
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							,    _lowerCamelCase							,    _lowerCamelCase=None							,    _lowerCamelCase=None							,    _lowerCamelCase=None							,    _lowerCamelCase="resnet50"							,    _lowerCamelCase=3							,    _lowerCamelCase=32							,    _lowerCamelCase=3							,    _lowerCamelCase=True							,    _lowerCamelCase=True							,    ) ->str:
           SCREAMING_SNAKE_CASE							:							List[str]								=      parent
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      out_indices if out_indices is not None else [4]
           SCREAMING_SNAKE_CASE							:							int								=      stage_names
           SCREAMING_SNAKE_CASE							:							List[str]								=      out_features
           SCREAMING_SNAKE_CASE							:							str								=      backbone
           SCREAMING_SNAKE_CASE							:							List[Any]								=      batch_size
           SCREAMING_SNAKE_CASE							:							int								=      image_size
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      num_channels
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      use_pretrained_backbone
           SCREAMING_SNAKE_CASE							:							List[str]								=      is_training






       def      __lowerCAmelCase			(						self					) ->Tuple:
           SCREAMING_SNAKE_CASE							:							int								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
           SCREAMING_SNAKE_CASE							:							Dict								=      self.get_config()

           return config, pixel_values






       def      __lowerCAmelCase			(						self					) ->Optional[Any]:
           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							,    _lowerCamelCase							,    _lowerCamelCase					) ->int:
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      TimmBackbone(config=_lowerCamelCase					)
           model.to(_lowerCamelCase					)
           model.eval()
           with torch.no_grad():
               SCREAMING_SNAKE_CASE							:							int								=      model(_lowerCamelCase					)
           self.parent.assertEqual(
               result.feature_map[-1].shape							,    (self.batch_size, model.channels[-1], 14, 14)							,    )






       def      __lowerCAmelCase			(						self					) ->List[str]:
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							Optional[int]								=      config_and_inputs
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      {'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
@require_timm
class   a_    (       a__							,  a__							,  a__							,  unittest.TestCase				):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE    : Optional[int]		=   (TimmBackbone,) if is_torch_available() else ()
       __SCREAMING_SNAKE_CASE    : str		=   {'feature-extraction': TimmBackbone} if is_torch_available() else {}
       __SCREAMING_SNAKE_CASE    : str		=   False
       __SCREAMING_SNAKE_CASE    : List[Any]		=   False
       __SCREAMING_SNAKE_CASE    : int		=   False
       __SCREAMING_SNAKE_CASE    : str		=   False






       def      __lowerCAmelCase			(						self					) ->Optional[int]:
           SCREAMING_SNAKE_CASE							:							Any								=      TimmBackboneModelTester(self					)
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      ConfigTester(self							,    config_class=_lowerCamelCase							,    has_text_modality=_lowerCamelCase					)






       def      __lowerCAmelCase			(						self					) ->Tuple:
           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					) ->Optional[Any]:
           SCREAMING_SNAKE_CASE							:							int								=      '''resnet18'''
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      '''microsoft/resnet-18'''

           SCREAMING_SNAKE_CASE							:							List[Any]								=      AutoBackbone.from_pretrained(_lowerCamelCase							,    use_timm_backbone=_lowerCamelCase					)
           SCREAMING_SNAKE_CASE							:							str								=      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]					)

           SCREAMING_SNAKE_CASE							:							Dict								=      AutoBackbone.from_pretrained(_lowerCamelCase							,    use_timm_backbone=_lowerCamelCase							,    out_indices=[1, 2, 3]					)
           SCREAMING_SNAKE_CASE							:							List[Any]								=      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      __lowerCAmelCase			(						self					) ->Optional[Any]:
           pass






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






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






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






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






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






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






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






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






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






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






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






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






       @unittest.skip('''Safetensors is not supported by timm.'''					)
       def      __lowerCAmelCase			(						self					) ->Dict:
           pass






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






       def      __lowerCAmelCase			(						self					) ->str:
           SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							Optional[int]								=      self.model_tester.prepare_config_and_inputs_for_common()

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

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






       def      __lowerCAmelCase			(						self					) ->Union[str, Any]:
           SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							List[Any]								=      self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      True
           SCREAMING_SNAKE_CASE							:							Tuple								=      self.has_attentions

           # no need to test all models as different heads yield the same functionality
           SCREAMING_SNAKE_CASE							:							List[str]								=      self.all_model_classes[0]
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      model_class(_lowerCamelCase					)
           model.to(_lowerCamelCase					)

           SCREAMING_SNAKE_CASE							:							Any								=      self._prepare_for_class(_lowerCamelCase							,    _lowerCamelCase					)
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      model(**_lowerCamelCase					)
           SCREAMING_SNAKE_CASE							:							Dict								=      outputs[0][-1]

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

           if self.has_attentions:
               SCREAMING_SNAKE_CASE							:							List[str]								=      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      __lowerCAmelCase			(						self					) ->str:
           SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							Tuple								=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               SCREAMING_SNAKE_CASE							:							List[str]								=      model_class(_lowerCamelCase					)
               model.to(_lowerCamelCase					)
               model.eval()
               SCREAMING_SNAKE_CASE							:							Optional[Any]								=      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
               SCREAMING_SNAKE_CASE							:							str								=      copy.deepcopy(_lowerCamelCase					)
               SCREAMING_SNAKE_CASE							:							List[Any]								=      None
               SCREAMING_SNAKE_CASE							:							int								=      model_class(_lowerCamelCase					)
               model.to(_lowerCamelCase					)
               model.eval()
               SCREAMING_SNAKE_CASE							:							Any								=      model(**_lowerCamelCase					)

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

               # Check backbone can be initialized with fresh weights
               SCREAMING_SNAKE_CASE							:							Tuple								=      copy.deepcopy(_lowerCamelCase					)
               SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      False
               SCREAMING_SNAKE_CASE							:							List[Any]								=      model_class(_lowerCamelCase					)
               model.to(_lowerCamelCase					)
               model.eval()
               SCREAMING_SNAKE_CASE							:							Optional[int]								=      model(**_lowerCamelCase					)




def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

