UpperCAmelCase__			:				Dict												=       processor(text=snake_case__   )

   UpperCAmelCase__			:				Any												=       tokenizer(snake_case__   )

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,							encoded_processor[key]   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[int]												=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   UpperCAmelCase__			:				Any												=       processor.batch_decode(sequences=snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.batch_decode(snake_case__   )

   self.assertListEqual(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_feature_extractor()
   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Union[str, Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   self.assertListEqual(
       processor.model_input_names ,							feature_extractor.model_input_names ,							msg="`processor` and `feature_extractor` model input names do not match" ,							)







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor(padding_side="left"   )
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       [floats_list((1, x)   )[0] for x in range(5 ,							20 ,							5   )]
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__   ).padding_mask

   UpperCAmelCase__			:				Dict												=       np.asarray(floats_list((3, 20)   )   )[:, None, :]
   UpperCAmelCase__			:				Optional[Any]												=       processor.batch_decode(snake_case__ ,							padding_mask=snake_case__   )

   self.assertIsInstance(snake_case__ ,							snake_case__   )

   for audio in decoded_audios:
     self.assertIsInstance(snake_case__ ,							np.ndarray   )

   self.assertTrue(decoded_audios[0].shape == (1, 10)   )
   self.assertTrue(decoded_audios[1].shape == (1, 15)   )
   self.assertTrue(decoded_audios[2].shape == (1, 20)   )



