

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    _lowercase				:

   def __init__(				self	:    int			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[Any]=None			,						lowerCamelCase__	:    Any=None			,						lowerCamelCase__	:    int=None			,						lowerCamelCase__	:    Union[str, Any]="resnet50"			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    Any=3_2			,						lowerCamelCase__	:    Optional[Any]=3			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    Optional[Any]=True			,						) ->			List[Any]:
         """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     UpperCamelCase					(				self	:    Any       ) ->			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     UpperCamelCase					(				self	:    List[str]       ) ->			List[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     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    int       ) ->			Union[str, 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], 1_4, 1_4)			,						)





   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			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    _lowercase				(					__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Optional[int]  					=    (TimmBackbone,) if is_torch_available() else ()
   _lowercase     :       Dict  					=    {'feature-extraction': TimmBackbone} if is_torch_available() else {}
   _lowercase     :       Tuple  					=    False
   _lowercase     :       Optional[int]  					=    False
   _lowercase     :       Any  					=    False
   _lowercase     :       Union[str, Any]  					=    False

   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[Any]:
         """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     UpperCamelCase					(				self	:    Tuple       ) ->			Optional[Any]:
         """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     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			Optional[int]:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone initialization is managed on the timm side'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			Tuple:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''       )
   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[Any]:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''       )
   def     UpperCamelCase					(				self	:    Any       ) ->			Tuple:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			Optional[int]:
         """simple docstring"""





         pass

   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''       )
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         pass

   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''       )
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         pass

   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''       )
   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





         pass

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





         pass

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





         pass

   @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''       )
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         pass

   @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''       )
   def     UpperCamelCase					(				self	:    List[str]       ) ->			int:
         """simple docstring"""





         pass

   @unittest.skip('''Safetensors is not supported by timm.'''       )
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Dict:
         """simple docstring"""





         pass

   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
   def     UpperCamelCase					(				self	:    int       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			List[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__       )
               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     UpperCamelCase					(				self	:    List[str]       ) ->			Union[str, Any]:
         """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     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			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__       )








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





