

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=7							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=99							,       lowerCAmelCase=32							,       lowerCAmelCase=2							,       lowerCAmelCase=4							,       lowerCAmelCase=37							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=512							,       lowerCAmelCase=16							,       lowerCAmelCase=2							,       lowerCAmelCase=0.02							,       lowerCAmelCase=3							,       lowerCAmelCase=4							,       lowerCAmelCase=None							,       )  ->			Any:
       '''simple docstring'''






       _lowercase     	=parent
       _lowercase     	=13
       _lowercase     	=7
       _lowercase     	=True
       _lowercase     	=True
       _lowercase     	=True
       _lowercase     	=True
       _lowercase     	=99
       _lowercase     	=32
       _lowercase     	=2
       _lowercase     	=4
       _lowercase     	=37
       _lowercase     	="gelu"
       _lowercase     	=0.1
       _lowercase     	=0.1
       _lowercase     	=512
       _lowercase     	=16
       _lowercase     	=False
       _lowercase     	=-1
       _lowercase     	=True
       _lowercase     	=2
       _lowercase     	=0.02
       _lowercase     	=3
       _lowercase     	=4
       _lowercase     	=None




  def   A__					(    self )  ->			str:
       '''simple docstring'''






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

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

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

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

       _lowercase     	=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							,       relative_attention=self.relative_attention							,       max_relative_positions=self.max_relative_positions							,       position_biased_input=self.position_biased_input							,       initializer_range=self.initializer_range							,       return_dict=lowerCAmelCase							,       )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Tuple:
       '''simple docstring'''






       _lowercase     	=TFDebertaModel(config=lowerCAmelCase )
       _lowercase     	={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

       _lowercase     	=[input_ids, input_mask]
       _lowercase     	=model(lowerCAmelCase )

       _lowercase     	=model(lowerCAmelCase )

       self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
       '''simple docstring'''






       _lowercase     	=TFDebertaForMaskedLM(config=lowerCAmelCase )
       _lowercase     	={
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       _lowercase     	=model(lowerCAmelCase )
       self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
       '''simple docstring'''






       _lowercase     	=self.num_labels
       _lowercase     	=TFDebertaForSequenceClassification(config=lowerCAmelCase )
       _lowercase     	={
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       _lowercase     	=model(lowerCAmelCase )
       self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
       '''simple docstring'''






       _lowercase     	=self.num_labels
       _lowercase     	=TFDebertaForTokenClassification(config=lowerCAmelCase )
       _lowercase     	={
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       _lowercase     	=model(lowerCAmelCase )
       self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[int]:
       '''simple docstring'''






       _lowercase     	=TFDebertaForQuestionAnswering(config=lowerCAmelCase )
       _lowercase     	={
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       _lowercase     	=model(lowerCAmelCase )
       self.parent.assertEqual(result.start_logits.shape							,       (self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape							,       (self.batch_size, self.seq_length) )




  def   A__					(    self )  ->			str:
       '''simple docstring'''






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





@require_tf
class 			__lowerCAmelCase					( snake_case__      ,      snake_case__      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {
          "feature-extraction": TFDebertaModel,
          "fill-mask": TFDebertaForMaskedLM,
          "question-answering": TFDebertaForQuestionAnswering,
          "text-classification": TFDebertaForSequenceClassification,
          "token-classification": TFDebertaForTokenClassification,
          "zero-shot": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			List[Any]:
       '''simple docstring'''






       _lowercase     	=TFDebertaModelTester(self )
       _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       hidden_size=37 )




  def   A__					(    self )  ->			int:
       '''simple docstring'''






       self.config_tester.run_common_tests()




  def   A__					(    self )  ->			Union[str, Any]:
       '''simple docstring'''






       _lowercase     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			str:
       '''simple docstring'''






       _lowercase     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )




  def   A__					(    self )  ->			List[str]:
       '''simple docstring'''






       _lowercase     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[Any]:
       '''simple docstring'''






       _lowercase     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			str:
       '''simple docstring'''






       _lowercase     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[Any]:
       '''simple docstring'''






       _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
       self.assertIsNotNone(lowerCAmelCase )





@require_tf
class 			__lowerCAmelCase					( unittest.TestCase							):




  @unittest.skip(reason='Model not available yet' )
  def   A__					(    self )  ->			int:
       '''simple docstring'''






       pass




  @slow
  def   A__					(    self )  ->			Optional[Any]:
       '''simple docstring'''






       _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
       _lowercase     	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
       _lowercase     	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
       _lowercase     	=model(lowerCAmelCase							,       attention_mask=lowerCAmelCase )[0]

       _lowercase     	=tf.constant(
           [
               [
                   [-0.59855896, -0.80552566, -0.8462135],
                   [1.4484025, -0.93483794, -0.80593085],
                   [0.3122741, 0.00316059, -1.4131377],
               ]
           ] )
       tf.debugging.assert_near(output[:, 1:4, 1:4]							,       lowerCAmelCase							,       atol=1e-4 )





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	='<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
        _lowercase     	=dict(zip(lowerCAmelCase							,       range(len(lowerCAmelCase ) ) ) )

        _lowercase     	={
            'pad_token': '<pad>',
            'unk_token': '<unk>',
            'bos_token': '<s>',
            'eos_token': '</s>',
        }
        _lowercase     	={
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'return_attention_mask': False,
            'do_normalize': True,
        }

        _lowercase     	=tempfile.mkdtemp()
        _lowercase     	=os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file'] )
        _lowercase     	=os.path.join(self.tmpdirname							,       lowerCAmelCase )
        with open(self.vocab_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )

        with open(self.feature_extraction_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )




  def   A__					(    self							,       **lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self							,       **lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.get_tokenizer()
        _lowercase     	=self.get_feature_extractor()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        processor.save_pretrained(self.tmpdirname )
        _lowercase     	=WavaVecaProcessor.from_pretrained(self.tmpdirname )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=WavaVecaProcessor(tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        _lowercase     	=self.get_tokenizer(bos_token='(BOS)'							,       eos_token='(EOS)' )
        _lowercase     	=self.get_feature_extractor(do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        _lowercase     	=WavaVecaProcessor.from_pretrained(
            self.tmpdirname							,       bos_token='(BOS)'							,       eos_token='(EOS)'							,       do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer_add_kwargs.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor_add_kwargs.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=floats_list((3, 1_000) )

        _lowercase     	=feature_extractor(lowerCAmelCase							,       return_tensors='np' )
        _lowercase     	=processor(lowerCAmelCase							,       return_tensors='np' )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()							,       input_processor[key].sum()							,       delta=1e-2 )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	='This is a test string'

        _lowercase     	=processor(text=lowerCAmelCase )

        _lowercase     	=tokenizer(lowerCAmelCase )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]							,       encoded_processor[key] )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase     	=processor.batch_decode(lowerCAmelCase )
        _lowercase     	=tokenizer.batch_decode(lowerCAmelCase )

        self.assertListEqual(lowerCAmelCase							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        self.assertListEqual(
            processor.model_input_names							,       feature_extractor.model_input_names							,       msg='`processor` and `feature_extractor` model input names do not match'							,       )


