



'''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  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					int="resnet50"  ,						UpperCamelCase__      :					Tuple=3  ,						UpperCamelCase__      :					List[str]=3_2  ,						UpperCamelCase__      :					int=3  ,						UpperCamelCase__      :					Optional[int]=True  ,						UpperCamelCase__      :					Tuple=True  ,						):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







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

       return config, pixel_values






    def  A       (  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  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  TimmBackbone(config=_lowerCamelCase )
       model.to(_lowerCamelCase )
       model.eval()
       with torch.no_grad():
          UpperCamelCase					=  model(_lowerCamelCase )
       self.parent.assertEqual(
           result.feature_map[-1].shape  ,						(self.batch_size, model.channels[-1], 1_4, 1_4)  ,						)






    def  A       (  self      :					Dict ):


       """simple docstring"""







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

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






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (TimmBackbone,) if is_torch_available() else ()
    _SCREAMING_SNAKE_CASE	  =  {"feature-extraction": TimmBackbone} if is_torch_available() else {}
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  TimmBackboneModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=_lowerCamelCase  ,						has_text_modality=_lowerCamelCase )






    def  A       (  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  A       (  self      :					str ):


       """simple docstring"""







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

       UpperCamelCase					=  AutoBackbone.from_pretrained(_lowerCamelCase  ,						use_timm_backbone=_lowerCamelCase )
       UpperCamelCase					=  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] )

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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






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


       """simple docstring"""







       pass






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

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






    def  A       (  self      :					int ):


       """simple docstring"""







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

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

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

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

       if self.has_attentions:
          UpperCamelCase					=  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  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  model_class(_lowerCamelCase )
          model.to(_lowerCamelCase )
          model.eval()
          UpperCamelCase					=  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
          UpperCamelCase					=  copy.deepcopy(_lowerCamelCase )
          UpperCamelCase					=  None
          UpperCamelCase					=  model_class(_lowerCamelCase )
          model.to(_lowerCamelCase )
          model.eval()
          UpperCamelCase					=  model(**_lowerCamelCase )

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

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









"""simple docstring"""


import re



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]'''		,       str_ )]



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= split_input(str_ )
  return "".join(
      [''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= split_input(__UpperCamelCase )
    if upper:
      __A									= ''''''.join(
          [
              separator.join([char.upper() for char in sub_str] )
              for sub_str in string_split
          ] )
    else:
      __A									= ''''''.join(
          [
              separator.join([char.lower() for char in sub_str] )
              for sub_str in string_split
          ] )
    return res_str
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return to_simple_case(__UpperCamelCase )



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= to_simple_case(__UpperCamelCase )
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''_''' )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''-''' )


if __name__ == "__main__":
       __import__('doctest').testmod()




