

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    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    str			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[Any]=1_3			,						lowerCamelCase__	:    Tuple=7			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    Union[str, Any]=9_9			,						lowerCamelCase__	:    Optional[Any]=3_2			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    str=4			,						lowerCamelCase__	:    Tuple=3_7			,						lowerCamelCase__	:    List[str]="gelu"			,						lowerCamelCase__	:    str=0.1			,						lowerCamelCase__	:    Dict=0.1			,						lowerCamelCase__	:    Dict=5_1_2			,						lowerCamelCase__	:    Tuple=1_6			,						lowerCamelCase__	:    Optional[Any]=2			,						lowerCamelCase__	:    str=0.02			,						lowerCamelCase__	:    str=4			,						) ->			Optional[Any]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    str       ) ->			Union[str, Any]:
         """simple docstring"""





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

         A_							       =		None
         if self.use_attention_mask:
               A_							       =		random_attention_mask([self.batch_size, self.seq_length]       )

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

         A_							       =		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     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Tuple:
         """simple docstring"""





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





   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Any:
         """simple docstring"""





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

         A_							       =		True
         A_							       =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         A_							       =		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    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    True

   _lowercase     :       int  					=    (
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         A_							       =		FlaxRobertaModelTester(self       )





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





