






"""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 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     int,       _lowerCamelCase :     str=None,       _lowerCamelCase :     str=None,       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     int="resnet50",       _lowerCamelCase :     Tuple=3,       _lowerCamelCase :     List[str]=32,       _lowerCamelCase :     int=3,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     Tuple=True,       ):

     '''simple docstring'''
     __A									= parent
     __A									= out_indices if out_indices is not None else [4]
     __A									= stage_names
     __A									= out_features
     __A									= backbone
     __A									= batch_size
     __A									= image_size
     __A									= num_channels
     __A									= use_pretrained_backbone
     __A									= is_training


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
     __A									= self.get_config()

     return config, pixel_values


   def 				_SCREAMING_SNAKE_CASE				(	self :     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 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Any,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A									= TimmBackbone(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     with torch.no_grad():
       __A									= model(_lowerCamelCase  )
     self.parent.assertEqual(
         result.feature_map[-1].shape,       (self.batch_size, model.channels[-1], 14, 14),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     __A			,    __A									= config_and_inputs
     __A									= {'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_torch
@require_timm
class 					snake_case		(							_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[str]						 = (TimmBackbone,) if is_torch_available() else ()
   A_						:	Tuple						 = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
   A_						:	int						 = False
   A_						:	Tuple						 = False
   A_						:	Optional[int]						 = False
   A_						:	int						 = False


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= TimmBackboneModelTester(self  )
     __A									= ConfigTester(self,       config_class=_lowerCamelCase,       has_text_modality=_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''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 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= '''resnet18'''
     __A									= '''microsoft/resnet-18'''

     __A									= AutoBackbone.from_pretrained(_lowerCamelCase,       use_timm_backbone=_lowerCamelCase  )
     __A									= AutoBackbone.from_pretrained(_lowerCamelCase  )

     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]  )

     __A									= AutoBackbone.from_pretrained(_lowerCamelCase,       use_timm_backbone=_lowerCamelCase,       out_indices=[1, 2, 3]  )
     __A									= AutoBackbone.from_pretrained(_lowerCamelCase,       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 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     pass


   @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     pass


   @unittest.skip('''TimmBackbone initialization is managed on the timm side'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     pass


   @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     pass


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

     '''simple docstring'''
     pass


   @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     pass


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

     '''simple docstring'''
     pass


   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     pass


   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     pass


   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     pass


   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     pass


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

     '''simple docstring'''
     pass


   @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     pass


   @unittest.skip('''Safetensors is not supported by timm.'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     pass


   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

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

       __A									= ['''pixel_values''']
       self.assertListEqual(arg_names[:1],       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()
     __A									= True
     __A									= self.has_attentions

     # no need to test all models as different heads yield the same functionality
     __A									= self.all_model_classes[0]
     __A									= model_class(_lowerCamelCase  )
     model.to(_lowerCamelCase  )

     __A									= self._prepare_for_class(_lowerCamelCase,       _lowerCamelCase  )
     __A									= model(**_lowerCamelCase  )
     __A									= outputs[0][-1]

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

     if self.has_attentions:
       __A									= outputs.attentions[0]
       attentions.retain_grad()

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

     self.assertIsNotNone(hidden_states.grad  )

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






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A									= model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       __A									= model(**_lowerCamelCase  )

       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
       __A									= copy.deepcopy(_lowerCamelCase  )
       __A									= None
       __A									= model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       __A									= model(**_lowerCamelCase  )

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

       # Check backbone can be initialized with fresh weights
       __A									= copy.deepcopy(_lowerCamelCase  )
       __A									= False
       __A									= model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       __A									= model(**_lowerCamelCase  )












"""simple docstring"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




