'''simple docstring'''





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowerCAmelCase  :Tuple  				=    random.Random()

if is_torch_available():
 import torch


def     __lowerCAmelCase						(       a_       ,      a_=1.0       ,      a_=None       ,      a_=None		)      ->	List[str]:





     '''simple docstring'''





     if rng is None:
          SCREAMING_SNAKE_CASE					:			Any    	=       global_rng

     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     for batch_idx in range(shape[0]		):
          values.append([]		)
          for _ in range(shape[1]		):
               values[-1].append(rng.random() * scale		)

     return values






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=7    ,							lowercase__=400    ,							lowercase__=2_000    ,							lowercase__=1    ,							lowercase__=0.0    ,							lowercase__=16_000    ,							lowercase__=True    ,							lowercase__=True    ,							)							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       parent
         SCREAMING_SNAKE_CASE					:			List[str]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			str    	=       min_seq_length
         SCREAMING_SNAKE_CASE					:			Any    	=       max_seq_length
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         SCREAMING_SNAKE_CASE					:			Any    	=       feature_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       padding_value
         SCREAMING_SNAKE_CASE					:			int    	=       sampling_rate
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       return_attention_mask
         SCREAMING_SNAKE_CASE					:			Tuple    	=       do_normalize






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }






    def       _UpperCamelCase    (       self    ,							lowercase__=False    ,							lowercase__=False )							-> Optional[Any]:
         def _flatten(lowercase__ ):
              return list(itertools.chain(*lowerCAmelCase__ ) )

         if equal_length:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       floats_list((self.batch_size, self.max_seq_length) )
         else:
              # make sure that inputs increase in size
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                  _flatten(floats_list((x, self.feature_size) ) )
                  for x in range(self.min_seq_length    ,							self.max_seq_length    ,							self.seq_length_diff )
              ]

         if numpify:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [np.asarray(lowerCAmelCase__ ) for x in speech_inputs]

         return speech_inputs






@require_torch
@require_torchaudio
class 				UpperCAmelCase (       _snake_case   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						ASTFeatureExtractor






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       ASTFeatureExtractionTester(self )






    def       _UpperCamelCase    (       self )							-> List[str]:
         # Tests that all call wrap to encode_plus and batch_encode_plus
         SCREAMING_SNAKE_CASE					:			Any    	=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
         # create three inputs of length 800, 1000, and 1200
         SCREAMING_SNAKE_CASE					:			Dict    	=       [floats_list((1, x) )[0] for x in range(800    ,							1_400    ,							200 )]
         SCREAMING_SNAKE_CASE					:			str    	=       [np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs]

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feat_extract(speech_inputs[0]    ,							return_tensors='np' ).input_values
         SCREAMING_SNAKE_CASE					:			Any    	=       feat_extract(np_speech_inputs[0]    ,							return_tensors='np' ).input_values
         self.assertTrue(np.allclose(lowerCAmelCase__    ,							lowerCAmelCase__    ,							atol=1E-3 ) )

         # Test batched
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       feat_extract(lowerCAmelCase__    ,							padding=lowerCAmelCase__    ,							return_tensors='np' ).input_values
         SCREAMING_SNAKE_CASE					:			Tuple    	=       feat_extract(lowerCAmelCase__    ,							padding=lowerCAmelCase__    ,							return_tensors='np' ).input_values
         for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__    ,							lowerCAmelCase__ ):
              self.assertTrue(np.allclose(lowerCAmelCase__    ,							lowerCAmelCase__    ,							atol=1E-3 ) )

         # Test 2-D numpy arrays are batched.
         SCREAMING_SNAKE_CASE					:			int    	=       [floats_list((1, x) )[0] for x in (800, 800, 800)]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       np.asarray(lowerCAmelCase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       feat_extract(lowerCAmelCase__    ,							return_tensors='np' ).input_values
         SCREAMING_SNAKE_CASE					:			int    	=       feat_extract(lowerCAmelCase__    ,							return_tensors='np' ).input_values
         for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__    ,							lowerCAmelCase__ ):
              self.assertTrue(np.allclose(lowerCAmelCase__    ,							lowerCAmelCase__    ,							atol=1E-3 ) )






    @require_torch
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         import torch

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.random.rand(100 ).astype(np.floataa )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       feature_extractor.pad([{'input_values': inputs}]    ,							return_tensors='np' )
              self.assertTrue(np_processed.input_values.dtype == np.floataa )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       feature_extractor.pad([{'input_values': inputs}]    ,							return_tensors='pt' )
              self.assertTrue(pt_processed.input_values.dtype == torch.floataa )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         from datasets import load_dataset

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       load_dataset('hf-internal-testing/librispeech_asr_dummy'    ,							'clean'    ,							split='validation' )
         # automatic decoding with librispeech
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ds.sort('id' ).select(range(lowerCAmelCase__ ) )[:num_samples]['audio']

         return [x["array"] for x in speech_samples]






    @require_torch
    def       _UpperCamelCase    (       self )							-> int:
         # fmt: off
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(
             [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
              -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
              -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
              -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9] )
         # fmt: on

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._load_datasamples(1 )
         SCREAMING_SNAKE_CASE					:			int    	=       ASTFeatureExtractor()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor(lowerCAmelCase__    ,							return_tensors='pt' ).input_values
         self.assertEquals(input_values.shape    ,							(1, 1_024, 128) )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,							lowerCAmelCase__    ,							atol=1E-4 ) )

'''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    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=64    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=64    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=None    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       parent
         SCREAMING_SNAKE_CASE					:			int    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Any    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Any    	=       is_training
         SCREAMING_SNAKE_CASE					:			Any    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			int    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Any    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Tuple    	=       type_vocab_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       type_sequence_label_size
         SCREAMING_SNAKE_CASE					:			int    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_labels
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_choices
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scope






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return MPNetConfig.from_pretrained('microsoft/mpnet-base' )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			str    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			Dict    	=       random_attention_mask([self.batch_size, self.seq_length] )

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

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






    def       _UpperCamelCase    (       self )							-> Tuple:
         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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       MPNetModel(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(lowercase__ )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )
         self.parent.assertEqual(result.pooler_output.shape    ,							(self.batch_size, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       MPNetForQuestionAnswering(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Any    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							start_positions=lowercase__    ,							end_positions=lowercase__    ,							)
         self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length) )
         self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MPNetForSequenceClassification(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_choices
         SCREAMING_SNAKE_CASE					:			Any    	=       MPNetForMultipleChoice(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			str    	=       input_ids.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       input_mask.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__    ,							)
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_choices) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       MPNetForTokenClassification(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
         ((SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE)	,   (SCREAMING_SNAKE_CASE))					:			str    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Dict    	=       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






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




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			int    	=       MPNetModelTester(self )
         SCREAMING_SNAKE_CASE					:			int    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> Any:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase__ )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MPNetModel.from_pretrained('microsoft/mpnet-base' )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.Size((1, 11, 768) )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(
             [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )



