
import copy
import inspect

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





@require_torch
class 							UpperCAmelCase   :




 '''simple docstring'''





 snake_case_      					=  ()
 snake_case_      					=  True






 def        UpperCamelCase_						( self      :				Optional[Any]				):
     __A         =				self.config_class

     # test default config
     __A         =				config_class()
     self.assertIsNotNone(A				)
     __A         =				["stem"] + [f'''stage{idx}''' for idx in range(1							,len(config.depths				) + 1				)]
     self.assertEqual(config.stage_names							,A				)
     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
     __A         =				config_class(out_features=A							,out_indices=A				)
     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
     __A         =				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
     __A         =				config_class(out_features=["stage1", "stage3"]				)
     self.assertEqual(config.out_features							,["stage1", "stage3"]				)
     self.assertEqual(config.out_indices							,[1, 3]				)

     # Only out_indices set
     __A         =				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(A				):
         __A         =				config_class(out_features=["stage1", "stage2"]							,out_indices=[0, 2]				)






 def        UpperCamelCase_						( self      :				List[str]				):
     __A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         __A         =				model_class(A				)
         __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]							,A				)






 def        UpperCamelCase_						( self      :				int				):
     __A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






 def        UpperCamelCase_						( self      :				Optional[Any]				):
     __A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         __A         =				model_class(A				)
         model.to(A				)
         model.eval()
         __A         =				model(**A				)

         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
         __A         =				copy.deepcopy(A				)
         __A         =				None
         __A         =				model_class(A				)
         model.to(A				)
         model.eval()
         __A         =				model(**A				)

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

         __A         =				copy.deepcopy(A				)
         __A         =				None
         __A         =				model_class(A				)
         model.to(A				)
         model.eval()
         __A         =				model(**A				)

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

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






 def        UpperCamelCase_						( self      :				Dict				):
     __A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
         __A         =				backbone_class(A				)

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

         self.assertIsInstance(backbone.backbone_type							,A				)
         # 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        UpperCamelCase_						( self      :				int				):
     __A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()
     __A         =				inputs_dict["pixel_values"].shape[0]

     for backbone_class in self.all_model_classes:
         __A         =				backbone_class(A				)
         backbone.to(A				)
         backbone.eval()

         __A         =				backbone(**A				)

         # Test default outputs and verify feature maps
         self.assertIsInstance(outputs.feature_maps							,A				)
         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
         __A         =				backbone(**A							,output_hidden_states=A				)
         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:
             __A         =				backbone(**A							,output_attentions=A				)
             self.assertIsNotNone(outputs.attentions				)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
