



import copy
import inspect

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



@require_torch
class 			_lowercase							:





	"""simple docstring"""
	__A					=							()
	__A					=							True




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.config_class

						# test default config
						a        =     config_class()
						self.assertIsNotNone(lowerCamelCase_	)
						a        =     ["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
						a        =     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
						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(lowerCamelCase_	):
											a        =     config_class(out_features=["stage1", "stage2"]	, out_indices=[0, 2]	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a        =     model_class(lowerCamelCase_	)
											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]	, lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a        =     model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.eval()
											a        =     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
											a        =     copy.deepcopy(lowerCamelCase_	)
											a        =     None
											a        =     model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.eval()
											a        =     model(**lowerCamelCase_	)

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

											a        =     copy.deepcopy(lowerCamelCase_	)
											a        =     None
											a        =     model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.eval()
											a        =     model(**lowerCamelCase_	)

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

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

						for backbone_class in self.all_model_classes:
											a        =     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	):
						"""simple docstring"""

						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(lowerCamelCase_	)
											backbone.to(lowerCamelCase_	)
											backbone.eval()

											a        =     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
											a        =     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:
																a        =     backbone(**lowerCamelCase_	, output_attentions=lowerCamelCase_	)
																self.assertIsNotNone(outputs.attentions	)





import warnings

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


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
