


'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_   :      Tuple				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""input_features"""]
 def __init__(	self ,			__magic_name__=80 ,			__magic_name__=1_60_00 ,			__magic_name__=1_60 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=0.0 ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__ ,			sampling_rate=__magic_name__ ,			padding_value=__magic_name__ ,			return_attention_mask=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							n_fft
      _a      =							hop_length
      _a      =							chunk_length
      _a      =							chunk_length * sampling_rate
      _a      =							self.n_samples // hop_length
      _a      =							sampling_rate
      _a      =							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__magic_name__ ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__magic_name__ ,			norm='slaney' ,			mel_scale='slaney' ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							spectrogram(
          __magic_name__ ,			window_function(self.n_fft ,			'hann'  ) ,			frame_length=self.n_fft ,			hop_length=self.hop_length ,			power=2.0 ,			mel_filters=self.mel_filters ,			log_mel='log10' ,			)
      _a      =							log_spec[:, :-1]
      _a      =							np.maximum(__magic_name__ ,			log_spec.max() - 8.0  )
      _a      =							(log_spec + 4.0) / 4.0
      return log_spec
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ = 0.0  )   ->							List[np.ndarray]:
      if attention_mask is not None:
           _a      =							np.array(__magic_name__ ,			np.intaa  )
           _a      =							[]

           for vector, length in zip(__magic_name__ ,			attention_mask.sum(-1  )  ):
                _a      =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7  )
                if length < normed_slice.shape[0]:
                     _a      =							padding_value

                normed_input_values.append(__magic_name__  )
      else:
           _a      =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7  ) for x in input_values]

      return normed_input_values
 def __call__(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = "max_length" ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							BatchFeature:

      if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'  )
      else:
           logger.warning(
               'It is strongly recommended to pass the `sampling_rate` argument to this function. '
               'Failing to do so can result in silent errors that might be hard to debug.'  )

      _a      =							isinstance(__magic_name__ ,			np.ndarray  ) and len(raw_speech.shape  ) > 1
      if is_batched_numpy and len(raw_speech.shape  ) > 2:
           raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
      _a      =							is_batched_numpy or (
          isinstance(__magic_name__ ,			(list, tuple)  ) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)  ))
      )

      if is_batched:
           _a      =							[np.asarray([speech] ,			dtype=np.floataa  ).T for speech in raw_speech]
      elif not is_batched and not isinstance(__magic_name__ ,			np.ndarray  ):
           _a      =							np.asarray(__magic_name__ ,			dtype=np.floataa  )
      elif isinstance(__magic_name__ ,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
           _a      =							raw_speech.astype(np.floataa  )

      # always return batch
      if not is_batched:
           _a      =							[np.asarray([raw_speech]  ).T]

      _a      =							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a      =							self.pad(
          __magic_name__ ,			padding=__magic_name__ ,			max_length=max_length if max_length else self.n_samples ,			truncation=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=return_attention_mask or do_normalize ,			)

      # zero-mean and unit-variance normalization
      if do_normalize:
           _a      =							self.zero_mean_unit_var_norm(
               padded_inputs['input_features'] ,			attention_mask=padded_inputs['attention_mask'] ,			padding_value=self.padding_value ,			)
           _a      =							np.stack(padded_inputs['input_features'] ,			axis=0  )

      # make sure list is in array format
      _a      =							padded_inputs.get('input_features'  ).transpose(2 ,			0 ,			1  )

      _a      =							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

      if isinstance(input_features[0] ,			__magic_name__  ):
           _a      =							[np.asarray(__magic_name__ ,			dtype=np.floataa  ) for feature in input_features]
      else:
           _a      =							input_features

      if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           _a      =							padded_inputs['attention_mask'][:, :: self.hop_length]

      if return_tensors is not None:
           _a      =							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(	self  )   ->							Dict[str, Any]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output





'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=False ,			__magic_name__=True ,			__magic_name__="None" ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							Any:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_token_type_ids
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							type_vocab_size
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							num_labels
      _a      =							num_choices
      _a      =							relative_attention
      _a      =							position_biased_input
      _a      =							pos_att_type
      _a      =							scope
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

      _a      =							None
      if self.use_input_mask:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2  )

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

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

      _a      =							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return DebertaConfig(
          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 ,			initializer_range=self.initializer_range ,			relative_attention=self.relative_attention ,			position_biased_input=self.position_biased_input ,			pos_att_type=self.pos_att_type ,			)
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.get_config()
      _a      =							3_00
      return config
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      self.parent.assertListEqual(list(result.loss.size()  ) ,			[]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__  )[0]

      self.parent.assertListEqual(list(sequence_output.size()  ) ,			[self.batch_size, self.seq_length, self.hidden_size]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaForMaskedLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )

      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							self.num_labels
      _a      =							DebertaForSequenceClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertListEqual(list(result.logits.size()  ) ,			[self.batch_size, self.num_labels]  )
      self.check_loss_output(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							self.num_labels
      _a      =							DebertaForTokenClassification(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							DebertaForQuestionAnswering(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(
          __magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			start_positions=__magic_name__ ,			end_positions=__magic_name__ ,			)
      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 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.prepare_config_and_inputs()
      (
          (
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				
      )      =							config_and_inputs
      _a      =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							DebertaModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							DebertaModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )





@require_torch
@require_sentencepiece
@require_tokenizers
class 	a       (       unittest.TestCase    ):
 @unittest.skip(reason='Model not available yet'  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      pass




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							DebertaModel.from_pretrained('microsoft/deberta-base'  )

      _a      =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _a      =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      with torch.no_grad():
           _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )[0]
      # compare the actual values for a slice.
      _a      =							torch.tensor(
          [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]  )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,			__magic_name__ ,			atol=1e-4  ) ,			f'{output[:, 1:4, 1:4]}'  )

