


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)



class 				A       :
					'''simple docstring'''






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




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_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_labels
							lowercase__	     =						num_choices
							lowercase__	     =						relative_attention
							lowercase__	     =						position_biased_input
							lowercase__	     =						pos_att_type
							lowercase__	     =						scope

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


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

							lowercase__	     =						None
							if self.use_input_mask:
									lowercase__	     =						random_attention_mask([self.batch_size, self.seq_length]						)

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

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)

							lowercase__	     =						DebertaVaConfig(
							    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 ,   relative_attention=self.relative_attention ,   position_biased_input=self.position_biased_input ,   initializer_range=self.initializer_range ,   return_dict=_UpperCAmelCase ,   )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel(config=_UpperCAmelCase						)
							lowercase__	     =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowercase__	     =						[input_ids, input_mask]
							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(_UpperCAmelCase						)

							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaForMaskedLM(config=_UpperCAmelCase						)
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						TFDebertaVaForSequenceClassification(config=_UpperCAmelCase						)
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)						)

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Any						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						TFDebertaVaForTokenClassification(config=_UpperCAmelCase						)
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase						)
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowercase__	     =						model(_UpperCAmelCase						)
							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     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


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






@require_tf
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        TFDebertaVaModel,
					        TFDebertaVaForMaskedLM,
					        TFDebertaVaForQuestionAnswering,
					        TFDebertaVaForSequenceClassification,
					        TFDebertaVaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					A__     =					(
					    {
					        '''feature-extraction''': TFDebertaVaModel,
					        '''fill-mask''': TFDebertaVaForMaskedLM,
					        '''question-answering''': TFDebertaVaForQuestionAnswering,
					        '''text-classification''': TFDebertaVaForSequenceClassification,
					        '''token-classification''': TFDebertaVaForTokenClassification,
					        '''zero-shot''': TFDebertaVaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					A__     =					False
					A__     =					False

					def     lowerCamelCase__	(self       :		Tuple						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   hidden_size=37						)

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
							self.assertIsNotNone(_UpperCAmelCase						)






@require_tf
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@unittest.skip(reason="""Model not available yet"""						)
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Optional[int]:




							"""simple docstring"""


							pass


					@slow
					def     lowerCamelCase__	(self       :		Dict						)		->  str:




							"""simple docstring"""


							lowercase__	     =						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
							lowercase__	     =						tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
							lowercase__	     =						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase						)[0]

							lowercase__	     =						tf.constant(
							    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]						)
							tf.debugging.assert_near(output[:, 1:4, 1:4] ,   _UpperCAmelCase ,   atol=1E-4						)









'''simple docstring'''







import math





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	str									=   []
				a_		:	Tuple									=   2
				a_		:	Optional[Any]									=   int(math.sqrt(__A   )   )  # Size of every segment
				a_		:	Optional[Any]									=   [True] * (end + 1)
				a_		:	Union[str, Any]									=   []

				while start <= end:
								if temp[start] is True:
												in_prime.append(__A   )
												for i in range(start * start    ,		end + 1    ,		__A   ):
																a_		:	List[Any]									=   False
								start += 1
				prime += in_prime

				a_		:	Any									=   end + 1
				a_		:	Optional[Any]									=   min(2 * end    ,		__A   )

				while low <= n:
								a_		:	Optional[Any]									=   [True] * (high - low + 1)
								for each in in_prime:
												a_		:	Any									=   math.floor(low / each   ) * each
												if t < low:
																t += each

												for j in range(__A    ,		high + 1    ,		__A   ):
																a_		:	Union[str, Any]									=   False

								for j in range(len(__A   )   ):
												if temp[j] is True:
																prime.append(j + low   )

								a_		:	Tuple									=   high + 1
								a_		:	str									=   min(high + end    ,		__A   )

				return prime




print(sieve(10**6))




