




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
     import jax.numpy as jnp

     from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormModel,
     )





class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

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


    def   a (			self	:	Union[str, Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size  )

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

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

         lowerCAmelCase__            =					RobertaPreLayerNormConfig(
             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=SCREAMING_SNAKE_CASE__  ,      initializer_range=self.initializer_range  ,      )

         return config, input_ids, token_type_ids, attention_mask


    def   a (			self	:	List[str]  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs
         lowerCAmelCase__            =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict


    def   a (			self	:	Optional[Any]  ) ->			Dict:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs

         lowerCAmelCase__            =					True
         lowerCAmelCase__            =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         lowerCAmelCase__            =					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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = True

    snake_case__   = (
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )


    def   a (			self	:	int  ) ->			Dict:
         lowerCAmelCase__            =					FlaxRobertaPreLayerNormModelTester(self  )


    @slow
    def   a (			self	:	Tuple  ) ->			Union[str, Any]:
         for model_class_name in self.all_model_classes:
              lowerCAmelCase__            =					model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,      from_pt=SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					model(np.ones((1, 1)  )  )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






@require_flax
class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    @slow
    def   a (			self	:	int  ) ->			Dict:
         lowerCAmelCase__            =					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,      from_pt=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )[0]
         lowerCAmelCase__            =					[1, 11, 50_265]
         self.assertEqual(list(output.shape  )  ,      SCREAMING_SNAKE_CASE__  )
         # compare the actual values for a slice.
         lowerCAmelCase__            =					np.array(
             [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]  ,      dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )


    @slow
    def   a (			self	:	Union[str, Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,      from_pt=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )[0]
         # compare the actual values for a slice.
         lowerCAmelCase__            =					np.array(
             [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]  ,      dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )


'''simple docstring'''
from __future__ import annotations






def      UpperCamelCase_				(      A__						):
	a_      				=						[True] * limit
	a_      				=						False
	a_      				=						False
	a_      				=						True

	for i in range(3  ,      int(limit**0.5 + 1						)  ,      2						):
		a_      				=						i * 2
		while index < limit:
			a_      				=						False
			a_      				=						index + i

	a_      				=						[2]

	for i in range(3  ,      A__  ,      2						):
		if is_prime[i]:
			primes.append(A__						)

	return primes






def      UpperCamelCase_				(      A__ = 1_00_00_00						):
	a_      				=						prime_sieve(A__						)
	a_      				=						0
	a_      				=						0

	for i in range(len(A__						)						):
		for j in range(i + length  ,      len(A__						)						):
			a_      				=						sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				a_      				=						j - i
				a_      				=						sol

	return largest


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