






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        a       (					lowercase__  ):



      """simple docstring"""


      def __init__( self   :     Optional[Any]						, __lowercase   :     Optional[int]						, __lowercase   :     List[Any]=13						, __lowercase   :     List[str]=7						, __lowercase   :     Any=True						, __lowercase   :     Tuple=True						, __lowercase   :     List[Any]=False						, __lowercase   :     Any=True						, __lowercase   :     List[str]=99						, __lowercase   :     Any=32						, __lowercase   :     Optional[Any]=5						, __lowercase   :     Dict=4						, __lowercase   :     Optional[Any]=37						, __lowercase   :     int="gelu"						, __lowercase   :     Optional[int]=0.1						, __lowercase   :     List[Any]=0.1						, __lowercase   :     Tuple=512						, __lowercase   :     str=16						, __lowercase   :     Dict=2						, __lowercase   :     Any=0.02						, __lowercase   :     Optional[Any]=3						, __lowercase   :     Any=4						, __lowercase   :     Optional[Any]=None						, )				-> List[Any]:
        __UpperCAmelCase				:      str	 =    parent
        __UpperCAmelCase				:      Optional[Any]	 =    batch_size
        __UpperCAmelCase				:      Tuple	 =    seq_length
        __UpperCAmelCase				:      Union[str, Any]	 =    is_training
        __UpperCAmelCase				:      Any	 =    use_input_mask
        __UpperCAmelCase				:      Optional[int]	 =    use_token_type_ids
        __UpperCAmelCase				:      Optional[int]	 =    use_labels
        __UpperCAmelCase				:      Optional[Any]	 =    vocab_size
        __UpperCAmelCase				:      Dict	 =    hidden_size
        __UpperCAmelCase				:      Optional[int]	 =    num_hidden_layers
        __UpperCAmelCase				:      int	 =    num_attention_heads
        __UpperCAmelCase				:      int	 =    intermediate_size
        __UpperCAmelCase				:      Optional[int]	 =    hidden_act
        __UpperCAmelCase				:      List[Any]	 =    hidden_dropout_prob
        __UpperCAmelCase				:      List[str]	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      List[str]	 =    max_position_embeddings
        __UpperCAmelCase				:      List[str]	 =    type_vocab_size
        __UpperCAmelCase				:      Tuple	 =    type_sequence_label_size
        __UpperCAmelCase				:      int	 =    initializer_range
        __UpperCAmelCase				:      Optional[int]	 =    num_labels
        __UpperCAmelCase				:      Any	 =    num_choices
        __UpperCAmelCase				:      Tuple	 =    scope
      def       UpperCAmelCase ( self   :     Any   )				-> Union[str, Any]:
        __UpperCAmelCase				:      List[Any]	 =    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size   )

        __UpperCAmelCase				:      str	 =    None
        if self.use_input_mask:
          __UpperCAmelCase				:      Optional[int]	 =    random_attention_mask([self.batch_size, self.seq_length]   )

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

        __UpperCAmelCase				:      Any	 =    self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def       UpperCAmelCase ( self   :     Any   )				-> int:
        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   :     Any						, __lowercase   :     str						, __lowercase   :     Union[str, Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     int						, __lowercase   :     str   )				-> str:
        __UpperCAmelCase				:      Any	 =    DistilBertModel(config=__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Any	 =    model(__lowercase						, __lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    model(__lowercase   )
        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     List[str]						, __lowercase   :     Optional[Any]						, __lowercase   :     Dict   )				-> int:
        __UpperCAmelCase				:      Union[str, Any]	 =    DistilBertForMaskedLM(config=__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Dict	 =    model(__lowercase						, attention_mask=__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)   )
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     List[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     int						, __lowercase   :     Tuple						, __lowercase   :     List[str]   )				-> Tuple:
        __UpperCAmelCase				:      Tuple	 =    DistilBertForQuestionAnswering(config=__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      List[Any]	 =    model(
            __lowercase						, attention_mask=__lowercase						, start_positions=__lowercase						, end_positions=__lowercase   )
        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   :     Tuple						, __lowercase   :     Optional[Any]						, __lowercase   :     List[Any]						, __lowercase   :     Tuple						, __lowercase   :     Union[str, Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     int   )				-> str:
        __UpperCAmelCase				:      Any	 =    self.num_labels
        __UpperCAmelCase				:      List[str]	 =    DistilBertForSequenceClassification(__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Union[str, Any]	 =    model(__lowercase						, attention_mask=__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)   )
      def       UpperCAmelCase ( self   :     Dict						, __lowercase   :     Union[str, Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Dict						, __lowercase   :     str						, __lowercase   :     Any						, __lowercase   :     str   )				-> int:
        __UpperCAmelCase				:      Optional[Any]	 =    self.num_labels
        __UpperCAmelCase				:      Optional[int]	 =    DistilBertForTokenClassification(config=__lowercase   )
        model.to(__lowercase   )
        model.eval()

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




      def       UpperCAmelCase ( self   :     Tuple   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Any	 =    self.prepare_config_and_inputs()
        ((__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase))				:      int	 =    config_and_inputs
        __UpperCAmelCase				:      str	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict


@require_torch
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Optional[Any]							=      (
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      a    :					Union[str, Any]							=      (
          {
              'feature-extraction': DistilBertModel,
              'fill-mask': DistilBertForMaskedLM,
              'question-answering': DistilBertForQuestionAnswering,
              'text-classification': DistilBertForSequenceClassification,
              'token-classification': DistilBertForTokenClassification,
              'zero-shot': DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a    :					Tuple							=      True
      a    :					Tuple							=      True
      a    :					Any							=      True
      a    :					Optional[Any]							=      True
      def       UpperCAmelCase ( self   :     List[str]   )				-> str:
        __UpperCAmelCase				:      Dict	 =    DistilBertModelTester(self   )
        __UpperCAmelCase				:      Optional[int]	 =    ConfigTester(self						, config_class=__lowercase						, dim=37   )
      def       UpperCAmelCase ( self   :     Any   )				-> Optional[Any]:
        self.config_tester.run_common_tests()
      def       UpperCAmelCase ( self   :     Any   )				-> Any:
        __UpperCAmelCase				:      List[Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*__lowercase   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Tuple:
        __UpperCAmelCase				:      Tuple	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowercase   )
      def       UpperCAmelCase ( self   :     int   )				-> Any:
        __UpperCAmelCase				:      List[Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        __UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> int:
        __UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowercase   )
      @slow
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Dict:
        for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase				:      List[Any]	 =    DistilBertModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )




      @slow
      @require_torch_gpu
      def       UpperCAmelCase ( self   :     List[Any]   )				-> int:
        __UpperCAmelCase ,					__UpperCAmelCase				:      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

          __UpperCAmelCase				:      List[str]	 =    True
          __UpperCAmelCase				:      Dict	 =    model_class(config=__lowercase   )

          __UpperCAmelCase				:      List[Any]	 =    self._prepare_for_class(__lowercase						, __lowercase   )
          __UpperCAmelCase				:      Dict	 =    torch.jit.trace(
              __lowercase						, (inputs_dict["""input_ids"""].to("""cpu"""   ), inputs_dict["""attention_mask"""].to("""cpu"""   ))   )

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


@require_torch
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @slow
      def       UpperCAmelCase ( self   :     Any   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Tuple	 =    DistilBertModel.from_pretrained("""distilbert-base-uncased"""   )
        __UpperCAmelCase				:      Optional[int]	 =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
        __UpperCAmelCase				:      List[str]	 =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
        with torch.no_grad():
          __UpperCAmelCase				:      List[str]	 =    model(__lowercase						, attention_mask=__lowercase   )[0]
        __UpperCAmelCase				:      Dict	 =    torch.Size((1, 11, 768)   )
        self.assertEqual(output.shape						, __lowercase   )
        __UpperCAmelCase				:      Dict	 =    torch.tensor(
            [[[-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(torch.allclose(output[:, 1:4, 1:4]						, __lowercase						, atol=1e-4   )   )









from __future__ import annotations

import math





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> list[int]:




    """simple docstring"""




    if num <= 0:
        _A     =							F"{num}: Invalid input, please enter a positive integer."
        raise ValueError(_SCREAMING_SNAKE_CASE				)

    _A     =							[True] * (num + 1)
    _A     =							[]
    _A     =							2
    _A     =							int(math.sqrt(_SCREAMING_SNAKE_CASE				)				)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(_SCREAMING_SNAKE_CASE				)

            # Set multiples of start be False
            for i in range(start * start							,     num + 1							,     _SCREAMING_SNAKE_CASE				):
                if sieve[i] is True:
                    _A     =							False

        start += 1

    for j in range(end + 1							,     num + 1				):
        if sieve[j] is True:
            prime.append(_SCREAMING_SNAKE_CASE				)

    return prime


if __name__ == "__main__":
 print(prime_sieve(int(input("Enter a positive integer: ").strip())))





