

"""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     a  (				unittest.TestCase ):



    """simple docstring"""



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






         """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:       Tuple				):






         """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=lowercase_			,  initializer_range=self.initializer_range			,  )

         return config, input_ids, token_type_ids, attention_mask

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






         """simple docstring"""


         A__								=     self.prepare_config_and_inputs()
         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[int]				):






         """simple docstring"""


         A__								=     self.prepare_config_and_inputs()
         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     a  (				_UpperCAmelCase,		unittest.TestCase ):



    """simple docstring"""



    UpperCAmelCase         =    True

    UpperCAmelCase         =    (
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )

    def  UpperCamelCase		(   self:       Dict				):






         """simple docstring"""


         A__								=     FlaxRobertaModelTester(self				)





    @slow
    def  UpperCamelCase		(   self:       List[Any]				):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
              A__								=     model_class_name.from_pretrained("""roberta-base"""			,  from_pt=lowercase_				)
              A__								=     model(np.ones((1, 1)				)				)
              self.assertIsNotNone(lowercase_				)







'''simple docstring'''




_lowercase						: str						=						tuple[float, float, float]
_lowercase						: List[Any]						=						tuple[float, float, float]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad )			->      Vectorad:
							lowercase_					:			List[str]										=       end_pointa[0] - end_pointa[0]
							lowercase_					:			Union[str, Any]										=       end_pointa[1] - end_pointa[1]
							lowercase_					:			List[Any]										=       end_pointa[2] - end_pointa[2]
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					Vectorad )			->      Vectorad:
							lowercase_					:			List[Any]										=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							lowercase_					:			Union[str, Any]										=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							lowercase_					:			List[str]										=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					int )			->      bool:
							return tuple(round(UpperCAmelCase__      ,      UpperCAmelCase__ ) for x in vector ) == (0, 0, 0)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					int = 10 )			->      bool:
							lowercase_					:			Dict										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      UpperCAmelCase__ )





