

import math

def 			lowerCamelCase__			(       _a):

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5				,      int(math.sqrt(_a) + 1)				,      6):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True

def 			lowerCamelCase__			(       _a = 0.1):
	SCREAMING_SNAKE_CASE :   int					  =							3
	SCREAMING_SNAKE_CASE :   List[Any]					  =							3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1				,      (j + 2) * (j + 2)				,      j + 1):
			primes += is_prime(_a)
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()


import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DistilBertForMaskedLM,
			    DistilBertForMultipleChoice,
			    DistilBertForQuestionAnswering,
			    DistilBertForSequenceClassification,
			    DistilBertForTokenClassification,
			    DistilBertModel,
			)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			Tuple   ,   a				:			Any=13   ,   a				:			Any=7   ,   a				:			Union[str, Any]=True   ,   a				:			List[Any]=True   ,   a				:			List[str]=False   ,   a				:			List[str]=True   ,   a				:			Any=99   ,   a				:			str=32   ,   a				:			Any=5   ,   a				:			Optional[int]=4   ,   a				:			Union[str, Any]=37   ,   a				:			Dict="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Optional[Any]=0.1   ,   a				:			List[str]=512   ,   a				:			Union[str, Any]=16   ,   a				:			str=2   ,   a				:			Dict=0.02   ,   a				:			Optional[int]=3   ,   a				:			Union[str, Any]=4   ,   a				:			int=None   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							parent
				SCREAMING_SNAKE_CASE :   Any					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							seq_length
				SCREAMING_SNAKE_CASE :   List[Any]					  =							is_training
				SCREAMING_SNAKE_CASE :   int					  =							use_input_mask
				SCREAMING_SNAKE_CASE :   Tuple					  =							use_token_type_ids
				SCREAMING_SNAKE_CASE :   str					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							vocab_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   str					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   str					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_choices
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							scope
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None
				if self.use_input_mask:
					SCREAMING_SNAKE_CASE :   str					  =							random_attention_mask([self.batch_size, self.seq_length]						)

				SCREAMING_SNAKE_CASE :   int					  =							None
				SCREAMING_SNAKE_CASE :   List[Any]					  =							None
				SCREAMING_SNAKE_CASE :   List[str]					  =							None
				if self.use_labels:
					SCREAMING_SNAKE_CASE :   Tuple					  =							ids_tensor([self.batch_size]   ,   self.type_sequence_label_size						)
					SCREAMING_SNAKE_CASE :   str					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ids_tensor([self.batch_size]   ,   self.num_choices						)

				SCREAMING_SNAKE_CASE :   int					  =							self.get_config()

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

				return DistilBertConfig(
				    vocab_size=self.vocab_size   ,   dim=self.hidden_size   ,   n_layers=self.num_hidden_layers   ,   n_heads=self.num_attention_heads   ,   hidden_dim=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   dropout=self.hidden_dropout_prob   ,   attention_dropout=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   initializer_range=self.initializer_range   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			Optional[int]   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			str   ,   a				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							DistilBertModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			Tuple   ,   a				:			int   ,   a				:			int   ,   a				:			Any						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							DistilBertForMaskedLM(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			str   ,   a				:			str   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertForQuestionAnswering(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(
				    a   ,   attention_mask=a   ,   start_positions=a   ,   end_positions=a						)
				self.parent.assertEqual(result.start_logits.shape   ,   (self.batch_size, self.seq_length)						)
				self.parent.assertEqual(result.end_logits.shape   ,   (self.batch_size, self.seq_length)						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			str   ,   a				:			Any   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			int   ,   a				:			str						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertForSequenceClassification(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Dict   ,   a				:			Any   ,   a				:			Optional[Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   List[str]					  =							DistilBertForTokenClassification(config=a						)
				model.to(a						)
				model.eval()

				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Any   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.num_choices
				SCREAMING_SNAKE_CASE :   Any					  =							DistilBertForMultipleChoice(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							input_ids.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Dict					  =							input_mask.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(
				    a   ,   attention_mask=a   ,   labels=a   ,   )
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_choices)						)






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.prepare_config_and_inputs()
				((SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)) :   Tuple					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   int					  =							{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(
			    (
			        DistilBertModel,
			        DistilBertForMaskedLM,
			        DistilBertForMultipleChoice,
			        DistilBertForQuestionAnswering,
			        DistilBertForSequenceClassification,
			        DistilBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			lowerCamelCase__							     =(
			    {
			        'feature-extraction': DistilBertModel,
			        'fill-mask': DistilBertForMaskedLM,
			        'question-answering': DistilBertForQuestionAnswering,
			        'text-classification': DistilBertForSequenceClassification,
			        'token-classification': DistilBertForTokenClassification,
			        'zero-shot': DistilBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							DistilBertModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConfigTester(self   ,   config_class=a   ,   dim=37						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_model(*a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_masked_lm(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_question_answering(*a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_sequence_classification(*a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_token_classification(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_multiple_choice(*a						)
			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Any:

				"""simple docstring"""

				for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertModel.from_pretrained(a						)
					self.assertIsNotNone(a						)






			@slow
			@require_torch_gpu
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					# BertForMultipleChoice behaves incorrectly in JIT environments.
					if model_class == DistilBertForMultipleChoice:
						return

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(config=a						)

					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._prepare_for_class(a   ,   a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.jit.trace(
					    a   ,   (inputs_dict["input_ids"].to("cpu"						), inputs_dict["attention_mask"].to("cpu"						))						)

					with tempfile.TemporaryDirectory() as tmp:
						torch.jit.save(a   ,   os.path.join(a   ,   "traced_model.pt"						)						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							torch.jit.load(os.path.join(a   ,   "traced_model.pt"						)   ,   map_location=a						)
						loaded(inputs_dict["input_ids"].to(a						)   ,   inputs_dict["attention_mask"].to(a						)						)






@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertModel.from_pretrained("distilbert-base-uncased"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   attention_mask=a						)[0]
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.Size((1, 11, 768)						)
				self.assertEqual(output.shape   ,   a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor(
				    [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]						)

				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,   a   ,   atol=1e-4						)						)