import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType







@require_torch
class __snake_case							:
   lowerCAmelCase_								=				()
   lowerCAmelCase_								=				True




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.config_class

     # test default config
     SCREAMING_SNAKE_CASE__     				= config_class()
     self.assertIsNotNone(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= ['''stem'''] + [f"""stage{idx}""" for idx in range(1			,      len(config.depths ) + 1 )]
     self.assertEqual(config.stage_names			,      _UpperCAmelCase )
     self.assertTrue(set(config.out_features ).issubset(set(config.stage_names ) ) )

     # Test out_features and out_indices are correctly set
     # out_features and out_indices both None
     SCREAMING_SNAKE_CASE__     				= config_class(out_features=_UpperCAmelCase			,      out_indices=_UpperCAmelCase )
     self.assertEqual(config.out_features			,      [config.stage_names[-1]] )
     self.assertEqual(config.out_indices			,      [len(config.stage_names ) - 1] )

     # out_features and out_indices both set
     SCREAMING_SNAKE_CASE__     				= config_class(out_features=["""stem""", """stage1"""]			,      out_indices=[0, 1] )
     self.assertEqual(config.out_features			,      ["""stem""", """stage1"""] )
     self.assertEqual(config.out_indices			,      [0, 1] )

     # Only out_features set
     SCREAMING_SNAKE_CASE__     				= config_class(out_features=["""stage1""", """stage3"""] )
     self.assertEqual(config.out_features			,      ["""stage1""", """stage3"""] )
     self.assertEqual(config.out_indices			,      [1, 3] )

     # Only out_indices set
     SCREAMING_SNAKE_CASE__     				= config_class(out_indices=[0, 2] )
     self.assertEqual(config.out_features			,      [config.stage_names[0], config.stage_names[2]] )
     self.assertEqual(config.out_indices			,      [0, 2] )

     # Error raised when out_indices do not correspond to out_features
     with self.assertRaises(_UpperCAmelCase ):
       SCREAMING_SNAKE_CASE__     				= config_class(out_features=["""stage1""", """stage2"""]			,      out_indices=[0, 2] )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       SCREAMING_SNAKE_CASE__     				= inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       SCREAMING_SNAKE_CASE__     				= [*signature.parameters.keys()]
       SCREAMING_SNAKE_CASE__     				= ['''pixel_values''']
       self.assertListEqual(arg_names[:1]			,      _UpperCAmelCase )




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       self.assertEqual(len(model.channels )			,      len(config.out_features ) )
       SCREAMING_SNAKE_CASE__     				= model.num_features
       SCREAMING_SNAKE_CASE__     				= [config.stage_names.index(_UpperCAmelCase ) for feat in config.out_features]
       SCREAMING_SNAKE_CASE__     				= [num_features[idx] for idx in out_indices]
       self.assertListEqual(model.channels			,      _UpperCAmelCase )

       SCREAMING_SNAKE_CASE__     				= copy.deepcopy(_UpperCAmelCase )
       SCREAMING_SNAKE_CASE__     				= None
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       self.assertEqual(len(model.channels )			,      1 )
       self.assertListEqual(model.channels			,      [num_features[-1]] )

       SCREAMING_SNAKE_CASE__     				= copy.deepcopy(_UpperCAmelCase )
       SCREAMING_SNAKE_CASE__     				= None
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       self.assertEqual(len(model.channels )			,      1 )
       self.assertListEqual(model.channels			,      [num_features[-1]] )




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       SCREAMING_SNAKE_CASE__     				= model(**_UpperCAmelCase )

       self.assertEqual(len(result.feature_maps )			,      len(config.out_features ) )
       self.assertEqual(len(model.channels )			,      len(config.out_features ) )
       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__     				= copy.deepcopy(_UpperCAmelCase )
       SCREAMING_SNAKE_CASE__     				= None
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       SCREAMING_SNAKE_CASE__     				= model(**_UpperCAmelCase )

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

       SCREAMING_SNAKE_CASE__     				= copy.deepcopy(_UpperCAmelCase )
       SCREAMING_SNAKE_CASE__     				= None
       SCREAMING_SNAKE_CASE__     				= model_class(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       SCREAMING_SNAKE_CASE__     				= model(**_UpperCAmelCase )

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

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




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= backbone_class(_UpperCAmelCase )

       self.assertTrue(hasattr(_UpperCAmelCase			,      """backbone_type""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """stage_names""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """num_features""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """out_indices""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """out_features""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """out_feature_channels""" ) )
       self.assertTrue(hasattr(_UpperCAmelCase			,      """channels""" ) )

       self.assertIsInstance(backbone.backbone_type			,      _UpperCAmelCase )
       # Verify num_features has been initialized in the backbone init
       self.assertIsNotNone(backbone.num_features )
       self.assertTrue(len(backbone.channels ) == len(backbone.out_indices ) )
       self.assertTrue(len(backbone.stage_names ) == len(backbone.num_features ) )
       self.assertTrue(len(backbone.channels ) <= len(backbone.num_features ) )
       self.assertTrue(len(backbone.out_feature_channels ) == len(backbone.stage_names ) )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__     				= inputs_dict['''pixel_values'''].shape[0]

     for backbone_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= backbone_class(_UpperCAmelCase )
       backbone.to(_UpperCAmelCase )
       backbone.eval()

       SCREAMING_SNAKE_CASE__     				= backbone(**_UpperCAmelCase )

       # Test default outputs and verify feature maps
       self.assertIsInstance(outputs.feature_maps			,      _UpperCAmelCase )
       self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
       for feature_map, n_channels in zip(outputs.feature_maps			,      backbone.channels ):
         self.assertTrue(feature_map.shape[:2]			,      (batch_size, n_channels) )
       self.assertIsNone(outputs.hidden_states )
       self.assertIsNone(outputs.attentions )

       # Test output_hidden_states=True
       SCREAMING_SNAKE_CASE__     				= backbone(**_UpperCAmelCase			,      output_hidden_states=_UpperCAmelCase )
       self.assertIsNotNone(outputs.hidden_states )
       self.assertTrue(len(outputs.hidden_states )			,      len(backbone.stage_names ) )
       for hidden_state, n_channels in zip(outputs.hidden_states			,      backbone.channels ):
         self.assertTrue(hidden_state.shape[:2]			,      (batch_size, n_channels) )

       # Test output_attentions=True
       if self.has_attentions:
         SCREAMING_SNAKE_CASE__     				= backbone(**_UpperCAmelCase			,      output_attentions=_UpperCAmelCase )
         self.assertIsNotNone(outputs.attentions )








'''simple docstring'''







import unittest

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


if is_torch_available():
  import torch

  from transformers import CamembertModel




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

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)