


from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFFunnelBaseModel,
       TFFunnelForMaskedLM,
       TFFunnelForMultipleChoice,
       TFFunnelForPreTraining,
       TFFunnelForQuestionAnswering,
       TFFunnelForSequenceClassification,
       TFFunnelForTokenClassification,
       TFFunnelModel,
   )







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=7		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=99		,				_UpperCAmelCase=[1, 1, 2]		,				_UpperCAmelCase=1		,				_UpperCAmelCase=32		,				_UpperCAmelCase=4		,				_UpperCAmelCase=8		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu_new"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=3		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=False		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				seq_length
       snake_case_	=				is_training
       snake_case_	=				use_input_mask
       snake_case_	=				use_token_type_ids
       snake_case_	=				use_labels
       snake_case_	=				vocab_size
       snake_case_	=				block_sizes
       snake_case_	=				num_decoder_layers
       snake_case_	=				d_model
       snake_case_	=				n_head
       snake_case_	=				d_head
       snake_case_	=				d_inner
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				max_position_embeddings
       snake_case_	=				type_vocab_size
       snake_case_	=				2
       snake_case_	=				num_labels
       snake_case_	=				num_choices
       snake_case_	=				scope
       snake_case_	=				initializer_std

       # Used in the tests to check the size of the first attention layer
       snake_case_	=				n_head
       # Used in the tests to check the size of the first hidden state
       snake_case_	=				self.d_model
       # Used in the tests to check the number of output hidden states/attentions
       snake_case_	=				sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
       # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
       # the last hidden state of the first block (which is the first hidden state of the decoder).
       if not base:
             snake_case_	=				self.num_hidden_layers + 2



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size )

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

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

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

       snake_case_	=				FunnelConfig(
           vocab_size=self.vocab_size		,				block_sizes=self.block_sizes		,				num_decoder_layers=self.num_decoder_layers		,				d_model=self.d_model		,				n_head=self.n_head		,				d_head=self.d_head		,				d_inner=self.d_inner		,				hidden_act=self.hidden_act		,				hidden_dropout=self.hidden_dropout		,				attention_dropout=self.attention_dropout		,				activation_dropout=self.activation_dropout		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				initializer_std=self.initializer_std		,				)

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				TFFunnelModel(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )

       snake_case_	=				[input_ids, input_mask]
       snake_case_	=				model(lowercase_ )

       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.d_model) )

       snake_case_	=				False
       snake_case_	=				TFFunnelModel(config=lowercase_ )
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.d_model) )

       snake_case_	=				False
       snake_case_	=				TFFunnelModel(config=lowercase_ )
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.d_model) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				TFFunnelBaseModel(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )

       snake_case_	=				[input_ids, input_mask]
       snake_case_	=				model(lowercase_ )

       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, 2, self.d_model) )

       snake_case_	=				False
       snake_case_	=				TFFunnelBaseModel(config=lowercase_ )
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, 3, self.d_model) )

       snake_case_	=				False
       snake_case_	=				TFFunnelBaseModel(config=lowercase_ )
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, 2, self.d_model) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				TFFunnelForPreTraining(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				TFFunnelForMaskedLM(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				self.num_labels
       snake_case_	=				TFFunnelForSequenceClassification(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				self.num_choices
       snake_case_	=				TFFunnelForMultipleChoice(config=lowercase_ )
       snake_case_	=				tf.tile(tf.expand_dims(lowercase_		,				1 )		,				(1, self.num_choices, 1) )
       snake_case_	=				tf.tile(tf.expand_dims(lowercase_		,				1 )		,				(1, self.num_choices, 1) )
       snake_case_	=				tf.tile(tf.expand_dims(lowercase_		,				1 )		,				(1, self.num_choices, 1) )
       snake_case_	=				{
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
           '''token_type_ids''': multiple_choice_token_type_ids,
       }
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				self.num_labels
       snake_case_	=				TFFunnelForTokenClassification(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(lowercase_ )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				TFFunnelForQuestionAnswering(config=lowercase_ )
       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       snake_case_	=				model(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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       (
           (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   
       )	=				config_and_inputs
       snake_case_	=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_tf
class lowerCAmelCase_						(			_lowerCamelCase ,						_lowerCamelCase ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (
         TFFunnelModel,
         TFFunnelForMaskedLM,
         TFFunnelForPreTraining,
         TFFunnelForQuestionAnswering,
         TFFunnelForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 __snake_case												=  (
     {
         "feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
         "fill-mask": TFFunnelForMaskedLM,
         "question-answering": TFFunnelForQuestionAnswering,
         "text-classification": TFFunnelForSequenceClassification,
         "token-classification": TFFunnelForTokenClassification,
         "zero-shot": TFFunnelForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				TFFunnelModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=lowercase_ )



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowercase_ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowercase_ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowercase_ )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase_ )







@require_tf
class lowerCAmelCase_						(			_lowerCamelCase ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
 )
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				TFFunnelModelTester(self		,				base=lowercase_ )
       snake_case_	=				ConfigTester(self		,				config_class=lowercase_ )



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_base_model(*lowercase_ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  None
 __snake_case												=  None



 @property
 def 						UpperCamelCase__					(     self ):
       return self.feat_extract_tester.prepare_feat_extract_dict()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''feature_size''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''sampling_rate''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''padding_value''' ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

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

       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''np''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

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

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

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

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

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

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				self.feat_extract_tester.seq_length_diff
       snake_case_	=				self.feat_extract_tester.max_seq_length + pad_diff
       snake_case_	=				self.feat_extract_tester.min_seq_length
       snake_case_	=				self.feat_extract_tester.batch_size
       snake_case_	=				self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest''' )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[-1] ) )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

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

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )
       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
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

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

       snake_case_	=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(_UpperCAmelCase ) == 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
       snake_case_	=				(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 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

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

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       # truncate to smallest
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] ) )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

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

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       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(_UpperCAmelCase ) )

       # truncate to middle
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

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

       # 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(_UpperCAmelCase ) )
       self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

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

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

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

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

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

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       snake_case_	=				len(speech_inputs[0] )
       if expected_length % pad_to_multiple_of != 0:
             snake_case_	=				((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(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

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





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )
       snake_case_	=				min(_UpperCAmelCase )

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       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] )