


'''simple docstring'''



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
      import torch

      from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Optional[Any]							,				snake_case  :	Tuple							,				snake_case  :	Tuple=None							,				snake_case  :	Dict=None							,				snake_case  :	Tuple=None							,				snake_case  :	Optional[int]="resnet50"							,				snake_case  :	Tuple=3							,				snake_case  :	Any=32							,				snake_case  :	List[str]=3							,				snake_case  :	str=True							,				snake_case  :	Optional[int]=True							,				)		->							Any:
          __UpperCAmelCase			:				int													=    parent
          __UpperCAmelCase			:				List[str]													=    out_indices if out_indices is not None else [4]
          __UpperCAmelCase			:				Tuple													=    stage_names
          __UpperCAmelCase			:				int													=    out_features
          __UpperCAmelCase			:				Optional[Any]													=    backbone
          __UpperCAmelCase			:				Optional[int]													=    batch_size
          __UpperCAmelCase			:				List[str]													=    image_size
          __UpperCAmelCase			:				Any													=    num_channels
          __UpperCAmelCase			:				Union[str, Any]													=    use_pretrained_backbone
          __UpperCAmelCase			:				Tuple													=    is_training

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[Any]:
          __UpperCAmelCase			:				Dict													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
          __UpperCAmelCase			:				List[str]													=    self.get_config()

          return config, pixel_values

     def 				lowerCamelCase__				(       self  :	str    )		->							Dict:
          return TimmBackboneConfig(
              image_size=self.image_size							,				num_channels=self.num_channels							,				out_features=self.out_features							,				out_indices=self.out_indices							,				stage_names=self.stage_names							,				use_pretrained_backbone=self.use_pretrained_backbone							,				backbone=self.backbone							,				)

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[Any]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Tuple													=    TimmBackbone(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          with torch.no_grad():
               __UpperCAmelCase			:				Any													=    model(snake_case    )
          self.parent.assertEqual(
              result.feature_map[-1].shape							,				(self.batch_size, model.channels[-1], 14, 14)							,				)






     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[Any]													=    self.prepare_config_and_inputs()
          __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    config_and_inputs
          __UpperCAmelCase			:				Tuple													=    {'''pixel_values''': pixel_values}
          return config, inputs_dict



@require_torch
@require_timm
class 			a       (	_a			,						_a			,						_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     (TimmBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE :				str							     =     {"feature-extraction": TimmBackbone} if is_torch_available() else {}
     SCREAMING_SNAKE_CASE :				Tuple							     =     False
     SCREAMING_SNAKE_CASE :				Dict							     =     False
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     False
     SCREAMING_SNAKE_CASE :				str							     =     False

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[Any]:
          __UpperCAmelCase			:				List[str]													=    TimmBackboneModelTester(self    )
          __UpperCAmelCase			:				Dict													=    ConfigTester(self							,				config_class=snake_case							,				has_text_modality=snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
          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 				lowerCamelCase__				(       self  :	Any    )		->							List[str]:
          __UpperCAmelCase			:				Tuple													=    '''resnet18'''
          __UpperCAmelCase			:				Optional[Any]													=    '''microsoft/resnet-18'''

          __UpperCAmelCase			:				Optional[Any]													=    AutoBackbone.from_pretrained(snake_case							,				use_timm_backbone=snake_case    )
          __UpperCAmelCase			:				List[Any]													=    AutoBackbone.from_pretrained(snake_case    )

          self.assertEqual(len(timm_model.out_features    )							,				len(transformers_model.out_features    )    )
          self.assertEqual(len(timm_model.stage_names    )							,				len(transformers_model.stage_names    )    )
          self.assertEqual(timm_model.channels							,				transformers_model.channels    )
          # Out indices are set to the last layer by default. For timm models, we don't know
          # the number of layers in advance, so we set it to (-1,), whereas for transformers
          # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
          self.assertEqual(timm_model.out_indices							,				(-1,)    )
          self.assertEqual(transformers_model.out_indices							,				[len(timm_model.stage_names    ) - 1]    )

          __UpperCAmelCase			:				Tuple													=    AutoBackbone.from_pretrained(snake_case							,				use_timm_backbone=snake_case							,				out_indices=[1, 2, 3]    )
          __UpperCAmelCase			:				Union[str, Any]													=    AutoBackbone.from_pretrained(snake_case							,				out_indices=[1, 2, 3]    )

          self.assertEqual(timm_model.out_indices							,				transformers_model.out_indices    )
          self.assertEqual(len(timm_model.out_features    )							,				len(transformers_model.out_features    )    )
          self.assertEqual(timm_model.channels							,				transformers_model.channels    )

     @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''    )
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Any:
          pass

     @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''    )
     def 				lowerCamelCase__				(       self  :	int    )		->							int:
          pass

     @unittest.skip('''TimmBackbone initialization is managed on the timm side'''    )
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[Any]:
          pass

     @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''    )
     def 				lowerCamelCase__				(       self  :	Any    )		->							int:
          pass

     @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''    )
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Any:
          pass

     @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''    )
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							int:
          pass

     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''    )
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          pass

     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''    )
     def 				lowerCamelCase__				(       self  :	int    )		->							Union[str, Any]:
          pass

     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''    )
     def 				lowerCamelCase__				(       self  :	Dict    )		->							str:
          pass

     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''    )
     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          pass

     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''    )
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Optional[int]:
          pass

     @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''    )
     def 				lowerCamelCase__				(       self  :	int    )		->							List[Any]:
          pass

     @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''    )
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Dict:
          pass

     @unittest.skip('''Safetensors is not supported by timm.'''    )
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Union[str, Any]:
          pass

     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Optional[Any]:
          pass

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[Any]:
          __UpperCAmelCase							,     __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase			:				Tuple													=    model_class(snake_case    )
               __UpperCAmelCase			:				Any													=    inspect.signature(model.forward    )
               # 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]							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Dict:
          __UpperCAmelCase							,     __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCAmelCase			:				Optional[int]													=    True
          __UpperCAmelCase			:				str													=    self.has_attentions

          # no need to test all models as different heads yield the same functionality
          __UpperCAmelCase			:				int													=    self.all_model_classes[0]
          __UpperCAmelCase			:				Optional[int]													=    model_class(snake_case    )
          model.to(snake_case    )

          __UpperCAmelCase			:				Optional[int]													=    self._prepare_for_class(snake_case							,				snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    model(**snake_case    )
          __UpperCAmelCase			:				Dict													=    outputs[0][-1]

          # Encoder-/Decoder-only models
          __UpperCAmelCase			:				str													=    outputs.hidden_states[0]
          hidden_states.retain_grad()

          if self.has_attentions:
               __UpperCAmelCase			:				Union[str, Any]													=    outputs.attentions[0]
               attentions.retain_grad()

          output.flatten()[0].backward(retain_graph=snake_case    )

          self.assertIsNotNone(hidden_states.grad    )

          if self.has_attentions:
               self.assertIsNotNone(attentions.grad    )






     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Union[str, Any]:
          __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(snake_case    )
               model.to(snake_case    )
               model.eval()
               __UpperCAmelCase			:				List[str]													=    model(**snake_case    )

               self.assertEqual(len(result.feature_maps    )							,				len(config.out_indices    )    )
               self.assertEqual(len(model.channels    )							,				len(config.out_indices    )    )

               # Check output of last stage is taken if out_features=None, out_indices=None
               __UpperCAmelCase			:				Dict													=    copy.deepcopy(snake_case    )
               __UpperCAmelCase			:				Optional[Any]													=    None
               __UpperCAmelCase			:				List[Any]													=    model_class(snake_case    )
               model.to(snake_case    )
               model.eval()
               __UpperCAmelCase			:				str													=    model(**snake_case    )

               self.assertEqual(len(result.feature_maps    )							,				1    )
               self.assertEqual(len(model.channels    )							,				1    )

               # Check backbone can be initialized with fresh weights
               __UpperCAmelCase			:				List[str]													=    copy.deepcopy(snake_case    )
               __UpperCAmelCase			:				Any													=    False
               __UpperCAmelCase			:				Any													=    model_class(snake_case    )
               model.to(snake_case    )
               model.eval()
               __UpperCAmelCase			:				Tuple													=    model(**snake_case    )



'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    int(number**0.5						)
     return number == sq * sq




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     __UpperCAmelCase			:				int													=    x_den * y_den * z_den
     __UpperCAmelCase			:				int													=    gcd(_lowercase    ,      _lowercase						)
     top //= hcf
     bottom //= hcf
     return top, bottom




def 			_a			(		_lowercase      :			int = 35						):





     '''simple docstring'''






     __UpperCAmelCase			:				set													=    set()
     __UpperCAmelCase			:				int
     __UpperCAmelCase			:				Fraction													=    Fraction(0						)
     __UpperCAmelCase			:				tuple[int, int]

     for x_num in range(1    ,      order + 1						):
          for x_den in range(x_num + 1    ,      order + 1						):
               for y_num in range(1    ,      order + 1						):
                    for y_den in range(y_num + 1    ,      order + 1						):
                         # n=1
                         __UpperCAmelCase			:				Optional[int]													=    x_num * y_den + x_den * y_num
                         __UpperCAmelCase			:				Dict													=    x_den * y_den
                         __UpperCAmelCase			:				List[Any]													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Dict													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[int]													=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         __UpperCAmelCase			:				Any													=    x_den * x_den * y_den * y_den
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Tuple													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Union[str, Any]													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				Union[str, Any]													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

                    # n=-1
                         __UpperCAmelCase			:				Union[str, Any]													=    x_num * y_num
                         __UpperCAmelCase			:				List[Any]													=    x_den * y_num + x_num * y_den
                         __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Optional[Any]													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[Any]													=    x_num * x_num * y_num * y_num
                         __UpperCAmelCase			:				Dict													=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				Any													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				int													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

     for num, den in unique_s:
          total += Fraction(_lowercase    ,      _lowercase						)

     return total.denominator + total.numerator


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