'''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    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Any=None    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				Any=None    ,  _UpperCamelCase     :				int="resnet50"    ,  _UpperCamelCase     :				Optional[int]=3    ,  _UpperCamelCase     :				Optional[int]=32    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  )				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						parent
          _lowerCamelCase     :		str 						=						out_indices if out_indices is not None else [4]
          _lowerCamelCase     :		Optional[int] 						=						stage_names
          _lowerCamelCase     :		int 						=						out_features
          _lowerCamelCase     :		Optional[int] 						=						backbone
          _lowerCamelCase     :		Any 						=						batch_size
          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		List[Any] 						=						num_channels
          _lowerCamelCase     :		Any 						=						use_pretrained_backbone
          _lowerCamelCase     :		Optional[int] 						=						is_training



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
          _lowerCamelCase     :		Optional[Any] 						=						self.get_config()

          return config, pixel_values



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->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 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->List[str]:


          """simple docstring"""


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




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


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.prepare_config_and_inputs()
          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						config_and_inputs
          _lowerCamelCase     :		Union[str, Any] 						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict





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





      '''simple docstring'''






      _snake_case    		=       (TimmBackbone,) if is_torch_available() else ()
      _snake_case    		=       {'feature-extraction': TimmBackbone} if is_torch_available() else {}
      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						TimmBackboneModelTester(self)
          _lowerCamelCase     :		Union[str, Any] 						=						ConfigTester(self    ,  config_class=_lowerCAmelCase    ,  has_text_modality=_lowerCAmelCase)



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


          """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)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						"""resnet18"""
          _lowerCamelCase     :		List[Any] 						=						"""microsoft/resnet-18"""

          _lowerCamelCase     :		int 						=						AutoBackbone.from_pretrained(_lowerCAmelCase    ,  use_timm_backbone=_lowerCAmelCase)
          _lowerCamelCase     :		int 						=						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])

          _lowerCamelCase     :		Union[str, Any] 						=						AutoBackbone.from_pretrained(_lowerCAmelCase    ,  use_timm_backbone=_lowerCAmelCase    ,  out_indices=[1, 2, 3])
          _lowerCamelCase     :		str 						=						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     :				Union[str, Any])				->Dict:


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[Any]:


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone initialization is managed on the timm side""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[Any]:


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Optional[Any]:


          """simple docstring"""


          pass



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


          """simple docstring"""


          pass



      @unittest.skip("""model weights aren't tied in TimmBackbone.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Union[str, Any]:


          """simple docstring"""


          pass



      @unittest.skip("""model weights aren't tied in TimmBackbone.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          pass



      @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->str:


          """simple docstring"""


          pass



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


          """simple docstring"""


          pass



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


          """simple docstring"""


          pass



      @unittest.skip("""TimmBackbone doesn't support output_attentions.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          pass



      @unittest.skip("""Safetensors is not supported by timm.""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


          """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     :				Tuple)				->int:


          """simple docstring"""


          pass



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowerCamelCase     :		List[Any] 						=						["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,  _lowerCAmelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->str:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						self.model_tester.prepare_config_and_inputs_for_common()
          _lowerCamelCase     :		List[Any] 						=						True
          _lowerCamelCase     :		List[str] 						=						self.has_attentions

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

          _lowerCamelCase     :		Optional[Any] 						=						self._prepare_for_class(_lowerCAmelCase    ,  _lowerCAmelCase)
          _lowerCamelCase     :		List[str] 						=						model(**_lowerCAmelCase)
          _lowerCamelCase     :		List[Any] 						=						outputs[0][-1]

          # Encoder-/Decoder-only models
          _lowerCamelCase     :		Union[str, Any] 						=						outputs.hidden_states[0]
          hidden_states.retain_grad()

          if self.has_attentions:
              _lowerCamelCase     :		Optional[Any] 						=						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     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

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

              # Check backbone can be initialized with fresh weights
              _lowerCamelCase     :		Optional[int] 						=						copy.deepcopy(_lowerCAmelCase)
              _lowerCamelCase     :		Dict 						=						False
              _lowerCamelCase     :		int 						=						model_class(_lowerCAmelCase)
              model.to(_lowerCAmelCase)
              model.eval()
              _lowerCamelCase     :		Optional[Any] 						=						model(**_lowerCAmelCase)





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		int 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




