'''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     __magic_name__						(    unittest.TestCase):







			def __init__(	self				:  List[str]				,			lowercase_				:  str				,			lowercase_				:  Optional[int]=13				,			lowercase_				:  Any=7				,			lowercase_				:  Dict=True				,			lowercase_				:  str=True				,			lowercase_				:  List[Any]=True				,			lowercase_				:  str=True				,			lowercase_				:  str=99				,			lowercase_				:  Optional[Any]=32				,			lowercase_				:  Optional[int]=5				,			lowercase_				:  Union[str, Any]=4				,			lowercase_				:  List[Any]=37				,			lowercase_				:  List[Any]="gelu"				,			lowercase_				:  Optional[int]=0.1				,			lowercase_				:  Dict=0.1				,			lowercase_				:  int=512				,			lowercase_				:  Optional[Any]=16				,			lowercase_				:  Tuple=2				,			lowercase_				:  Tuple=0.02				,			lowercase_				:  Dict=4				,			):
										lowercase_					:			Dict										=       parent
										lowercase_					:			Dict										=       batch_size
										lowercase_					:			Union[str, Any]										=       seq_length
										lowercase_					:			Dict										=       is_training
										lowercase_					:			Optional[int]										=       use_attention_mask
										lowercase_					:			Tuple										=       use_token_type_ids
										lowercase_					:			int										=       use_labels
										lowercase_					:			str										=       vocab_size
										lowercase_					:			List[Any]										=       hidden_size
										lowercase_					:			int										=       num_hidden_layers
										lowercase_					:			List[str]										=       num_attention_heads
										lowercase_					:			List[str]										=       intermediate_size
										lowercase_					:			Union[str, Any]										=       hidden_act
										lowercase_					:			Optional[int]										=       hidden_dropout_prob
										lowercase_					:			Tuple										=       attention_probs_dropout_prob
										lowercase_					:			int										=       max_position_embeddings
										lowercase_					:			Union[str, Any]										=       type_vocab_size
										lowercase_					:			Any										=       type_sequence_label_size
										lowercase_					:			Optional[Any]										=       initializer_range
										lowercase_					:			Any										=       num_choices







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			int										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

										lowercase_					:			List[str]										=       None
										if self.use_attention_mask:
																	lowercase_					:			Union[str, Any]										=       random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase_					:			Union[str, Any]										=       None
										if self.use_token_type_ids:
																	lowercase_					:			List[Any]										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

										lowercase_					:			Tuple										=       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 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Dict										=       self.prepare_config_and_inputs()
										lowercase_						,						lowercase_						,						lowercase_						,						lowercase_					:			Dict										=       config_and_inputs
										lowercase_					:			Optional[int]										=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
										return config, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			int										=       self.prepare_config_and_inputs()
										lowercase_						,						lowercase_						,						lowercase_						,						lowercase_					:			Tuple										=       config_and_inputs

										lowercase_					:			List[str]										=       True
										lowercase_					:			Tuple										=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
										lowercase_					:			Optional[Any]										=       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     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       True

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Tuple										=       FlaxRobertaModelTester(self    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										for model_class_name in self.all_model_classes:
																	lowercase_					:			int										=       model_class_name.from_pretrained("""roberta-base"""				,			from_pt=lowercase_    )
																	lowercase_					:			Optional[Any]										=       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__ )





