






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	):







				"""simple docstring"""




				def __init__( self     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :List[str]=13  ,     lowerCamelCase__     :Any=7  ,     lowerCamelCase__     :Dict=True  ,     lowerCamelCase__     :Union[str, Any]=True  ,     lowerCamelCase__     :Union[str, Any]=True  ,     lowerCamelCase__     :Optional[int]=True  ,     lowerCamelCase__     :List[str]=99  ,     lowerCamelCase__     :Optional[Any]=32  ,     lowerCamelCase__     :Any=5  ,     lowerCamelCase__     :Any=4  ,     lowerCamelCase__     :List[Any]=37  ,     lowerCamelCase__     :Optional[int]="gelu"  ,     lowerCamelCase__     :Tuple=0.1  ,     lowerCamelCase__     :List[Any]=0.1  ,     lowerCamelCase__     :Tuple=5_12  ,     lowerCamelCase__     :int=16  ,     lowerCamelCase__     :Optional[Any]=2  ,     lowerCamelCase__     :Optional[int]=0.02  ,     lowerCamelCase__     :Optional[Any]=4  ,     ):
					UpperCamelCase__ :Union[str, Any]						=      parent
					UpperCamelCase__ :Optional[Any]						=      batch_size
					UpperCamelCase__ :Optional[Any]						=      seq_length
					UpperCamelCase__ :str						=      is_training
					UpperCamelCase__ :int						=      use_attention_mask
					UpperCamelCase__ :Dict						=      use_token_type_ids
					UpperCamelCase__ :int						=      use_labels
					UpperCamelCase__ :List[str]						=      vocab_size
					UpperCamelCase__ :Optional[int]						=      hidden_size
					UpperCamelCase__ :int						=      num_hidden_layers
					UpperCamelCase__ :Optional[int]						=      num_attention_heads
					UpperCamelCase__ :Dict						=      intermediate_size
					UpperCamelCase__ :str						=      hidden_act
					UpperCamelCase__ :List[Any]						=      hidden_dropout_prob
					UpperCamelCase__ :List[str]						=      attention_probs_dropout_prob
					UpperCamelCase__ :Optional[int]						=      max_position_embeddings
					UpperCamelCase__ :Dict						=      type_vocab_size
					UpperCamelCase__ :Dict						=      type_sequence_label_size
					UpperCamelCase__ :Dict						=      initializer_range
					UpperCamelCase__ :Union[str, Any]						=      num_choices







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Any						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )

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

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

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







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Dict						=      self.prepare_config_and_inputs()
					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      config_and_inputs

					UpperCamelCase__ :Optional[Any]						=      True
					UpperCamelCase__ :Any						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
					UpperCamelCase__ :List[str]						=      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_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Any        =			True

				_snake_case				:   Union[str, Any]        =			(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Optional[int]						=      FlaxRobertaModelTester(self     )







				@slow
				def 				__a   ( self     :Any     ):
					for model_class_name in self.all_model_classes:
						UpperCamelCase__ :int						=      model_class_name.from_pretrained("""roberta-base"""  ,     from_pt=lowerCamelCase__     )
						UpperCamelCase__ :Optional[Any]						=      model(np.ones((1, 1)     )     )
						self.assertIsNotNone(lowerCamelCase__     )




'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


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




