





"""simple docstring"""




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         LevitForImageClassification,
         LevitForImageClassificationWithTeacher,
         LevitModel,
     )
     from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor







class     __lowerCAmelCase	(   UpperCamelCase__):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[int]  =self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"hidden_sizes"							)							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"num_attention_heads"							)							)







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=1_3		,				lowerCAmelCase__=6_4		,				lowerCAmelCase__=3		,				lowerCAmelCase__=3		,				lowerCAmelCase__=2		,				lowerCAmelCase__=1		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=[1_2_8, 2_5_6, 3_8_4]		,				lowerCAmelCase__=[4, 6, 8]		,				lowerCAmelCase__=[2, 3, 4]		,				lowerCAmelCase__=[1_6, 1_6, 1_6]		,				lowerCAmelCase__=0		,				lowerCAmelCase__=[2, 2, 2]		,				lowerCAmelCase__=[2, 2, 2]		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=2		,				)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : int  =parent
        a__     : Any  =batch_size
        a__     : Tuple  =image_size
        a__     : Optional[int]  =num_channels
        a__     : Optional[int]  =kernel_size
        a__     : Any  =stride
        a__     : str  =padding
        a__     : Union[str, Any]  =hidden_sizes
        a__     : List[str]  =num_attention_heads
        a__     : int  =depths
        a__     : List[Any]  =key_dim
        a__     : List[str]  =drop_path_rate
        a__     : int  =patch_size
        a__     : int  =attention_ratio
        a__     : Any  =mlp_ratio
        a__     : Tuple  =initializer_range
        a__     : int  =[
            ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]
        a__     : Optional[int]  =is_training
        a__     : Optional[Any]  =use_labels
        a__     : Optional[Any]  =num_labels
        a__     : Dict  =initializer_range







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



        '''simple docstring'''




        a__     : Tuple  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        a__     : Optional[int]  =None
        if self.use_labels:
          a__     : List[Any]  =ids_tensor([self.batch_size]		,				self.num_labels							)

        a__     : Union[str, Any]  =self.get_config()
        return config, pixel_values, labels







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



        '''simple docstring'''




        return LevitConfig(
            image_size=self.image_size		,				num_channels=self.num_channels		,				kernel_size=self.kernel_size		,				stride=self.stride		,				padding=self.padding		,				patch_size=self.patch_size		,				hidden_sizes=self.hidden_sizes		,				num_attention_heads=self.num_attention_heads		,				depths=self.depths		,				key_dim=self.key_dim		,				drop_path_rate=self.drop_path_rate		,				mlp_ratio=self.mlp_ratio		,				attention_ratio=self.attention_ratio		,				initializer_range=self.initializer_range		,				down_ops=self.down_ops		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




        a__     : Union[str, Any]  =LevitModel(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Optional[Any]  =model(lowerCAmelCase__							)
        a__     : List[str]  =(self.image_size, self.image_size)
        a__     : Dict  =image_size[0], image_size[1]
        for _ in range(4							):
          a__     : Optional[Any]  =floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
          a__     : str  =floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
        self.parent.assertEqual(
            result.last_hidden_state.shape		,				(self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.num_labels
        a__     : Tuple  =LevitForImageClassification(lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Union[str, Any]  =model(lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)







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



        '''simple docstring'''




        a__     : Dict  =self.prepare_config_and_inputs()
        a__     : List[Any]  =config_and_inputs
        a__     : Tuple  ={"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class     __lowerCAmelCase	(   UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : Optional[int]            =      (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      _lowercase    : Optional[int]            =      (
          {
              """feature-extraction""": LevitModel,
              """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      _lowercase    : str            =      False
      _lowercase    : Any            =      False
      _lowercase    : Union[str, Any]            =      False
      _lowercase    : List[Any]            =      False
      _lowercase    : Optional[Any]            =      False







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Tuple  =LevitModelTester(self							)
        a__     : List[str]  =ConfigTester(self		,				config_class=lowerCAmelCase__		,				has_text_modality=lowerCAmelCase__		,				hidden_size=3_7							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







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



        '''simple docstring'''




        return







      @unittest.skip(reason="Levit does not use inputs_embeds"							)
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        pass







      @unittest.skip(reason="Levit does not support input and output embeddings"							)
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        pass







      @unittest.skip(reason="Levit does not output attentions"							)
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        pass







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : int  =model_class(lowerCAmelCase__							)
          a__     : Optional[Any]  =inspect.signature(model.forward							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          a__     : List[Any]  =[*signature.parameters.keys()]

          a__     : Tuple  =["pixel_values"]
          self.assertListEqual(arg_names[:1]		,				lowerCAmelCase__							)







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



        '''simple docstring'''




        def check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):
          a__     : Any  =model_class(lowerCAmelCase__							)
          model.to(lowerCAmelCase__							)
          model.eval()

          with torch.no_grad():
            a__     : Dict  =model(**self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__							)							)

          a__     : Union[str, Any]  =outputs.hidden_states

          a__     : str  =len(self.model_tester.depths							) + 1
          self.assertEqual(len(lowerCAmelCase__							)		,				lowerCAmelCase__							)

          a__     : Union[str, Any]  =(self.model_tester.image_size, self.model_tester.image_size)
          a__     : str  =image_size[0], image_size[1]
          for _ in range(4							):
            a__     : List[str]  =floor(
                (
                    (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                    / self.model_tester.stride
                )
                + 1							)
            a__     : Optional[Any]  =floor(
                (
                    (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                    / self.model_tester.stride
                )
                + 1							)
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]							)		,				[
                  height * width,
                  self.model_tester.hidden_sizes[0],
              ]		,				)

        a__     : int  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : str  =True
          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a__     : Optional[int]  =True

          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)







      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        pass







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=False							)       ->						Dict:



        '''simple docstring'''




        a__     : int  =super()._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__		,				return_labels=lowerCAmelCase__							)

        if return_labels:
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
            del inputs_dict["labels"]

        return inputs_dict







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



        '''simple docstring'''




        a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)







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



        '''simple docstring'''




        if not self.model_tester.is_training:
          return

        a__     : Tuple  =self.model_tester.prepare_config_and_inputs_for_common()
        a__     : int  =True

        for model_class in self.all_model_classes:
          # LevitForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(lowerCAmelCase__							)
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
            continue
          a__     : Tuple  =model_class(lowerCAmelCase__							)
          model.to(lowerCAmelCase__							)
          model.train()
          a__     : Union[str, Any]  =self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__		,				return_labels=lowerCAmelCase__							)
          a__     : Dict  =model(**lowerCAmelCase__							).loss
          loss.backward()







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : int  =self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
          return

        a__     : List[Any]  =False
        a__     : int  =True

        for model_class in self.all_model_classes:
          if model_class in get_values(lowerCAmelCase__							) or not model_class.supports_gradient_checkpointing:
            continue
          # LevitForImageClassificationWithTeacher supports inference-only
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
            continue
          a__     : Dict  =model_class(lowerCAmelCase__							)
          model.gradient_checkpointing_enable()
          model.to(lowerCAmelCase__							)
          model.train()
          a__     : Tuple  =self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__		,				return_labels=lowerCAmelCase__							)
          a__     : int  =model(**lowerCAmelCase__							).loss
          loss.backward()







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =self.model_tester.prepare_config_and_inputs_for_common()

        a__     : List[Any]  =[
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

        for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(lowerCAmelCase__							),
              ]
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
            continue

          for problem_type in problem_types:
            with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''							):
              a__     : Tuple  =problem_type["title"]
              a__     : Optional[Any]  =problem_type["num_labels"]

              a__     : Dict  =model_class(lowerCAmelCase__							)
              model.to(lowerCAmelCase__							)
              model.train()

              a__     : Any  =self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__		,				return_labels=lowerCAmelCase__							)

              if problem_type["num_labels"] > 1:
                a__     : Optional[int]  =inputs["labels"].unsqueeze(1							).repeat(1		,				problem_type["num_labels"]							)

              a__     : Optional[Any]  =inputs["labels"].to(problem_type["dtype"]							)

              # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
              # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
              # they have the same size." which is a symptom something in wrong for the regression problem.
              # See https://github.com/huggingface/transformers/issues/11780
              with warnings.catch_warnings(record=lowerCAmelCase__							) as warning_list:
                a__     : int  =model(**lowerCAmelCase__							).loss
              for w in warning_list:
                if "Using a target size that is different to the input size" in str(w.message							):
                  raise ValueError(
                      F'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

              loss.backward()







      @slow
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__     : Dict  =LevitModel.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
def 					_A	(		):





  """simple docstring"""



  a__     : Tuple  =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
  return image







@require_torch
@require_vision
class     __lowerCAmelCase	(   unittest.TestCase):







      @cached_property
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)







      @slow
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
            lowerCAmelCase__							)

        a__     : Any  =self.default_image_processor
        a__     : Dict  =prepare_img()
        a__     : int  =image_processor(images=lowerCAmelCase__		,				return_tensors="pt"							).to(lowerCAmelCase__							)

        # forward pass
        with torch.no_grad():
          a__     : List[Any]  =model(**lowerCAmelCase__							)

        # verify the logits
        a__     : Tuple  =torch.Size((1, 1_0_0_0)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase__							)

        a__     : List[Any]  =torch.tensor([1.04_48, -0.37_45, -1.83_17]							).to(lowerCAmelCase__							)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				lowerCAmelCase__		,				atol=1E-4							)							)













from __future__ import annotations

import math
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if num <= 0:
    a__     : List[str]  =f'''{num}: Invalid input, please enter a positive integer.'''
    raise ValueError(SCREAMING_SNAKE_CASE						)

  a__     : Union[str, Any]  =[True] * (num + 1)
  a__     : Union[str, Any]  =[]
  a__     : str  =2
  a__     : Any  =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__     : Optional[int]  =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())))






