






import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)


class        __UpperCamelCase    (     unittest.TestCase   ):







	def __init__(  self      :							List[str]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							List[str]=13       ,				_lowerCAmelCase      :							List[str]=7       ,				_lowerCAmelCase      :							Optional[Any]=True       ,				_lowerCAmelCase      :							Union[str, Any]=True       ,				_lowerCAmelCase      :							Dict=True       ,				_lowerCAmelCase      :							Any=True       ,				_lowerCAmelCase      :							Tuple=99       ,				_lowerCAmelCase      :							Dict=32       ,				_lowerCAmelCase      :							Tuple=5       ,				_lowerCAmelCase      :							Dict=4       ,				_lowerCAmelCase      :							Tuple=37       ,				_lowerCAmelCase      :							Optional[int]="gelu"       ,				_lowerCAmelCase      :							Dict=0.1       ,				_lowerCAmelCase      :							Union[str, Any]=0.1       ,				_lowerCAmelCase      :							Union[str, Any]=512       ,				_lowerCAmelCase      :							Optional[Any]=16       ,				_lowerCAmelCase      :							str=2       ,				_lowerCAmelCase      :							int=0.02       ,				_lowerCAmelCase      :							Optional[Any]=4       ,				)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  seq_length
		__lowercase             =  is_training
		__lowercase             =  use_attention_mask
		__lowercase             =  use_token_type_ids
		__lowercase             =  use_labels
		__lowercase             =  vocab_size
		__lowercase             =  hidden_size
		__lowercase             =  num_hidden_layers
		__lowercase             =  num_attention_heads
		__lowercase             =  intermediate_size
		__lowercase             =  hidden_act
		__lowercase             =  hidden_dropout_prob
		__lowercase             =  attention_probs_dropout_prob
		__lowercase             =  max_position_embeddings
		__lowercase             =  type_vocab_size
		__lowercase             =  type_sequence_label_size
		__lowercase             =  initializer_range
		__lowercase             =  num_choices







	def  _a   (  self      :							List[Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size			)

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

		__lowercase             =  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       ,				tie_weights_=_lowerCAmelCase       ,				)

		return config, input_ids, attention_mask







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		__lowercase       ,						__lowercase       ,						__lowercase             =  config_and_inputs
		__lowercase             =  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
		return config, inputs_dict


@require_flax
class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:int   =       (
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def  _a   (  self      :							Union[str, Any]			)		-> int:
		"""simple docstring"""







		__lowercase             =  FlaxDistilBertModelTester(self			)







	@slow
	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for model_class_name in self.all_model_classes:
			__lowercase             =  model_class_name.from_pretrained("""distilbert-base-uncased"""			)
			__lowercase             =  model(np.ones((1, 1)			)			)
			self.assertIsNotNone(_lowerCAmelCase			)


@require_flax
class        __UpperCamelCase    (     unittest.TestCase   ):







	@slow
	def  _a   (  self      :							List[Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""			)
		__lowercase             =  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]			)
		__lowercase             =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
		__lowercase             =  model(_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)[0]
		__lowercase             =  (1, 11, 768)
		self.assertEqual(output.shape       ,				_lowerCAmelCase			)
		__lowercase             =  np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]			)

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









from math import sqrt





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	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(sqrt(lowerCamelCase					) + 1					)				,  6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 				snake_case  (     lowerCamelCase = 10_001					):




	'''simple docstring'''







	__lowercase             =  0
	__lowercase             =  1
	while count != nth and number < 3:
		number += 1
		if is_prime(lowerCamelCase					):
			count += 1
	while count != nth:
		number += 2
		if is_prime(lowerCamelCase					):
			count += 1
	return number


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

