import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MegatronBertForCausalLM,
       MegatronBertForMaskedLM,
       MegatronBertForMultipleChoice,
       MegatronBertForNextSentencePrediction,
       MegatronBertForPreTraining,
       MegatronBertForQuestionAnswering,
       MegatronBertForSequenceClassification,
       MegatronBertForTokenClassification,
       MegatronBertModel,
   )







class __snake_case       :




   def __init__(					self    :       List[str] ,			_lowercase    :       int ,			_lowercase    :       Optional[Any]=13 ,			_lowercase    :       List[Any]=7 ,			_lowercase    :       Dict=True ,			_lowercase    :       Any=True ,			_lowercase    :       int=True ,			_lowercase    :       str=True ,			_lowercase    :       List[Any]=99 ,			_lowercase    :       Any=64 ,			_lowercase    :       Optional[int]=32 ,			_lowercase    :       Optional[int]=5 ,			_lowercase    :       Any=4 ,			_lowercase    :       str=37 ,			_lowercase    :       Optional[int]="gelu" ,			_lowercase    :       str=0.1 ,			_lowercase    :       Optional[int]=0.1 ,			_lowercase    :       List[Any]=5_12 ,			_lowercase    :       List[str]=16 ,			_lowercase    :       List[str]=2 ,			_lowercase    :       Optional[Any]=0.02 ,			_lowercase    :       Tuple=3 ,			_lowercase    :       Any=4 ,			_lowercase    :       List[str]=None ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   seq_length
     SCREAMING_SNAKE_CASE__    							=   is_training
     SCREAMING_SNAKE_CASE__    							=   use_input_mask
     SCREAMING_SNAKE_CASE__    							=   use_token_type_ids
     SCREAMING_SNAKE_CASE__    							=   use_labels
     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   embedding_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   type_vocab_size
     SCREAMING_SNAKE_CASE__    							=   type_sequence_label_size
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   num_labels
     SCREAMING_SNAKE_CASE__    							=   num_choices
     SCREAMING_SNAKE_CASE__    							=   scope




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size   )

     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_input_mask:
       SCREAMING_SNAKE_CASE__    							=   random_attention_mask([self.batch_size, self.seq_length]   )

     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_token_type_ids:
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size   )

     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_labels:
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size] ,			self.type_sequence_label_size   )
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels   )
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size] ,			self.num_choices   )

     SCREAMING_SNAKE_CASE__    							=   self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return MegatronBertConfig(
         vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			embedding_size=self.embedding_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			is_decoder=_lowercase ,			initializer_range=self.initializer_range ,			)




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       List[Any] ,			_lowercase    :       Any ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertModel(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			token_type_ids=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )

     self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)   )
     self.parent.assertEqual(result.pooler_output.shape ,			(self.batch_size, self.hidden_size)   )




   def 			__a		(					self    :       str ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       str ,			_lowercase    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       List[Any] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertForMaskedLM(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertForCausalLM(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertForNextSentencePrediction(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         _lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase ,			)
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, 2)   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       int ,			_lowercase    :       str ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       str ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertForPreTraining(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         _lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase ,			next_sentence_label=_lowercase ,			)
     self.parent.assertEqual(result.prediction_logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)   )
     self.parent.assertEqual(result.seq_relationship_logits.shape ,			(self.batch_size, 2)   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertForQuestionAnswering(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         _lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			start_positions=_lowercase ,			end_positions=_lowercase ,			)
     self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)   )
     self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)   )




   def 			__a		(					self    :       Any ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[int] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_labels
     SCREAMING_SNAKE_CASE__    							=   MegatronBertForSequenceClassification(_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       List[str] ,			_lowercase    :       List[Any] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_labels
     SCREAMING_SNAKE_CASE__    							=   MegatronBertForTokenClassification(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Dict ,			_lowercase    :       int ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       str ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_choices
     SCREAMING_SNAKE_CASE__    							=   MegatronBertForMultipleChoice(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   input_ids.unsqueeze(1   ).expand(-1 ,			self.num_choices ,			-1   ).contiguous()
     SCREAMING_SNAKE_CASE__    							=   token_type_ids.unsqueeze(1   ).expand(-1 ,			self.num_choices ,			-1   ).contiguous()
     SCREAMING_SNAKE_CASE__    							=   input_mask.unsqueeze(1   ).expand(-1 ,			self.num_choices ,			-1   ).contiguous()
     SCREAMING_SNAKE_CASE__    							=   model(
         _lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase ,			)
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_choices)   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  
     )    							=   config_and_inputs
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							(
       (
           MegatronBertModel,
           MegatronBertForMaskedLM,
           MegatronBertForCausalLM,
           MegatronBertForMultipleChoice,
           MegatronBertForNextSentencePrediction,
           MegatronBertForPreTraining,
           MegatronBertForQuestionAnswering,
           MegatronBertForSequenceClassification,
           MegatronBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase_														=							(
       {
           "feature-extraction": MegatronBertModel,
           "fill-mask": MegatronBertForMaskedLM,
           "question-answering": MegatronBertForQuestionAnswering,
           "text-classification": MegatronBertForSequenceClassification,
           "text-generation": MegatronBertForCausalLM,
           "token-classification": MegatronBertForTokenClassification,
           "zero-shot": MegatronBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_														=							True
   # test_resize_embeddings = False
   lowerCAmelCase_														=							False




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Tuple=False   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   super()._prepare_for_class(_lowercase ,			_lowercase ,			return_labels=_lowercase   )

     if return_labels:
       if model_class in get_values(_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length) ,			dtype=torch.long ,			device=_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             self.model_tester.batch_size ,			dtype=torch.long ,			device=_lowercase   )
     return inputs_dict




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MegatronBertModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=_lowercase ,			hidden_size=37   )




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



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_model(*_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_masked_lm(*_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*_lowercase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*_lowercase   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_pretraining(*_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_question_answering(*_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_token_classification(*_lowercase   )





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      Any:







  """simple docstring"""





  return torch.tensor(
      __UpperCamelCase				,  dtype=torch.long				,  device=__UpperCamelCase				,  )


__lowerCamelCase    :				List[Any]							     =	1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case       (		unittest.TestCase	):




   @slow
   @unittest.skip("""Model is not available."""   )
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """nvidia/megatron-bert-uncased-345m"""
     if "MYDIR" in os.environ:
       SCREAMING_SNAKE_CASE__    							=   os.path.join(os.environ["""MYDIR"""] ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   MegatronBertModel.from_pretrained(_lowercase   )
     model.to(_lowercase   )
     model.half()
     SCREAMING_SNAKE_CASE__    							=   _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]   )
     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )[0]
     SCREAMING_SNAKE_CASE__    							=   torch.Size((1, 9, 10_24)   )
     self.assertEqual(output.shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
     for ii in range(3   ):
       for jj in range(3   ):
         SCREAMING_SNAKE_CASE__    							=   output[0, ii, jj]
         SCREAMING_SNAKE_CASE__    							=   expected[3 * ii + jj]
         SCREAMING_SNAKE_CASE__    							=   """ii={} jj={} a={} b={}""".format(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase   )
         self.assertTrue(math.isclose(_lowercase ,			_lowercase ,			rel_tol=_lowercase ,			abs_tol=_lowercase   ) ,			msg=_lowercase   )

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
