


'''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  __magic_name__							:






       def __init__(      self  ,							snake_case  ,							snake_case=None  ,							snake_case=None  ,							snake_case=None  ,							snake_case="resnet50"  ,							snake_case=3  ,							snake_case=3_2  ,							snake_case=3  ,							snake_case=True  ,							snake_case=True  ,							)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =parent
          _UpperCAmelCase       :       List[str]							   =out_indices if out_indices is not None else [4]
          _UpperCAmelCase       :       Tuple							   =stage_names
          _UpperCAmelCase       :       Any							   =out_features
          _UpperCAmelCase       :       List[Any]							   =backbone
          _UpperCAmelCase       :       Any							   =batch_size
          _UpperCAmelCase       :       Optional[Any]							   =image_size
          _UpperCAmelCase       :       Optional[int]							   =num_channels
          _UpperCAmelCase       :       Tuple							   =use_pretrained_backbone
          _UpperCAmelCase       :       Optional[Any]							   =is_training






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
          _UpperCAmelCase       :       Optional[Any]							   =self.get_config()

          return config, pixel_values






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




          '''simple docstring'''
          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  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =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], 1_4, 1_4)  ,							)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase       :       List[Any]							   =config_and_inputs
          _UpperCAmelCase       :       str							   ={'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
@require_timm
class  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=(TimmBackbone,) if is_torch_available() else ()
       UpperCAmelCase										={"feature-extraction": TimmBackbone} if is_torch_available() else {}
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=False






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =TimmBackboneModelTester(self)
          _UpperCAmelCase       :       Dict							   =ConfigTester(self  ,							config_class=snake_case  ,							has_text_modality=snake_case)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          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)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   ='resnet18'
          _UpperCAmelCase       :       Optional[Any]							   ='microsoft/resnet-18'

          _UpperCAmelCase       :       List[str]							   =AutoBackbone.from_pretrained(snake_case  ,							use_timm_backbone=snake_case)
          _UpperCAmelCase       :       str							   =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       :       str							   =AutoBackbone.from_pretrained(snake_case  ,							use_timm_backbone=snake_case  ,							out_indices=[1, 2, 3])
          _UpperCAmelCase       :       int							   =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)						->  Dict:




          '''simple docstring'''
          pass






       @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          pass






       @unittest.skip('TimmBackbone initialization is managed on the timm side')
       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          pass






       @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          pass






       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          pass






       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          pass






       @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          pass






       @unittest.skip('TimmBackbone doesn\'t support output_attentions.')
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          pass






       @unittest.skip('Safetensors is not supported by timm.')
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          pass






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




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Any:




          '''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(snake_case)
             _UpperCAmelCase       :       Dict							   =inspect.signature(model.forward)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _UpperCAmelCase       :       Any							   =[*signature.parameters.keys()]

             _UpperCAmelCase       :       int							   =['pixel_values']
             self.assertListEqual(arg_names[:1]  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase       :       List[Any]							   =True
          _UpperCAmelCase       :       Union[str, Any]							   =self.has_attentions

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

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

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

          if self.has_attentions:
             _UpperCAmelCase       :       Dict							   =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)						->  Optional[Any]:




          '''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       :       Dict							   =model_class(snake_case)
             model.to(snake_case)
             model.eval()
             _UpperCAmelCase       :       Optional[Any]							   =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       :       str							   =copy.deepcopy(snake_case)
             _UpperCAmelCase       :       List[Any]							   =None
             _UpperCAmelCase       :       Any							   =model_class(snake_case)
             model.to(snake_case)
             model.eval()
             _UpperCAmelCase       :       int							   =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       :       str							   =copy.deepcopy(snake_case)
             _UpperCAmelCase       :       Any							   =False
             _UpperCAmelCase       :       Tuple							   =model_class(snake_case)
             model.to(snake_case)
             model.eval()
             _UpperCAmelCase       :       Optional[Any]							   =model(**snake_case)





'''simple docstring'''






from __future__ import annotations

import bisect





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       int							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       Dict							   =lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         _UpperCAmelCase       :       int							   =mid + 1
      else:
         _UpperCAmelCase       :       Union[str, Any]							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       str							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       List[Any]							   =lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         _UpperCAmelCase       :       Dict							   =mid + 1
      else:
         _UpperCAmelCase       :       Tuple							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_left(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_right(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =0
   _UpperCAmelCase       :       Any							   =len(__lowerCamelCase							) - 1

   while left <= right:
      _UpperCAmelCase       :       int							   =left + (right - left) // 2
      _UpperCAmelCase       :       Union[str, Any]							   =sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         _UpperCAmelCase       :       Optional[Any]							   =midpoint - 1
      else:
         _UpperCAmelCase       :       int							   =midpoint + 1
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =bisect.bisect_left(__lowerCamelCase	,   __lowerCamelCase							)
   if index != len(__lowerCamelCase							) and sorted_collection[index] == item:
      return index
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if right < left:
      return None

   _UpperCAmelCase       :       str							   =left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   midpoint - 1							)
   else:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   midpoint + 1	,   __lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=input('Enter numbers separated by comma:\n').strip()
 lowercase							=sorted(int(item) for item in user_input.split(','))
 lowercase							=int(input('Enter a single number to be found in the list:\n'))
 lowercase							=binary_search(collection, target)
 if result is None:
  print(F"""{target} was not found in {collection}.""")
 else:
  print(F"""{target} was found at position {result} in {collection}.""")

