

'''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   :
				snake_case_							     = ()
				snake_case_							     = True

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.config_class

					# test default config
					snake_case							=					config_class()
					self.assertIsNotNone(lowercase_ )
					snake_case							=					['stem'] + [F'''stage{idx}''' for idx in range(1,						len(config.depths ) + 1 )]
					self.assertEqual(config.stage_names,						lowercase_ )
					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
					snake_case							=					config_class(out_features=lowercase_,						out_indices=lowercase_ )
					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
					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
					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
					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(lowercase_ ):
						snake_case							=					config_class(out_features=['stage1', 'stage2'],						out_indices=[0, 2] )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()
						snake_case							=					model(**lowercase_ )

						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
						snake_case							=					copy.deepcopy(lowercase_ )
						snake_case							=					None
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()
						snake_case							=					model(**lowercase_ )

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

						snake_case							=					copy.deepcopy(lowercase_ )
						snake_case							=					None
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()
						snake_case							=					model(**lowercase_ )

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

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

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					for backbone_class in self.all_model_classes:
						snake_case							=					backbone_class(lowercase_ )

						self.assertTrue(hasattr(lowercase_,						'backbone_type' ) )
						self.assertTrue(hasattr(lowercase_,						'stage_names' ) )
						self.assertTrue(hasattr(lowercase_,						'num_features' ) )
						self.assertTrue(hasattr(lowercase_,						'out_indices' ) )
						self.assertTrue(hasattr(lowercase_,						'out_features' ) )
						self.assertTrue(hasattr(lowercase_,						'out_feature_channels' ) )
						self.assertTrue(hasattr(lowercase_,						'channels' ) )

						self.assertIsInstance(backbone.backbone_type,						lowercase_ )
						# 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 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					inputs_dict['pixel_values'].shape[0]

					for backbone_class in self.all_model_classes:
						snake_case							=					backbone_class(lowercase_ )
						backbone.to(lowercase_ )
						backbone.eval()

						snake_case							=					backbone(**lowercase_ )

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








import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__A     						=      logging.get_logger(__name__)



class     lowercase_       (    __lowercase	):



      def __init__(     self :							List[str]     ,   *A__ :							Tuple     ,   **A__ :							int						)       ->	None:
        warnings.warn(
            '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DetrImageProcessor instead.'''     ,   A__     ,   )
        super().__init__(*A__     ,   **A__						)

