



'''simple docstring'''



import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)
class 	lowerCamelCase_							(					unittest.TestCase			):







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Dict=13				,    lowerCAmelCase__   :      int=7				,    lowerCAmelCase__   :      List[Any]=True				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      str=True				,    lowerCAmelCase__   :      Union[str, Any]=99				,    lowerCAmelCase__   :      Dict=32				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      int=4				,    lowerCAmelCase__   :      str=37				,    lowerCAmelCase__   :      Dict="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      str=0.1				,    lowerCAmelCase__   :      Any=5_12				,    lowerCAmelCase__   :      List[Any]=16				,    lowerCAmelCase__   :      Tuple=2				,    lowerCAmelCase__   :      Optional[Any]=0.02				,    lowerCAmelCase__   :      Optional[Any]=4				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      int     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Any     					=			use_attention_mask
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      str     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      Any     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      Any     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      str     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Dict     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      Dict     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      str     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Any     					=			num_choices







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_attention_mask:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			RobertaConfig(
													    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				,    is_decoder=lowerCAmelCase__				,    initializer_range=self.initializer_range				,    )

													return config, input_ids, token_type_ids, attention_mask







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
													return config, inputs_dict







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			config_and_inputs

													SCREAMING_SNAKE_CASE					:      str     					=			True
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
													SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor([self.batch_size, self.seq_length]				,    vocab_size=2				)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)


@require_flax
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				True

						_lowerCAmelCase     :   Dict	     =				(
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			FlaxRobertaModelTester(self				)







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													for model_class_name in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_class_name.from_pretrained('''roberta-base'''				,    from_pt=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			model(np.ones((1, 1)				)				)
																				self.assertIsNotNone(lowerCAmelCase__				)









'''simple docstring'''



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 	lowerCamelCase_							(					unittest.TestCase			):







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''laion/clap-htsat-unfused'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()







						def 					__lowercase    (  self   :      Tuple				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_feature_extractor()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor.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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													SCREAMING_SNAKE_CASE					:      str     					=			ClapProcessor.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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_list((3, 10_00)				)

													SCREAMING_SNAKE_CASE					:      Any     					=			feature_extractor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(audios=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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''This is a test string'''

													SCREAMING_SNAKE_CASE					:      int     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

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




