
'''simple docstring'''







import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import 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 (
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetModel,
  )







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Any							,_a      :     Tuple							,_a      :     List[str]=13							,_a      :     str=7							,_a      :     Dict=True							,_a      :     Any=True							,_a      :     Any=False							,_a      :     List[Any]=True							,_a      :     Tuple=99							,_a      :     Optional[int]=64							,_a      :     Dict=5							,_a      :     int=4							,_a      :     Optional[Any]=64							,_a      :     str="gelu"							,_a      :     Dict=0.1							,_a      :     Dict=0.1							,_a      :     Union[str, Any]=512							,_a      :     List[str]=16							,_a      :     int=2							,_a      :     Any=0.02							,_a      :     Dict=3							,_a      :     str=4							,_a      :     Union[str, Any]=None							,):


     '''simple docstring'''






     _a				:	Optional[int]			=   parent
     _a				:	Any			=   batch_size
     _a				:	Optional[int]			=   seq_length
     _a				:	List[str]			=   is_training
     _a				:	Optional[int]			=   use_input_mask
     _a				:	List[Any]			=   use_token_type_ids
     _a				:	List[Any]			=   use_labels
     _a				:	Any			=   vocab_size
     _a				:	Any			=   hidden_size
     _a				:	List[str]			=   num_hidden_layers
     _a				:	Union[str, Any]			=   num_attention_heads
     _a				:	Optional[int]			=   intermediate_size
     _a				:	List[str]			=   hidden_act
     _a				:	Tuple			=   hidden_dropout_prob
     _a				:	Any			=   attention_probs_dropout_prob
     _a				:	Optional[Any]			=   max_position_embeddings
     _a				:	Any			=   type_vocab_size
     _a				:	Union[str, Any]			=   type_sequence_label_size
     _a				:	Optional[Any]			=   initializer_range
     _a				:	Dict			=   num_labels
     _a				:	List[str]			=   num_choices
     _a				:	str			=   scope


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     return MPNetConfig.from_pretrained('microsoft/mpnet-base'       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size       )

     _a				:	int			=   None
     if self.use_input_mask:
        _a				:	List[str]			=   random_attention_mask([self.batch_size, self.seq_length]       )

     _a				:	Tuple			=   None
     _a				:	List[Any]			=   None
     _a				:	Optional[int]			=   None
     if self.use_labels:
        _a				:	Union[str, Any]			=   ids_tensor([self.batch_size]							,self.type_sequence_label_size       )
        _a				:	Optional[Any]			=   ids_tensor([self.batch_size, self.seq_length]							,self.num_labels       )
        _a				:	Dict			=   ids_tensor([self.batch_size]							,self.num_choices       )

     _a				:	str			=   self.get_config()
     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     return MPNetConfig(
         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							,initializer_range=self.initializer_range							,)


  def   __lowercase		(		self      :     List[str]							,_a      :     Tuple							,_a      :     Union[str, Any]							,_a      :     Union[str, Any]							,_a      :     Dict							,_a      :     Optional[int]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   MPNetModel(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	Optional[int]			=   model(_a							,_a       )
     _a				:	Dict			=   model(_a       )
     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   __lowercase		(		self      :     Optional[int]							,_a      :     str							,_a      :     str							,_a      :     Union[str, Any]							,_a      :     Optional[Any]							,_a      :     List[str]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Any			=   MPNetForQuestionAnswering(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	Optional[Any]			=   model(
         _a							,attention_mask=_a							,start_positions=_a							,end_positions=_a							,)
     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   __lowercase		(		self      :     str							,_a      :     List[str]							,_a      :     List[str]							,_a      :     List[str]							,_a      :     int							,_a      :     Tuple							,_a      :     List[str]       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.num_labels
     _a				:	Optional[Any]			=   MPNetForSequenceClassification(_a       )
     model.to(_a       )
     model.eval()
     _a				:	Optional[Any]			=   model(_a							,attention_mask=_a							,labels=_a       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)       )


  def   __lowercase		(		self      :     Tuple							,_a      :     Any							,_a      :     Any							,_a      :     Tuple							,_a      :     Optional[int]							,_a      :     Optional[Any]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.num_choices
     _a				:	Optional[int]			=   MPNetForMultipleChoice(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	Optional[int]			=   input_ids.unsqueeze(1       ).expand(-1							,self.num_choices							,-1       ).contiguous()
     _a				:	Optional[int]			=   input_mask.unsqueeze(1       ).expand(-1							,self.num_choices							,-1       ).contiguous()
     _a				:	Any			=   model(
         _a							,attention_mask=_a							,labels=_a							,)
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_choices)       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     Optional[Any]							,_a      :     int							,_a      :     Tuple							,_a      :     Dict							,_a      :     int       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.num_labels
     _a				:	List[str]			=   MPNetForTokenClassification(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	Dict			=   model(_a							,attention_mask=_a							,labels=_a       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.num_labels)       )


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


     '''simple docstring'''






     _a				:	Optional[Any]			=   self.prepare_config_and_inputs()
     ((_a),  (_a),  (_a),  (_a),  (_a),  (_a))				:	Dict			=   config_and_inputs
     _a				:	List[Any]			=   {'input_ids': input_ids, 'attention_mask': input_mask}
     return config, inputs_dict







@require_torch
class UpperCAmelCase__	( lowercase__    ,				lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				int   =					(
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  __UpperCAmelCase				:				List[Any]   =					(
      {
          '''feature-extraction''': MPNetModel,
          '''fill-mask''': MPNetForMaskedLM,
          '''question-answering''': MPNetForQuestionAnswering,
          '''text-classification''': MPNetForSequenceClassification,
          '''token-classification''': MPNetForTokenClassification,
          '''zero-shot''': MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __UpperCAmelCase				:				Tuple   =					False
  __UpperCAmelCase				:				Union[str, Any]   =					True


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   MPNetModelTester(self       )
     _a				:	int			=   ConfigTester(self							,config_class=_a							,hidden_size=37       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     self.config_tester.run_common_tests()


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	int			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*_a       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Any			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*_a       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	int			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Dict			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*_a       )







@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @slow
  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   MPNetModel.from_pretrained('microsoft/mpnet-base'       )
     _a				:	Dict			=   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
     _a				:	Optional[int]			=   model(_a       )[0]
     _a				:	str			=   torch.Size((1, 11, 768)       )
     self.assertEqual(output.shape							,_a       )
     _a				:	Dict			=   torch.tensor(
         [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]       )
     # compare the actual values for a slice.
     self.assertTrue(torch.allclose(output[:, :3, :3]							,_a							,atol=1E-4       )       )





'''simple docstring'''







import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Union[str, Any]   =					None
  __UpperCAmelCase				:				List[Any]   =					None


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return self.feat_extract_tester.prepare_feat_extract_dict()


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     self.assertTrue(hasattr(_a							,'feature_size'       )       )
     self.assertTrue(hasattr(_a							,'sampling_rate'       )       )
     self.assertTrue(hasattr(_a							,'padding_value'       )       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Any			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	str			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	int			=   feat_extract.model_input_names[0]

     _a				:	List[Any]			=   BatchFeature({input_name: speech_inputs}       )

     self.assertTrue(all(len(_a       ) == len(_a       ) for x, y in zip(_a							,processed_features[input_name]       )       )       )

     _a				:	Any			=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a       )
     _a				:	Union[str, Any]			=   BatchFeature({input_name: speech_inputs}							,tensor_type='np'       )

     _a				:	Union[str, Any]			=   processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
        _a				:	Optional[int]			=   batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.feature_size)       )


  @require_torch
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a       )
     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	int			=   feat_extract.model_input_names[0]

     _a				:	str			=   BatchFeature({input_name: speech_inputs}							,tensor_type='pt'       )

     _a				:	str			=   processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
        _a				:	str			=   batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.feature_size)       )


  @require_tf
  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a       )
     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Tuple			=   feat_extract.model_input_names[0]

     _a				:	int			=   BatchFeature({input_name: speech_inputs}							,tensor_type='tf'       )

     _a				:	Optional[int]			=   processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
        _a				:	Optional[Any]			=   batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.feature_size)       )


  def   __lowercase		(		self      :     Dict							,_a      :     Any=False       ):


     '''simple docstring'''






     def _inputs_have_equal_length(_a      :     Tuple       ):
        _a				:	Tuple			=   len(input[0]       )
        for input_slice in input[1:]:
           if len(_a       ) != length:
              return False
        return True

     def _inputs_are_equal(_a      :     Optional[Any]							,_a      :     Union[str, Any]       ):
        if len(_a       ) != len(_a       ):
           return False

        for input_slice_a, input_slice_a in zip(_a							,_a       ):
           if not np.allclose(np.asarray(_a       )							,np.asarray(_a       )							,atol=1E-3       ):
              return False
        return True

     _a				:	int			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Tuple			=   self.feat_extract_tester.prepare_inputs_for_common(numpify=_a       )
     _a				:	Union[str, Any]			=   feat_extract.model_input_names[0]

     _a				:	Tuple			=   BatchFeature({input_name: speech_inputs}       )

     _a				:	str			=   self.feat_extract_tester.seq_length_diff
     _a				:	Dict			=   self.feat_extract_tester.max_seq_length + pad_diff
     _a				:	Dict			=   self.feat_extract_tester.min_seq_length
     _a				:	Optional[Any]			=   self.feat_extract_tester.batch_size
     _a				:	Tuple			=   self.feat_extract_tester.feature_size

     # test padding for List[int] + numpy
     _a				:	int			=   feat_extract.pad(_a							,padding=_a       )
     _a				:	List[Any]			=   input_a[input_name]

     _a				:	Tuple			=   feat_extract.pad(_a							,padding='longest'       )
     _a				:	Any			=   input_a[input_name]

     _a				:	Optional[Any]			=   feat_extract.pad(_a							,padding='max_length'							,max_length=len(speech_inputs[-1]       )       )
     _a				:	List[str]			=   input_a[input_name]

     _a				:	List[str]			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )
     _a				:	str			=   input_a[input_name]

     # max_length parameter has to be provided when setting `padding="max_length"`
     with self.assertRaises(_a       ):
        feat_extract.pad(_a							,padding='max_length'       )[input_name]

     _a				:	int			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=_a							,return_tensors='np'       )
     _a				:	Optional[int]			=   input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_are_equal(_a							,_a       )       )
     self.assertTrue(len(input_a[0]       ) == pad_min_length       )
     self.assertTrue(len(input_a[1]       ) == pad_min_length + pad_diff       )
     self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]       ))       )
     self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)       )

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size       )

     # test padding for `pad_to_multiple_of` for List[int] + numpy
     _a				:	Tuple			=   feat_extract.pad(_a							,pad_to_multiple_of=10       )
     _a				:	List[str]			=   input_a[input_name]

     _a				:	str			=   feat_extract.pad(_a							,padding='longest'							,pad_to_multiple_of=10       )
     _a				:	Tuple			=   input_a[input_name]

     _a				:	Optional[int]			=   feat_extract.pad(
         _a							,padding='max_length'							,pad_to_multiple_of=10							,max_length=_a       )
     _a				:	Any			=   input_a[input_name]

     _a				:	Optional[int]			=   feat_extract.pad(
         _a							,padding='max_length'							,pad_to_multiple_of=10							,max_length=_a							,return_tensors='np'							,)
     _a				:	Dict			=   input_a[input_name]

     self.assertTrue(all(len(_a       ) % 10 == 0 for x in input_a       )       )
     self.assertTrue(_inputs_are_equal(_a							,_a       )       )

     _a				:	List[str]			=   pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
     self.assertTrue(all(len(_a       ) == expected_mult_pad_length for x in input_a       )       )
     self.assertEqual(input_a.shape[:2]							,(batch_size, expected_mult_pad_length)       )

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size       )

     # Check padding value is correct
     _a				:	Any			=   (np.ones(self.feat_extract_tester.feature_size       ) * feat_extract.padding_value).sum()
     self.assertTrue(
         abs(np.asarray(input_a[0]       )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)       )
         < 1E-3       )
     self.assertTrue(
         abs(
             np.asarray(input_a[1]       )[pad_min_length + pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)       )
         < 1E-3       )
     self.assertTrue(
         abs(
             np.asarray(input_a[2]       )[pad_min_length + 2 * pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)       )
         < 1E-3       )
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)       ) < 1E-3       )
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)       )
         < 1E-3       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     Optional[int]=False       ):


     '''simple docstring'''






     def _inputs_have_equal_length(_a      :     List[str]       ):
        _a				:	Union[str, Any]			=   len(input[0]       )
        for input_slice in input[1:]:
           if len(_a       ) != length:
              return False
        return True

     def _inputs_are_equal(_a      :     List[str]							,_a      :     List[str]       ):
        if len(_a       ) != len(_a       ):
           return False

        for input_slice_a, input_slice_a in zip(_a							,_a       ):
           if not np.allclose(np.asarray(_a       )							,np.asarray(_a       )							,atol=1E-3       ):
              return False
        return True

     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	str			=   self.feat_extract_tester.prepare_inputs_for_common(numpify=_a       )
     _a				:	Any			=   feat_extract.model_input_names[0]

     _a				:	List[Any]			=   BatchFeature({input_name: speech_inputs}       )

     # truncate to smallest
     _a				:	Union[str, Any]			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,truncation=_a       )
     _a				:	str			=   input_a[input_name]

     _a				:	List[str]			=   feat_extract.pad(_a							,padding='max_length'							,max_length=len(speech_inputs[0]       )       )
     _a				:	Tuple			=   input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertFalse(_inputs_have_equal_length(_a       )       )

     # truncate to smallest with np
     _a				:	Dict			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,return_tensors='np'							,truncation=_a							,)
     _a				:	Any			=   input_a[input_name]

     _a				:	List[Any]			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,return_tensors='np'       )
     _a				:	int			=   input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(input_a.shape[1] == len(speech_inputs[0]       )       )

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(_a       )       )

     # truncate to middle
     _a				:	Dict			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,truncation=_a							,return_tensors='np'							,)
     _a				:	List[Any]			=   input_a[input_name]

     _a				:	Tuple			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,truncation=_a       )
     _a				:	Tuple			=   input_a[input_name]

     _a				:	Tuple			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,return_tensors='np'       )
     _a				:	Dict			=   input_a[input_name]

     self.assertTrue(input_a.shape[1] == len(speech_inputs[1]       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_are_equal(_a							,_a       )       )

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(_a       )       )
     self.assertTrue(len(input_a[-1]       ) == len(speech_inputs[-1]       )       )

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(_a       ):
        feat_extract.pad(_a							,truncation=_a       )[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(_a       ):
        feat_extract.pad(_a							,padding='longest'							,truncation=_a       )[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(_a       ):
        feat_extract.pad(_a							,padding='longest'							,truncation=_a       )[input_name]

     # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
     with self.assertRaises(_a       ):
        feat_extract.pad(_a							,padding='max_length'							,truncation=_a       )[input_name]

     # test truncation for `pad_to_multiple_of` for List[int] + numpy
     _a				:	Optional[Any]			=   12
     _a				:	List[Any]			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,pad_to_multiple_of=_a							,truncation=_a							,)
     _a				:	Tuple			=   input_a[input_name]

     _a				:	str			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,pad_to_multiple_of=_a							,)
     _a				:	List[Any]			=   input_a[input_name]

     # retrieve expected_length as multiple of pad_to_multiple_of
     _a				:	List[Any]			=   len(speech_inputs[0]       )
     if expected_length % pad_to_multiple_of != 0:
        _a				:	Union[str, Any]			=   ((len(speech_inputs[0]       ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

     self.assertTrue(len(input_a[0]       ) == expected_length       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertFalse(_inputs_have_equal_length(_a       )       )


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


     '''simple docstring'''






     self._check_padding(numpify=_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     self._check_padding(numpify=_a       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     self._check_truncation(numpify=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     self._check_truncation(numpify=_a       )


  @require_torch
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Any			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	List[Any]			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Union[str, Any]			=   feat_extract.model_input_names[0]

     _a				:	Optional[int]			=   BatchFeature({input_name: speech_inputs}       )

     _a				:	List[Any]			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )[input_name]
     _a				:	List[str]			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='pt'       )[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_pt.numpy().astype(np.floataa       ).sum()       ) < 1E-2       )


  @require_tf
  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Optional[Any]			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Dict			=   feat_extract.model_input_names[0]

     _a				:	Optional[Any]			=   BatchFeature({input_name: speech_inputs}       )

     _a				:	Dict			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )[input_name]
     _a				:	Any			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='tf'       )[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_tf.numpy().astype(np.floataa       ).sum()       ) < 1E-2       )


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


     '''simple docstring'''






     _a				:	str			=   self.feat_extract_dict
     _a				:	List[Any]			=   True
     _a				:	Optional[int]			=   self.feature_extraction_class(**_a       )
     _a				:	List[Any]			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Tuple			=   [len(_a       ) for x in speech_inputs]
     _a				:	int			=   feat_extract.model_input_names[0]

     _a				:	Optional[Any]			=   BatchFeature({input_name: speech_inputs}       )

     _a				:	str			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )
     self.assertIn('attention_mask'							,_a       )
     self.assertListEqual(list(processed.attention_mask.shape       )							,list(processed[input_name].shape[:2]       )       )
     self.assertListEqual(processed.attention_mask.sum(-1       ).tolist()							,_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Any			=   self.feat_extract_dict
     _a				:	Tuple			=   True
     _a				:	Optional[int]			=   self.feature_extraction_class(**_a       )
     _a				:	Dict			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Dict			=   [len(_a       ) for x in speech_inputs]
     _a				:	Union[str, Any]			=   feat_extract.model_input_names[0]

     _a				:	Any			=   BatchFeature({input_name: speech_inputs}       )
     _a				:	List[Any]			=   min(_a       )

     _a				:	Dict			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=_a							,truncation=_a							,return_tensors='np'       )
     self.assertIn('attention_mask'							,_a       )
     self.assertListEqual(
         list(processed_pad.attention_mask.shape       )							,[processed_pad[input_name].shape[0], max_length]       )
     self.assertListEqual(
         processed_pad.attention_mask[:, :max_length].sum(-1       ).tolist()							,[max_length for x in speech_inputs]       )



