"""simple docstring"""







import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         ElectraForCausalLM,
         ElectraForMaskedLM,
         ElectraForMultipleChoice,
         ElectraForPreTraining,
         ElectraForQuestionAnswering,
         ElectraForSequenceClassification,
         ElectraForTokenClassification,
         ElectraModel,
     )
     from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=5 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=37 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=None ,				):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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

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

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

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				1	)

      __lowerCamelCase    		=					self.get_config()

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          fake_token_labels,
      )







   def    _A				(	self	):


      '''simple docstring'''

      return ElectraConfig(
          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 ,				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=_SCREAMING_SNAKE_CASE ,				initializer_range=self.initializer_range ,				)







   def    _A				(	self	):


      '''simple docstring'''

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					self.prepare_config_and_inputs()

      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2	)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					ElectraModel(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				encoder_hidden_states=_SCREAMING_SNAKE_CASE ,				encoder_attention_mask=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				encoder_hidden_states=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraForMaskedLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraForCausalLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ElectraForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ElectraForPreTraining(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ElectraForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				start_positions=_SCREAMING_SNAKE_CASE ,				end_positions=_SCREAMING_SNAKE_CASE ,				)
      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 ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    (
       (
           ElectraModel,
           ElectraForPreTraining,
           ElectraForMaskedLM,
           ElectraForCausalLM,
           ElectraForMultipleChoice,
           ElectraForTokenClassification,
           ElectraForSequenceClassification,
           ElectraForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase	:							Tuple    =    (
       {
           "feature-extraction": ElectraModel,
           "fill-mask": ElectraForMaskedLM,
           "question-answering": ElectraForQuestionAnswering,
           "text-classification": ElectraForSequenceClassification,
           "text-generation": ElectraForCausalLM,
           "token-classification": ElectraForTokenClassification,
           "zero-shot": ElectraForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase	:							List[Any]    =    True







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

      __lowerCamelCase    		=					super()._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				return_labels=_SCREAMING_SNAKE_CASE	)

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







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				hidden_size=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_model_as_decoder(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __lowerCamelCase    		=					type
         self.model_tester.create_and_check_electra_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_masked_lm(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_token_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_pretraining(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_question_answering(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_multiple_choice(*_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					ElectraModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_for_causal_lm(*_SCREAMING_SNAKE_CASE	)






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ElectraModel.from_pretrained('google/electra-small-discriminator'	)
      __lowerCamelCase    		=					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
      __lowerCamelCase    		=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE	)[0]
      __lowerCamelCase    		=					torch.Size((1, 11, 256)	)
      self.assertEqual(output.shape ,				_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					torch.tensor(
          [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]	)

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-4	)	)







"""simple docstring"""







from ..utils import DummyObject, requires_backends






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






