

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       __UpperCAmelCase  :



   def __init__(							self							:  Optional[Any],	__A							:  int,	__A							:  List[str]=3,	__A							:  Dict=3_2,	__A							:  Dict=3,	__A							:  Optional[Any]=1_0,	__A							:  List[str]=[1_0, 2_0, 3_0, 4_0],	__A							:  int=[1, 1, 2, 1],	__A							:  Optional[int]=True,	__A							:  Optional[Any]=True,	__A							:  Dict="relu",	__A							:  List[Any]=3,	__A							:  Any=None,	__A							:  Optional[Any]=["stage2", "stage3", "stage4"],	__A							:  int=[2, 3, 4],	):
     UpperCAmelCase :							Optional[Any]								=				parent
     UpperCAmelCase :							Dict								=				batch_size
     UpperCAmelCase :							List[str]								=				image_size
     UpperCAmelCase :							List[Any]								=				num_channels
     UpperCAmelCase :							Union[str, Any]								=				embeddings_size
     UpperCAmelCase :							Optional[Any]								=				hidden_sizes
     UpperCAmelCase :							str								=				depths
     UpperCAmelCase :							Any								=				is_training
     UpperCAmelCase :							Optional[int]								=				use_labels
     UpperCAmelCase :							Tuple								=				hidden_act
     UpperCAmelCase :							str								=				num_labels
     UpperCAmelCase :							int								=				scope
     UpperCAmelCase :							List[Any]								=				len(__A		)
     UpperCAmelCase :							Optional[int]								=				out_features
     UpperCAmelCase :							Optional[Any]								=				out_indices



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Optional[Any]								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

     UpperCAmelCase :							List[str]								=				self.get_config()

     return config, pixel_values, labels



   def  __magic_name__					(							self							:  Tuple		):
     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  __magic_name__					(							self							:  Tuple,	__A							:  int,	__A							:  List[str],	__A							:  Any		):
     UpperCAmelCase :							Any								=				ResNetModel(config=__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							Tuple								=				model(__A		)
     # 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  __magic_name__					(							self							:  Union[str, Any],	__A							:  str,	__A							:  List[str],	__A							:  Any		):
     UpperCAmelCase :							List[str]								=				self.num_labels
     UpperCAmelCase :							Optional[int]								=				ResNetForImageClassification(__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							Any								=				model(__A,	labels=__A		)
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)		)



   def  __magic_name__					(							self							:  str,	__A							:  str,	__A							:  str,	__A							:  Tuple		):
     UpperCAmelCase :							int								=				ResNetBackbone(config=__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							Tuple								=				model(__A		)

     # 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
     UpperCAmelCase :							Any								=				None
     UpperCAmelCase :							str								=				ResNetBackbone(config=__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							List[str]								=				model(__A		)

     # 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  __magic_name__					(							self							:  Any		):
     UpperCAmelCase :							Any								=				self.prepare_config_and_inputs()
     UpperCAmelCase :							Tuple								=				config_and_inputs
     UpperCAmelCase :							int								=				{"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class       __UpperCAmelCase  ( A__    ,						A__    ,						unittest.TestCase		):
   UpperCamelCase       =  (
       (
           ResNetModel,
           ResNetForImageClassification,
           ResNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   UpperCamelCase       =  (
       {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
       if is_torch_available()
       else {}
   )

   UpperCamelCase       =  True
   UpperCamelCase       =  False
   UpperCamelCase       =  False
   UpperCamelCase       =  False
   UpperCamelCase       =  False



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Union[str, Any]								=				ResNetModelTester(self		)
     UpperCAmelCase :							Optional[Any]								=				ConfigTester(self,	config_class=__A,	has_text_modality=__A		)



   def  __magic_name__					(							self							:  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  __magic_name__					(							self							:  int		):
     return



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



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



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							int								=				self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase :							str								=				["pixel_values"]
       self.assertListEqual(arg_names[:1],	__A		)



   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase :							List[str]								=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A		)



   def  __magic_name__					(							self							:  List[Any]		):
     UpperCAmelCase :							List[Any]								=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*__A		)



   def  __magic_name__					(							self							:  Any		):
     UpperCAmelCase :							int								=				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       UpperCAmelCase :							str								=				model_class(config=__A		)
       for name, module in model.named_modules():
         if isinstance(__A,	(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  __magic_name__					(							self							:  int		):






     def check_hidden_states_output(__A							:  List[Any],	__A							:  Optional[int],	__A							:  Optional[int]		):
       UpperCAmelCase :							Tuple								=				model_class(__A		)
       model.to(__A		)
       model.eval()

       with torch.no_grad():
         UpperCAmelCase :							Dict								=				model(**self._prepare_for_class(__A,	__A		)		)

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

       UpperCAmelCase :							List[Any]								=				self.model_tester.num_stages
       self.assertEqual(len(__A		),	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 :							Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase :							Tuple								=				["basic", "bottleneck"]
     for model_class in self.all_model_classes:
       for layer_type in layers_type:
         UpperCAmelCase :							Union[str, Any]								=				layer_type
         UpperCAmelCase :							Any								=				True
         check_hidden_states_output(__A,	__A,	__A		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         UpperCAmelCase :							Union[str, Any]								=				True

         check_hidden_states_output(__A,	__A,	__A		)



   @unittest.skip(reason='''ResNet does not use feedforward chunking'''		)
   def  __magic_name__					(							self							:  Optional[int]		):
     pass



   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							str								=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__A		)





   @slow
   def  __magic_name__					(							self							:  Dict		):






     for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase :							Tuple								=				ResNetModel.from_pretrained(__A		)
       self.assertIsNotNone(__A		)






def     a__  (	)						->		int:
  UpperCAmelCase :							str								=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
  return image





@require_torch
@require_vision
class       __UpperCAmelCase  ( unittest.TestCase		):



   @cached_property
   def  __magic_name__					(							self							:  Optional[Any]		):
     return (
         AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
         if is_vision_available()
         else None
     )





   @slow
   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							List[Any]								=				ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__A		)

     UpperCAmelCase :							str								=				self.default_image_processor
     UpperCAmelCase :							Any								=				prepare_img()
     UpperCAmelCase :							List[str]								=				image_processor(images=__A,	return_tensors='''pt'''		).to(__A		)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase :							int								=				model(**__A		)

     # verify the logits
     UpperCAmelCase :							Optional[int]								=				torch.Size((1, 1_0_0_0)		)
     self.assertEqual(outputs.logits.shape,	__A		)

     UpperCAmelCase :							Optional[int]								=				torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]		).to(__A		)

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






@require_torch
class       __UpperCAmelCase  ( A__    ,						unittest.TestCase		):
   UpperCamelCase       =  (ResNetBackbone,) if is_torch_available() else ()
   UpperCamelCase       =  False
   UpperCamelCase       =  ResNetConfig



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Optional[int]								=				ResNetModelTester(self		)








import decimal

def     a__  (	)						->		int:
  UpperCAmelCase :							List[Any]								=				0
  UpperCAmelCase :							Dict								=				decimal.Context(prec=105    )
  for i in range(2       ,				100    ):
    UpperCAmelCase :							List[str]								=				decimal.Decimal(UpperCAmelCase    )
    UpperCAmelCase :							List[str]								=				number.sqrt(UpperCAmelCase    )
    if len(str(UpperCAmelCase    )    ) > 1:
      answer += int(str(UpperCAmelCase    )[0]    )
      UpperCAmelCase :							str								=				str(UpperCAmelCase    )[2:101]
      answer += sum(int(UpperCAmelCase    ) for x in sqrt_number_str    )
  return answer


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution() = }""")





