





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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       List[Any]    ,  a    :       Dict    ,  a    :       str=13    ,  a    :       List[str]=7    ,  a    :       int=True    ,  a    :       Tuple=True    ,  a    :       str=True    ,  a    :       List[str]=True    ,  a    :       str=99    ,  a    :       str=32    ,  a    :       Tuple=5    ,  a    :       Dict=4    ,  a    :       Optional[Any]=37    ,  a    :       Dict="gelu"    ,  a    :       Optional[int]=0.1    ,  a    :       Any=0.1    ,  a    :       Dict=512    ,  a    :       Any=16    ,  a    :       Tuple=2    ,  a    :       Optional[Any]=0.02    ,  a    :       Any=4    ,  )->     str:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						seq_length
     lowercase__			    =						is_training
     lowercase__			    =						use_attention_mask
     lowercase__			    =						use_token_type_ids
     lowercase__			    =						use_labels
     lowercase__			    =						vocab_size
     lowercase__			    =						hidden_size
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						intermediate_size
     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						max_position_embeddings
     lowercase__			    =						type_vocab_size
     lowercase__			    =						type_sequence_label_size
     lowercase__			    =						initializer_range
     lowercase__			    =						num_choices


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     int:





     """simple docstring"""







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

     lowercase__			    =						None
     if self.use_attention_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__			    =						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=a    ,  initializer_range=self.initializer_range    ,  )

     return config, input_ids, token_type_ids, attention_mask


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__			    =						config_and_inputs
     lowercase__			    =						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
     return config, inputs_dict


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__			    =						config_and_inputs

     lowercase__			    =						True
     lowercase__			    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
     lowercase__			    =						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        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	str				      = True

   _UpperCamelCase					:	List[str]				      = (
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						FlaxRobertaModelTester(self )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """simple docstring"""







     for model_class_name in self.all_model_classes:
       lowercase__			    =						model_class_name.from_pretrained('roberta-base'    ,  from_pt=a )
       lowercase__			    =						model(np.ones((1, 1) ) )
       self.assertIsNotNone(a )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


