




'''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							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Dict=3				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=32				,     SCREAMING_SNAKE_CASE_							:       str=3				,     SCREAMING_SNAKE_CASE_							:       List[Any]=10				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=[10, 20, 30, 40]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=[1, 1, 2, 1]				,     SCREAMING_SNAKE_CASE_							:       Any=True				,     SCREAMING_SNAKE_CASE_							:       Dict=True				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]="relu"				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=3				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=["stage2", "stage3", "stage4"]				,     SCREAMING_SNAKE_CASE_							:       List[Any]=[2, 3, 4]				,     ) ->		Optional[int]:







       '''simple docstring'''



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



  def 				_snake_case    (   self							:       Optional[Any]  ) ->		int:







       '''simple docstring'''



       A:		Dict        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       A:		Optional[Any]        =		self.get_config()

       return config, pixel_values, labels



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







       '''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				,     out_features=self.out_features				,     out_indices=self.out_indices				,     )



  def 				_snake_case    (   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       List[str]  ) ->		Any:







       '''simple docstring'''



       A:		Tuple        =		ResNetModel(config=SCREAMING_SNAKE_CASE_  )
       model.to(SCREAMING_SNAKE_CASE_  )
       model.eval()
       A:		Union[str, 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 // 32, self.image_size // 32)				,     )



  def 				_snake_case    (   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Optional[int]:







       '''simple docstring'''



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



  def 				_snake_case    (   self							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]  ) ->		Optional[Any]:







       '''simple docstring'''



       A:		Union[str, Any]        =		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:		int        =		None
       A:		List[str]        =		ResNetBackbone(config=SCREAMING_SNAKE_CASE_  )
       model.to(SCREAMING_SNAKE_CASE_  )
       model.eval()
       A:		Optional[int]        =		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 				_snake_case    (   self							:       Optional[Any]  ) ->		Union[str, Any]:







       '''simple docstring'''



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






@require_torch
class 	lowerCAmelCase_  (					UpperCAmelCase_					,				UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			List[str]        =							(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  UpperCamelCase_ :			int        =							(
      {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCamelCase_ :			Dict        =							True
  UpperCamelCase_ :			Tuple        =							False
  UpperCamelCase_ :			Optional[int]        =							False
  UpperCamelCase_ :			List[Any]        =							False
  UpperCamelCase_ :			Dict        =							False



  def 				_snake_case    (   self							:       str  ) ->		Tuple:







       '''simple docstring'''



       A:		List[str]        =		ResNetModelTester(self  )
       A:		str        =		ConfigTester(self				,     config_class=SCREAMING_SNAKE_CASE_				,     has_text_modality=SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       Dict  ) ->		Tuple:







       '''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 				_snake_case    (   self							:       Tuple  ) ->		str:







       '''simple docstring'''



       return



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







       '''simple docstring'''



       pass



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







       '''simple docstring'''



       pass



  def 				_snake_case    (   self							:       int  ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

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

            A:		Any        =		['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,     SCREAMING_SNAKE_CASE_  )



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







       '''simple docstring'''



       A:		List[Any]        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       str  ) ->		List[Any]:







       '''simple docstring'''



       A:		Optional[int]        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       Dict  ) ->		Tuple:







       '''simple docstring'''



       A:		str        =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:		List[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 				_snake_case    (   self							:       Optional[int]  ) ->		Any:







       '''simple docstring'''



       def check_hidden_states_output(SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       Optional[int]  ):
            A:		Optional[Any]        =		model_class(SCREAMING_SNAKE_CASE_  )
            model.to(SCREAMING_SNAKE_CASE_  )
            model.eval()

            with torch.no_grad():
                 A:		List[str]        =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )  )

            A:		Union[str, Any]        =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A:		List[str]        =		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:		int        =		self.model_tester.prepare_config_and_inputs_for_common()
       A:		List[str]        =		['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 A:		Optional[Any]        =		layer_type
                 A:		List[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"]
                 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 				_snake_case    (   self							:       Optional[Any]  ) ->		int:







       '''simple docstring'''



       pass



  def 				_snake_case    (   self							:       Optional[Any]  ) ->		Tuple:







       '''simple docstring'''



       A:		Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_  )




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







       '''simple docstring'''



       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A:		Optional[Any]        =		ResNetModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )






def 				SCREAMING_SNAKE_CASE(      ) ->   Optional[Any]:
     A:		Optional[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 				_snake_case    (   self							:       Tuple  ) ->		Tuple:







       '''simple docstring'''



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




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







       '''simple docstring'''



       A:		Union[str, Any]        =		ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(SCREAMING_SNAKE_CASE_  )

       A:		List[Any]        =		self.default_image_processor
       A:		Tuple        =		prepare_img()
       A:		Optional[Any]        =		image_processor(images=SCREAMING_SNAKE_CASE_				,     return_tensors='''pt'''  ).to(SCREAMING_SNAKE_CASE_  )

       # forward pass
       with torch.no_grad():
            A:		Optional[int]        =		model(**SCREAMING_SNAKE_CASE_  )

       # verify the logits
       A:		Union[str, Any]        =		torch.Size((1, 10_00)  )
       self.assertEqual(outputs.logits.shape				,     SCREAMING_SNAKE_CASE_  )

       A:		Union[str, Any]        =		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_  (					UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			Any        =							(ResNetBackbone,) if is_torch_available() else ()
  UpperCamelCase_ :			str        =							False
  UpperCamelCase_ :			Union[str, Any]        =							ResNetConfig



  def 				_snake_case    (   self							:       Dict  ) ->		Optional[int]:







       '''simple docstring'''



       A:		Union[str, Any]        =		ResNetModelTester(self  )












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase  ) ->   float:
     A:		str        =		[1 / first_term]
     A:		Optional[int]        =		1 / first_term
     for _ in range(number_of_terms - 1  ):
          first_term += common_difference
          arithmetic_progression.append(__lowercase  )
     A:		int        =		[1 / step for step in arithmetic_progression]
     return sum(__lowercase  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))






