




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 :  int						,							A_ :  Dict						,							A_ :  int=None						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							A_ :  Any="resnet50"						,							A_ :  Tuple=3						,							A_ :  str=32						,							A_ :  Dict=3						,							A_ :  List[Any]=True						,							A_ :  Any=True						,							)       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   out_indices if out_indices is not None else [4]
       lowerCamelCase_							=   stage_names
       lowerCamelCase_							=   out_features
       lowerCamelCase_							=   backbone
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   image_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   use_pretrained_backbone
       lowerCamelCase_							=   is_training

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

       """simple docstring"""






       lowerCamelCase_							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
       lowerCamelCase_							=   self.get_config()

       return config, pixel_values

 def     a__			(    self :  int  )       ->     str:

       """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     a__			(    self :  List[Any]						,							A_ :  int						,							A_ :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   TimmBackbone(config=A_  )
       model.to(A_  )
       model.eval()
       with torch.no_grad():
             lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(
           result.feature_map[-1].shape						,							(self.batch_size, model.channels[-1], 14, 14)						,							)





 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






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







@require_torch
@require_timm
class   A(						UpperCamelCase							,				UpperCamelCase							,				UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 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     a__			(    self :  Optional[Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   TimmBackboneModelTester(self  )
       lowerCamelCase_							=   ConfigTester(self						,							config_class=A_						,							has_text_modality=A_  )

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

       """simple docstring"""






       lowerCamelCase_							=   'resnet18'
       lowerCamelCase_							=   'microsoft/resnet-18'

       lowerCamelCase_							=   AutoBackbone.from_pretrained(A_						,							use_timm_backbone=A_  )
       lowerCamelCase_							=   AutoBackbone.from_pretrained(A_  )

       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]  )

       lowerCamelCase_							=   AutoBackbone.from_pretrained(A_						,							use_timm_backbone=A_						,							out_indices=[1, 2, 3]  )
       lowerCamelCase_							=   AutoBackbone.from_pretrained(A_						,							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     a__			(    self :  List[str]  )       ->     Dict:

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

 @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'  )
 def     a__			(    self :  Union[str, Any]  )       ->     Optional[int]:

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

 @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'  )
 def     a__			(    self :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       pass

 @unittest.skip('TimmBackbone doesn\'t support output_attentions.'  )
 def     a__			(    self :  str  )       ->     List[Any]:

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

 def     a__			(    self :  Tuple  )       ->     str:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_							=   ['pixel_values']
             self.assertListEqual(arg_names[:1]						,							A_  )

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

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_							=   True
       lowerCamelCase_							=   self.has_attentions

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

       lowerCamelCase_							=   self._prepare_for_class(A_						,							A_  )
       lowerCamelCase_							=   model(**A_  )
       lowerCamelCase_							=   outputs[0][-1]

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

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

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

       self.assertIsNotNone(hidden_states.grad  )

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





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

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=   model_class(A_  )
             model.to(A_  )
             model.eval()
             lowerCamelCase_							=   model(**A_  )

             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
             lowerCamelCase_							=   copy.deepcopy(A_  )
             lowerCamelCase_							=   None
             lowerCamelCase_							=   model_class(A_  )
             model.to(A_  )
             model.eval()
             lowerCamelCase_							=   model(**A_  )

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

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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Dict     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if index == r:
            for j in range(lowercase					):
                  print(data[j]     ,							end=' '					)
            print(' '					)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      lowerCamelCase_							=   arr[i]
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							index + 1     ,							lowercase     ,							i + 1					)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							i + 1					)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Any     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   [0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							0     ,							lowercase     ,							0					)


if __name__ == "__main__":
 # Driver code to check the function above
 lowerCamelCase    :		int			   =       [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu



