'''simple docstring'''

import copy
import inspect

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




@require_torch
class    lowerCamelCase__  :
   __UpperCAmelCase           =						()
   __UpperCAmelCase           =						True



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.config_class

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

        # Only out_indices set
        _UpperCamelCase  :Optional[int]       =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(lowerCamelCase__     ):
             _UpperCamelCase  :Dict       =config_class(out_features=["""stage1""", """stage2"""]					,      out_indices=[0, 2]     )



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :Dict       =model_class(lowerCamelCase__     )
             _UpperCamelCase  :List[Any]       =inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _UpperCamelCase  :Any       =[*signature.parameters.keys()]
             _UpperCamelCase  :List[str]       =["pixel_values"]
             self.assertListEqual(arg_names[:1]					,      lowerCamelCase__     )



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :str       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :Any       =model_class(lowerCamelCase__     )
             self.assertEqual(len(model.channels     )					,      len(config.out_features     )     )
             _UpperCamelCase  :Any       =model.num_features
             _UpperCamelCase  :List[str]       =[config.stage_names.index(lowerCamelCase__     ) for feat in config.out_features]
             _UpperCamelCase  :Optional[Any]       =[num_features[idx] for idx in out_indices]
             self.assertListEqual(model.channels					,      lowerCamelCase__     )

             _UpperCamelCase  :Optional[int]       =copy.deepcopy(lowerCamelCase__     )
             _UpperCamelCase  :int       =None
             _UpperCamelCase  :Optional[int]       =model_class(lowerCamelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )

             _UpperCamelCase  :List[str]       =copy.deepcopy(lowerCamelCase__     )
             _UpperCamelCase  :List[str]       =None
             _UpperCamelCase  :List[str]       =model_class(lowerCamelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :int       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :str       =model_class(lowerCamelCase__     )
             model.to(lowerCamelCase__     )
             model.eval()
             _UpperCamelCase  :Tuple       =model(**lowerCamelCase__     )

             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
             _UpperCamelCase  :List[str]       =copy.deepcopy(lowerCamelCase__     )
             _UpperCamelCase  :Union[str, Any]       =None
             _UpperCamelCase  :int       =model_class(lowerCamelCase__     )
             model.to(lowerCamelCase__     )
             model.eval()
             _UpperCamelCase  :Union[str, Any]       =model(**lowerCamelCase__     )

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

             _UpperCamelCase  :Dict       =copy.deepcopy(lowerCamelCase__     )
             _UpperCamelCase  :List[str]       =None
             _UpperCamelCase  :Dict       =model_class(lowerCamelCase__     )
             model.to(lowerCamelCase__     )
             model.eval()
             _UpperCamelCase  :Union[str, Any]       =model(**lowerCamelCase__     )

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

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



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs_for_common()

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :Optional[Any]       =backbone_class(lowerCamelCase__     )

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

             self.assertIsInstance(backbone.backbone_type					,      lowerCamelCase__     )
             # 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     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :int       =self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase  :Tuple       =inputs_dict["pixel_values"].shape[0]

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :int       =backbone_class(lowerCamelCase__     )
             backbone.to(lowerCamelCase__     )
             backbone.eval()

             _UpperCamelCase  :str       =backbone(**lowerCamelCase__     )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps					,      lowerCamelCase__     )
             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
             _UpperCamelCase  :Dict       =backbone(**lowerCamelCase__					,      output_hidden_states=lowerCamelCase__     )
             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:
                  _UpperCamelCase  :Optional[int]       =backbone(**lowerCamelCase__					,      output_attentions=lowerCamelCase__     )
                  self.assertIsNotNone(outputs.attentions     )



'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



