





'''simple docstring'''







import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=3   ,							SCREAMING_SNAKE_CASE    :							str=32   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=3   ,							SCREAMING_SNAKE_CASE    :							int=10   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=[10, 20, 30, 40]   ,							SCREAMING_SNAKE_CASE    :							str=[1, 1, 2, 1]   ,							SCREAMING_SNAKE_CASE    :							int=True   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=True   ,							SCREAMING_SNAKE_CASE    :							Any="relu"   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=3   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Any=["stage2", "stage3", "stage4"]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=[2, 3, 4]   ,							):
           _A  :			Optional[Any]											=      parent
           _A  :			Tuple											=      batch_size
           _A  :			Union[str, Any]											=      image_size
           _A  :			Tuple											=      num_channels
           _A  :			Any											=      embeddings_size
           _A  :			List[Any]											=      hidden_sizes
           _A  :			Dict											=      depths
           _A  :			Union[str, Any]											=      is_training
           _A  :			Optional[Any]											=      use_labels
           _A  :			Any											=      hidden_act
           _A  :			str											=      num_labels
           _A  :			str											=      scope
           _A  :			Optional[int]											=      len(SCREAMING_SNAKE_CASE)
           _A  :			Tuple											=      out_features
           _A  :			Optional[int]											=      out_indices


    def      A					(						self    :							int):
           _A  :			Optional[int]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A  :			str											=      None
           if self.use_labels:
                  _A  :			str											=      ids_tensor([self.batch_size]   ,							self.num_labels)

           _A  :			int											=      self.get_config()

           return config, pixel_values, labels


    def      A					(						self    :							List[Any]):
           return ResNetConfig(
               num_channels=self.num_channels   ,							embeddings_size=self.embeddings_size   ,							hidden_sizes=self.hidden_sizes   ,							depths=self.depths   ,							hidden_act=self.hidden_act   ,							num_labels=self.num_labels   ,							out_features=self.out_features   ,							out_indices=self.out_indices   ,							)


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[Any]):
           _A  :			Union[str, Any]											=      ResNetModel(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			Tuple											=      model(SCREAMING_SNAKE_CASE)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,							)


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[Any]):
           _A  :			int											=      self.num_labels
           _A  :			Any											=      ResNetForImageClassification(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			Union[str, Any]											=      model(SCREAMING_SNAKE_CASE   ,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels))


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           _A  :			List[str]											=      ResNetBackbone(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			str											=      model(SCREAMING_SNAKE_CASE)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps)   ,							len(config.out_features))
           self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,							[self.batch_size, self.hidden_sizes[1], 4, 4])

           # verify channels
           self.parent.assertEqual(len(model.channels)   ,							len(config.out_features))
           self.parent.assertListEqual(model.channels   ,							config.hidden_sizes[1:])

           # verify backbone works with out_features=None
           _A  :			str											=      None
           _A  :			int											=      ResNetBackbone(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			Optional[Any]											=      model(SCREAMING_SNAKE_CASE)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps)   ,							1)
           self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,							[self.batch_size, self.hidden_sizes[-1], 1, 1])

           # verify channels
           self.parent.assertEqual(len(model.channels)   ,							1)
           self.parent.assertListEqual(model.channels   ,							[config.hidden_sizes[-1]])


    def      A					(						self    :							Dict):
           _A  :			List[Any]											=      self.prepare_config_and_inputs()
           _A  :			Optional[int]											=      config_and_inputs
           _A  :			List[str]											=      {'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 				__lowerCamelCase				( a_       ,      a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(
        (
            ResNetModel,
            ResNetForImageClassification,
            ResNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    a									=					(
        {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
        if is_torch_available()
        else {}
    )

    a									=					True
    a									=					False
    a									=					False
    a									=					False
    a									=					False


    def      A					(						self    :							List[str]):
           _A  :			Optional[Any]											=      ResNetModelTester(self)
           _A  :			Any											=      ConfigTester(self   ,							config_class=SCREAMING_SNAKE_CASE   ,							has_text_modality=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple):
           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      A					(						self    :							List[str]):
           return


    @unittest.skip(reason='ResNet does not use inputs_embeds')
    def      A					(						self    :							Any):
           pass


    @unittest.skip(reason='ResNet does not support input and output embeddings')
    def      A					(						self    :							Dict):
           pass


    def      A					(						self    :							str):
           _A  :			Optional[int]											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			Union[str, Any]											=      model_class(SCREAMING_SNAKE_CASE)
                  _A  :			Any											=      inspect.signature(model.forward)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A  :			str											=      [*signature.parameters.keys()]

                  _A  :			List[Any]											=      ['pixel_values']
                  self.assertListEqual(arg_names[:1]   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           _A  :			Dict											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Union[str, Any]):
           _A  :			Dict											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           _A  :			List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			Optional[Any]											=      model_class(config=SCREAMING_SNAKE_CASE)
                  for name, module in model.named_modules():
                         if isinstance(SCREAMING_SNAKE_CASE   ,							(nn.BatchNormad, nn.GroupNorm)):
                                self.assertTrue(
                                    torch.all(module.weight == 1)   ,							msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,							)
                                self.assertTrue(
                                    torch.all(module.bias == 0)   ,							msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,							)


    def      A					(						self    :							Optional[int]):
           def check_hidden_states_output(SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]):
                  _A  :			str											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()

                  with torch.no_grad():
                         _A  :			Optional[Any]											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))

                  _A  :			Dict											=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  _A  :			Union[str, Any]											=      self.model_tester.num_stages
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							expected_num_stages + 1)

                  # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:])   ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,							)

           _A  :			Union[str, Any]											=      self.model_tester.prepare_config_and_inputs_for_common()
           _A  :			Any											=      ['basic', 'bottleneck']
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         _A  :			Optional[int]											=      layer_type
                         _A  :			Tuple											=      True
                         check_hidden_states_output(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                         # check that output_hidden_states also work using config
                         del inputs_dict["output_hidden_states"]
                         _A  :			Any											=      True

                         check_hidden_states_output(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    @unittest.skip(reason='ResNet does not use feedforward chunking')
    def      A					(						self    :							Dict):
           pass


    def      A					(						self    :							str):
           _A  :			Optional[Any]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    @slow
    def      A					(						self    :							str):
           for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A  :			Optional[int]											=      ResNetModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def    lowerCAmelCase__ (       ):
       _A  :			Any											=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


@require_torch
@require_vision
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @cached_property
    def      A					(						self    :							Union[str, Any]):
           return (
               AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )


    @slow
    def      A					(						self    :							List[Any]):
           _A  :			Optional[Any]											=      ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(SCREAMING_SNAKE_CASE)

           _A  :			Any											=      self.default_image_processor
           _A  :			int											=      prepare_img()
           _A  :			Optional[int]											=      image_processor(images=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').to(SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
                  _A  :			Optional[Any]											=      model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A  :			Any											=      torch.Size((1, 1000))
           self.assertEqual(outputs.logits.shape   ,							SCREAMING_SNAKE_CASE)

           _A  :			Tuple											=      torch.tensor([-11.1069, -9.7877, -8.3777]).to(SCREAMING_SNAKE_CASE)

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




@require_torch
class 				__lowerCamelCase				( a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(ResNetBackbone,) if is_torch_available() else ()
    a									=					False
    a									=					ResNetConfig


    def      A					(						self    :							Optional[int]):
           _A  :			Optional[Any]											=      ResNetModelTester(self)










'''simple docstring'''







import math






def    lowerCAmelCase__ (       lowerCamelCase  :    int     ):

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






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000						,lowerCamelCase  :    int = 1000     ):
       _A  :			int											=      [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1						,lowerCamelCase     ):
              for b in range(2						,lowerCamelCase     ):
                     if is_prime(lowerCamelCase     ):
                            _A  :			Dict											=      0
                            _A  :			Tuple											=      0
                            while is_prime((n**2) + (a * n) + b     ):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   _A  :			List[str]											=      [count, a, b]
       _A  :			str											=      longest[1] * longest[2]
       return ans


if __name__ == "__main__":
   print(solution(1000, 1000))






