
import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MegatronBertForCausalLM,
							    MegatronBertForMaskedLM,
							    MegatronBertForMultipleChoice,
							    MegatronBertForNextSentencePrediction,
							    MegatronBertForPreTraining,
							    MegatronBertForQuestionAnswering,
							    MegatronBertForSequenceClassification,
							    MegatronBertForTokenClassification,
							    MegatronBertModel,
							)

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Tuple						,	snake_case_ :       str						,	snake_case_ :       int=13						,	snake_case_ :       str=7						,	snake_case_ :       Any=True						,	snake_case_ :       List[Any]=True						,	snake_case_ :       str=True						,	snake_case_ :       int=True						,	snake_case_ :       Any=99						,	snake_case_ :       List[Any]=64						,	snake_case_ :       List[str]=32						,	snake_case_ :       Any=5						,	snake_case_ :       List[str]=4						,	snake_case_ :       List[Any]=37						,	snake_case_ :       Dict="gelu"						,	snake_case_ :       Union[str, Any]=0.1						,	snake_case_ :       Optional[Any]=0.1						,	snake_case_ :       List[Any]=512						,	snake_case_ :       Optional[Any]=16						,	snake_case_ :       str=2						,	snake_case_ :       str=0.02						,	snake_case_ :       List[str]=3						,	snake_case_ :       List[Any]=4						,	snake_case_ :       Optional[int]=None						,	):
							UpperCamelCase_:							Optional[Any]	=						parent
							UpperCamelCase_:							str	=						batch_size
							UpperCamelCase_:							Optional[Any]	=						seq_length
							UpperCamelCase_:							str	=						is_training
							UpperCamelCase_:							Any	=						use_input_mask
							UpperCamelCase_:							int	=						use_token_type_ids
							UpperCamelCase_:							Optional[int]	=						use_labels
							UpperCamelCase_:							Optional[int]	=						vocab_size
							UpperCamelCase_:							List[str]	=						hidden_size
							UpperCamelCase_:							Union[str, Any]	=						embedding_size
							UpperCamelCase_:							Any	=						num_hidden_layers
							UpperCamelCase_:							Any	=						num_attention_heads
							UpperCamelCase_:							str	=						intermediate_size
							UpperCamelCase_:							Dict	=						hidden_act
							UpperCamelCase_:							List[Any]	=						hidden_dropout_prob
							UpperCamelCase_:							Optional[int]	=						attention_probs_dropout_prob
							UpperCamelCase_:							str	=						max_position_embeddings
							UpperCamelCase_:							Optional[Any]	=						type_vocab_size
							UpperCamelCase_:							List[Any]	=						type_sequence_label_size
							UpperCamelCase_:							Dict	=						initializer_range
							UpperCamelCase_:							int	=						num_labels
							UpperCamelCase_:							Tuple	=						num_choices
							UpperCamelCase_:							List[Any]	=						scope

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							str	=						ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )

							UpperCamelCase_:							Tuple	=						None
							if self.use_input_mask:
											UpperCamelCase_:							Optional[Any]	=						random_attention_mask([self.batch_size, self.seq_length]      )

							UpperCamelCase_:							int	=						None
							if self.use_token_type_ids:
											UpperCamelCase_:							List[str]	=						ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size      )

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

							UpperCamelCase_:							List[str]	=						self.get_config()

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							return MegatronBertConfig(
							    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						,	embedding_size=self.embedding_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						,	is_decoder=snake_case_						,	initializer_range=self.initializer_range						,	)

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       str						,	snake_case_ :       Tuple						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       str						,	snake_case_ :       Dict						,	snake_case_ :       List[str]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						MegatronBertModel(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Optional[Any]	=						model(snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_      )
							UpperCamelCase_:							Optional[int]	=						model(snake_case_						,	token_type_ids=snake_case_      )
							UpperCamelCase_:							Optional[int]	=						model(snake_case_      )

							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       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       List[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						MegatronBertForMaskedLM(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Optional[int]	=						model(snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       List[str]						,	snake_case_ :       int						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Any						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						MegatronBertForCausalLM(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Any	=						model(snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Tuple						,	snake_case_ :       int						,	snake_case_ :       Optional[int]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							Tuple	=						MegatronBertForNextSentencePrediction(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							int	=						model(
							    snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_						,	)
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, 2)      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Any						,	snake_case_ :       Optional[int]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       str						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[Any]						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							List[Any]	=						MegatronBertForPreTraining(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Optional[Any]	=						model(
							    snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_						,	next_sentence_label=snake_case_						,	)
							self.parent.assertEqual(result.prediction_logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)      )
							self.parent.assertEqual(result.seq_relationship_logits.shape						,	(self.batch_size, 2)      )

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       str						,	snake_case_ :       int						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							List[Any]	=						MegatronBertForQuestionAnswering(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							List[str]	=						model(
							    snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	start_positions=snake_case_						,	end_positions=snake_case_						,	)
							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       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Any						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Dict						,	snake_case_ :       int      ):
							UpperCamelCase_:							List[Any]	=						self.num_labels
							UpperCamelCase_:							List[Any]	=						MegatronBertForSequenceClassification(snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							int	=						model(snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Optional[int]						,	snake_case_ :       int						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       Optional[int]						,	snake_case_ :       Any						,	snake_case_ :       Any      ):
							UpperCamelCase_:							Optional[Any]	=						self.num_labels
							UpperCamelCase_:							Any	=						MegatronBertForTokenClassification(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							List[str]	=						model(snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)      )

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       Tuple						,	snake_case_ :       int						,	snake_case_ :       str						,	snake_case_ :       List[str]						,	snake_case_ :       List[Any]						,	snake_case_ :       Any						,	snake_case_ :       str      ):
							UpperCamelCase_:							Tuple	=						self.num_choices
							UpperCamelCase_:							Any	=						MegatronBertForMultipleChoice(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Optional[int]	=						input_ids.unsqueeze(1      ).expand(-1						,	self.num_choices						,	-1      ).contiguous()
							UpperCamelCase_:							List[str]	=						token_type_ids.unsqueeze(1      ).expand(-1						,	self.num_choices						,	-1      ).contiguous()
							UpperCamelCase_:							Optional[Any]	=						input_mask.unsqueeze(1      ).expand(-1						,	self.num_choices						,	-1      ).contiguous()
							UpperCamelCase_:							Any	=						model(
							    snake_case_						,	attention_mask=snake_case_						,	token_type_ids=snake_case_						,	labels=snake_case_						,	)
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							int	=						self.prepare_config_and_inputs()
							(
							    (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   (
							    UpperCamelCase_
							),   
							):							List[str]	=						config_and_inputs
							UpperCamelCase_:							Optional[int]	=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict




@require_torch
class        _UpperCamelCase				(					_A     ,       _A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = (
			    (
			        MegatronBertModel,
			        MegatronBertForMaskedLM,
			        MegatronBertForCausalLM,
			        MegatronBertForMultipleChoice,
			        MegatronBertForNextSentencePrediction,
			        MegatronBertForPreTraining,
			        MegatronBertForQuestionAnswering,
			        MegatronBertForSequenceClassification,
			        MegatronBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__UpperCamelCase      :						int					      = (
			    {
			        """feature-extraction""": MegatronBertModel,
			        """fill-mask""": MegatronBertForMaskedLM,
			        """question-answering""": MegatronBertForQuestionAnswering,
			        """text-classification""": MegatronBertForSequenceClassification,
			        """text-generation""": MegatronBertForCausalLM,
			        """token-classification""": MegatronBertForTokenClassification,
			        """zero-shot""": MegatronBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__UpperCamelCase      :						int					      = True
			# test_resize_embeddings = False
			__UpperCamelCase      :						str					      = False

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Any=False      ):
							UpperCamelCase_:							int	=						super()._prepare_for_class(snake_case_						,	snake_case_						,	return_labels=snake_case_      )

							if return_labels:
											if model_class in get_values(snake_case_      ):
															UpperCamelCase_:							str	=						torch.zeros(
															    (self.model_tester.batch_size, self.model_tester.seq_length)						,	dtype=torch.long						,	device=snake_case_      )
															UpperCamelCase_:							List[str]	=						torch.zeros(
															    self.model_tester.batch_size						,	dtype=torch.long						,	device=snake_case_      )
							return inputs_dict

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Dict	=						MegatronBertModelTester(self      )
							UpperCamelCase_:							int	=						ConfigTester(self						,	config_class=snake_case_						,	hidden_size=37      )

			def       lowerCAmelCase__	(       self :       str      ):
							self.config_tester.run_common_tests()

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_model(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_masked_lm(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Optional[int]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*snake_case_      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							int	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							int	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_pretraining(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Optional[Any]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_question_answering(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*snake_case_      )




			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							str	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_megatron_bert_for_token_classification(*snake_case_      )







def        A__					(						lowerCamelCase						)  ->	List[Any]:
				return torch.tensor(
				    lowerCamelCase     ,    dtype=torch.long     ,    device=lowerCamelCase     ,    )


lowerCamelCase_						: str         =  1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@slow
			@unittest.skip("""Model is not available."""      )
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						"""nvidia/megatron-bert-uncased-345m"""
							if "MYDIR" in os.environ:
											UpperCamelCase_:							Optional[int]	=						os.path.join(os.environ["""MYDIR"""]						,	snake_case_      )
							UpperCamelCase_:							Tuple	=						MegatronBertModel.from_pretrained(snake_case_      )
							model.to(snake_case_      )
							model.half()
							UpperCamelCase_:							Optional[int]	=						_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]      )
							with torch.no_grad():
											UpperCamelCase_:							Optional[int]	=						model(snake_case_      )[0]
							UpperCamelCase_:							Tuple	=						torch.Size((1, 9, 1024)      )
							self.assertEqual(output.shape						,	snake_case_      )

							UpperCamelCase_:							Optional[int]	=						[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
							for ii in range(3      ):
											for jj in range(3      ):
															UpperCamelCase_:							Optional[Any]	=						output[0, ii, jj]
															UpperCamelCase_:							Dict	=						expected[3 * ii + jj]
															UpperCamelCase_:							Optional[int]	=						"""ii={} jj={} a={} b={}""".format(snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )
															self.assertTrue(math.isclose(snake_case_						,	snake_case_						,	rel_tol=snake_case_						,	abs_tol=snake_case_      )						,	msg=snake_case_      )








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

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

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							self.assertListEqual(
							    processor.model_input_names[2:]						,	feature_extractor.model_input_names						,	msg="""`processor` and `feature_extractor` model input names do not match"""						,	)






