
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=[1_0, 2_0, 3_0, 4_0]						,						__SCREAMING_SNAKE_CASE=[1, 1, 2, 1]						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE="relu"						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=None						,						)  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				parent
           UpperCamelCase__    :   List[str]					=				batch_size
           UpperCamelCase__    :   Tuple					=				image_size
           UpperCamelCase__    :   List[str]					=				num_channels
           UpperCamelCase__    :   Optional[int]					=				embeddings_size
           UpperCamelCase__    :   List[str]					=				hidden_sizes
           UpperCamelCase__    :   Any					=				depths
           UpperCamelCase__    :   Tuple					=				is_training
           UpperCamelCase__    :   Optional[int]					=				use_labels
           UpperCamelCase__    :   int					=				hidden_act
           UpperCamelCase__    :   int					=				num_labels
           UpperCamelCase__    :   List[Any]					=				scope
           UpperCamelCase__    :   Union[str, Any]					=				len(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

           UpperCamelCase__    :   Tuple					=				self.get_config()

           return config, pixel_values, labels






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




           """simple docstring"""







           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						,						image_size=self.image_size						,						)






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




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				TFResNetModel(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any					=				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 // 3_2, self.image_size // 3_2)						,						)






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




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.num_labels
           UpperCamelCase__    :   Optional[int]					=				TFResNetForImageClassification(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				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  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.prepare_config_and_inputs()
           UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				config_and_inputs
           UpperCamelCase__    :   Any					=				{'''pixel_values''': pixel_values}
           return config, inputs_dict




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE_		   =  (
         {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				TFResNetModelTester(self  )
           UpperCamelCase__    :   List[Any]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						has_text_modality=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           return






     @unittest.skip(reason='''ResNet does not use inputs_embeds'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

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

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






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




           """simple docstring"""







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






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




           """simple docstring"""







           def check_hidden_states_output(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Union[str, Any]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Optional[Any]					=				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )

                 UpperCamelCase__    :   Tuple					=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 UpperCamelCase__    :   List[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]						,						)

           UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__    :   Optional[Any]					=				['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       UpperCamelCase__    :   Optional[int]					=				layer_type
                       UpperCamelCase__    :   Optional[int]					=				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__    :   int					=				True

                       check_hidden_states_output(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   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  )  ->   Tuple:




           """simple docstring"""







           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   List[Any]					=				TFResNetModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   str					=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




@require_tf
@require_vision
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @cached_property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
               if is_vision_available()
               else None
           )






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




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

           UpperCamelCase__    :   Optional[int]					=				self.default_image_processor
           UpperCamelCase__    :   int					=				prepare_img()
           UpperCamelCase__    :   List[Any]					=				image_processor(images=__SCREAMING_SNAKE_CASE						,						return_tensors='''tf'''  )

           # forward pass
           UpperCamelCase__    :   Optional[Any]					=				model(**__SCREAMING_SNAKE_CASE  )

           # verify the logits
           UpperCamelCase__    :   str					=				tf.TensorShape((1, 1_0_0_0)  )
           self.assertEqual(outputs.logits.shape						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				tf.constant([-11.1069, -9.7877, -8.3777]  )

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




def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 5_0_0_0_0_0_0_0		):
      UpperCamelCase__    :   Any					=				set()
      UpperCamelCase__    :   Any					=				int((limit - 2_4) ** (1 / 2)		)

      UpperCamelCase__    :   Dict					=				set(range(3 ,		prime_square_limit + 1 ,		2		)		)
      primes.add(2		)
      for p in range(3 ,		prime_square_limit + 1 ,		2		):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,		prime_square_limit + 1 ,		UpperCamelCase__		)		)		)

      for primea in primes:
            UpperCamelCase__    :   Dict					=				primea * primea
            for primea in primes:
                  UpperCamelCase__    :   str					=				primea * primea * primea
                  if square + cube >= limit - 1_6:
                        break
                  for primea in primes:
                        UpperCamelCase__    :   Dict					=				primea * primea * primea * primea
                        UpperCamelCase__    :   Tuple					=				square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(UpperCamelCase__		)

      return len(UpperCamelCase__		)


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


