





'''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 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     ()
		__lowerCAmelCase								=     True







		def 							A  (self    :   str   ):
			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 							A  (self    :   Optional[int]   ):
			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 							A  (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 							A  (self    :   Dict   ):
			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 							A  (self    :   Tuple   ):
			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 							A  (self    :   Optional[Any]   ):
			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   )





"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)