
import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 MobileNetVaForImageClassification, MobileNetVaModel
    from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''tf_padding'''	)	)
           self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''depth_multiplier'''	)	)




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=1_3    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=0.25    ,    __SCREAMING_SNAKE_CASE=8    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=1_0_2_4    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE="relu6"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=1_0    ,    __SCREAMING_SNAKE_CASE=None    ,    )	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					parent
           UpperCamelCase__     : List[str]											=					batch_size
           UpperCamelCase__     : Optional[Any]											=					num_channels
           UpperCamelCase__     : Dict											=					image_size
           UpperCamelCase__     : int											=					depth_multiplier
           UpperCamelCase__     : int											=					min_depth
           UpperCamelCase__     : Any											=					tf_padding
           UpperCamelCase__     : List[str]											=					int(last_hidden_size * depth_multiplier	)
           UpperCamelCase__     : List[Any]											=					output_stride
           UpperCamelCase__     : Any											=					hidden_act
           UpperCamelCase__     : List[Any]											=					classifier_dropout_prob
           UpperCamelCase__     : Dict											=					use_labels
           UpperCamelCase__     : int											=					is_training
           UpperCamelCase__     : Optional[int]											=					num_labels
           UpperCamelCase__     : List[str]											=					initializer_range
           UpperCamelCase__     : int											=					scope






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           UpperCamelCase__     : Tuple											=					None
           UpperCamelCase__     : Optional[int]											=					None
           if self.use_labels:
                 UpperCamelCase__     : Tuple											=					ids_tensor([self.batch_size]    ,    self.num_labels	)
                 UpperCamelCase__     : Optional[Any]											=					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,    self.num_labels	)

           UpperCamelCase__     : Any											=					self.get_config()

           return config, pixel_values, labels, pixel_labels






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           return MobileNetVaConfig(
               num_channels=self.num_channels    ,    image_size=self.image_size    ,    depth_multiplier=self.depth_multiplier    ,    min_depth=self.min_depth    ,    tf_padding=self.tf_padding    ,    hidden_act=self.hidden_act    ,    classifier_dropout_prob=self.classifier_dropout_prob    ,    initializer_range=self.initializer_range    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : str											=					MobileNetVaModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Optional[Any]											=					model(__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(
               result.last_hidden_state.shape    ,    (
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.num_labels
           UpperCamelCase__     : int											=					MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[str]											=					model(__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.num_labels)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.prepare_config_and_inputs()
           UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Dict											=					config_and_inputs
           UpperCamelCase__     : Union[str, Any]											=					{'''pixel_values''': pixel_values}
           return config, inputs_dict




@require_torch
class 					_lowerCamelCase			(   UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(
         {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_		 =	False
     SCREAMING_SNAKE_CASE_		 =	False
     SCREAMING_SNAKE_CASE_		 =	False
     SCREAMING_SNAKE_CASE_		 =	False






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




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					MobileNetVaModelTester(self	)
           UpperCamelCase__     : str											=					MobileNetVaConfigTester(self    ,    config_class=__SCREAMING_SNAKE_CASE    ,    has_text_modality=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.config_tester.run_common_tests()






     @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''	)
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           pass






     @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''	)
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           pass






     @unittest.skip(reason='''MobileNetV1 does not output attentions'''	)
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







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

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

                 UpperCamelCase__     : List[str]											=					['''pixel_values''']
                 self.assertListEqual(arg_names[:1]    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           def check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : int											=					model_class(__SCREAMING_SNAKE_CASE	)
                 model.to(__SCREAMING_SNAKE_CASE	)
                 model.eval()

                 with torch.no_grad():
                       UpperCamelCase__     : str											=					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	)

                 UpperCamelCase__     : Optional[Any]											=					outputs.hidden_states

                 UpperCamelCase__     : Optional[Any]											=					2_6
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE	)    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__					,UpperCamelCase__     : Dict											=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 UpperCamelCase__     : Any											=					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"]
                 UpperCamelCase__     : Tuple											=					True

                 check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__     : Optional[int]											=					MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           return (
               MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''	) if is_vision_available() else None
           )






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''	).to(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					self.default_image_processor
           UpperCamelCase__     : str											=					prepare_img()
           UpperCamelCase__     : Any											=					image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)

           # forward pass
           with torch.no_grad():
                 UpperCamelCase__     : Any											=					model(**__SCREAMING_SNAKE_CASE	)

           # verify the logits
           UpperCamelCase__     : Tuple											=					torch.Size((1, 1_0_0_1)	)
           self.assertEqual(outputs.logits.shape    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Optional[Any]											=					torch.tensor([-4.1739, -1.1233, 3.1205]	).to(__SCREAMING_SNAKE_CASE	)

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




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = 2_0_0_0_0_0_0		):
      UpperCamelCase__     : Dict											=					[0 for i in range(n + 1		)]
      UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : int											=					1

      for i in range(2			,		int(n**0.5		) + 1		):
            if primality_list[i] == 0:
                  for j in range(i * i			,		n + 1			,		UpperCamelCase__		):
                        UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : Optional[Any]											=					0
      for i in range(UpperCamelCase__		):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


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


