




import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

     from transformers import RegNetForImageClassification, RegNetModel
     from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Tuple=3  ,      SCREAMING_SNAKE_CASE__	:	List[str]=32  ,      SCREAMING_SNAKE_CASE__	:	List[str]=3  ,      SCREAMING_SNAKE_CASE__	:	Dict=10  ,      SCREAMING_SNAKE_CASE__	:	str=[10, 20, 30, 40]  ,      SCREAMING_SNAKE_CASE__	:	Any=[1, 1, 2, 1]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=True  ,      SCREAMING_SNAKE_CASE__	:	Dict=True  ,      SCREAMING_SNAKE_CASE__	:	Any="relu"  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=3  ,      SCREAMING_SNAKE_CASE__	:	Dict=None  ,      ) ->			Dict:
         lowerCAmelCase__            =					parent
         lowerCAmelCase__            =					batch_size
         lowerCAmelCase__            =					image_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					embeddings_size
         lowerCAmelCase__            =					hidden_sizes
         lowerCAmelCase__            =					depths
         lowerCAmelCase__            =					is_training
         lowerCAmelCase__            =					use_labels
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					num_labels
         lowerCAmelCase__            =					scope
         lowerCAmelCase__            =					len(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[str]  ) ->			List[str]:
         lowerCAmelCase__            =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

         lowerCAmelCase__            =					self.get_config()

         return config, pixel_values, labels


    def   a (			self	:	Tuple  ) ->			str:
         return RegNetConfig(
             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  ,      )


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Dict:
         lowerCAmelCase__            =					RegNetModel(config=SCREAMING_SNAKE_CASE__  )
         model.to(SCREAMING_SNAKE_CASE__  )
         model.eval()
         lowerCAmelCase__            =					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	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			List[str]:
         lowerCAmelCase__            =					self.num_labels
         lowerCAmelCase__            =					RegNetForImageClassification(SCREAMING_SNAKE_CASE__  )
         model.to(SCREAMING_SNAKE_CASE__  )
         model.eval()
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)  )


    def   a (			self	:	List[Any]  ) ->			int:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs
         lowerCAmelCase__            =					{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 					__lowerCamelCase			(       UpperCamelCase__      ,							UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    snake_case__   = (
        {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    snake_case__   = False
    snake_case__   = False
    snake_case__   = False
    snake_case__   = False


    def   a (			self	:	List[Any]  ) ->			List[Any]:
         lowerCAmelCase__            =					RegNetModelTester(self  )
         lowerCAmelCase__            =					ConfigTester(self  ,      config_class=SCREAMING_SNAKE_CASE__  ,      has_text_modality=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Union[str, Any]  ) ->			Dict:
         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	:	Any  ) ->			Dict:
         return


    @unittest.skip(reason="RegNet does not use inputs_embeds"  )
    def   a (			self	:	Union[str, Any]  ) ->			Dict:
         pass


    @unittest.skip(reason="RegNet does not support input and output embeddings"  )
    def   a (			self	:	List[Any]  ) ->			Dict:
         pass


    def   a (			self	:	Tuple  ) ->			Dict:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def   a (			self	:	Dict  ) ->			Dict:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ) ->			List[Any]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					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	:	int  ) ->			Optional[Any]:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Dict  ):
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              model.to(SCREAMING_SNAKE_CASE__  )
              model.eval()

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

              lowerCAmelCase__            =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              lowerCAmelCase__            =					self.model_tester.num_stages
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      expected_num_stages + 1  )

              # RegNet'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 // 2, self.model_tester.image_size // 2]  ,      )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__            =					["basic", "bottleneck"]
         for model_class in self.all_model_classes:
              for layer_type in layers_type:
                   lowerCAmelCase__            =					layer_type
                   lowerCAmelCase__            =					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"]
                   lowerCAmelCase__            =					True

                   check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			List[str]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__  )


    @slow
    def   a (			self	:	Dict  ) ->			List[str]:
         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__            =					RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					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	:	Dict  ) ->			List[Any]:
         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
             if is_vision_available()
             else None
         )


    @slow
    def   a (			self	:	Tuple  ) ->			Any:
         lowerCAmelCase__            =					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.default_image_processor
         lowerCAmelCase__            =					prepare_img()
         lowerCAmelCase__            =					image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).to(SCREAMING_SNAKE_CASE__  )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__            =					model(**SCREAMING_SNAKE_CASE__  )

         # verify the logits
         lowerCAmelCase__            =					torch.Size((1, 1_000)  )
         self.assertEqual(outputs.logits.shape  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.tensor([-0.4_180, -1.5_051, -3.4_836]  ).to(SCREAMING_SNAKE_CASE__  )

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




from math import isqrt




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[True] * max_number
       for i in range(2						,     isqrt(max_number - 1	) + 1	):
              if is_prime[i]:
                     for j in range(i**2						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
                            _lowerCamelCase			:						str            =False

       return [i for i in range(2						,     SCREAMING_SNAKE_CASE__	) if is_prime[i]]




def       a_ (       SCREAMING_SNAKE_CASE__   : int = 10**8	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =calculate_prime_numbers(max_number // 2	)

       _lowerCamelCase			:						Dict            =0
       _lowerCamelCase			:						int            =0
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while left <= right:
              while prime_numbers[left] * prime_numbers[right] >= max_number:
                     right -= 1
              semiprimes_count += right - left + 1
              left += 1

       return semiprimes_count


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


