



"""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 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					_a       =				()
					_a       =				True



					def 						snake_case			(			self				:    Dict	)->   int:
								lowerCamelCase__						:						int						      =self.config_class

								# test default config
								lowerCamelCase__						:						Union[str, Any]						      =config_class()
								self.assertIsNotNone(lowerCamelCase_	)
								lowerCamelCase__						:						Optional[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
								lowerCamelCase__						:						List[Any]						      =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
								lowerCamelCase__						:						List[Any]						      =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
								lowerCamelCase__						:						List[Any]						      =config_class(out_features=['''stage1''', '''stage3''']	)
								self.assertEqual(config.out_features,    ['''stage1''', '''stage3''']	)
								self.assertEqual(config.out_indices,    [1, 3]	)

								# Only out_indices set
								lowerCamelCase__						:						List[Any]						      =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_	):
											lowerCamelCase__						:						Tuple						      =config_class(out_features=['''stage1''', '''stage2'''],    out_indices=[0, 2]	)



					def 						snake_case			(			self				:    Tuple	)->   Tuple:
								lowerCamelCase__      ,  lowerCamelCase__						:						int						      =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__						:						Optional[Any]						      =model_class(lowerCamelCase_	)
											lowerCamelCase__						:						Dict						      =inspect.signature(model.forward	)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCamelCase__						:						Optional[int]						      =[*signature.parameters.keys()]
											lowerCamelCase__						:						Tuple						      =['''pixel_values''']
											self.assertListEqual(arg_names[:1],    lowerCamelCase_	)



					def 						snake_case			(			self				:    str	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__						:						str						      =model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.eval()
											lowerCamelCase__						:						Union[str, Any]						      =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
											lowerCamelCase__						:						Optional[Any]						      =copy.deepcopy(lowerCamelCase_	)
											lowerCamelCase__						:						List[Any]						      =None
											lowerCamelCase__						:						Union[str, Any]						      =model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.eval()
											lowerCamelCase__						:						Optional[int]						      =model(**lowerCamelCase_	)

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

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



					def 						snake_case			(			self				:    Any	)->   Dict:
								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =self.model_tester.prepare_config_and_inputs_for_common()

								for backbone_class in self.all_model_classes:
											lowerCamelCase__						:						Union[str, 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 						snake_case			(			self				:    Dict	)->   Tuple:
								lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__						:						List[str]						      =inputs_dict['''pixel_values'''].shape[0]

								for backbone_class in self.all_model_classes:
											lowerCamelCase__						:						Any						      =backbone_class(lowerCamelCase_	)
											backbone.to(lowerCamelCase_	)
											backbone.eval()

											lowerCamelCase__						:						Optional[int]						      =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
											lowerCamelCase__						:						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:
														lowerCamelCase__						:						Dict						      =backbone(**lowerCamelCase_,    output_attentions=lowerCamelCase_	)
														self.assertIsNotNone(outputs.attentions	)








"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     ArgumentParser(
						    description=(
						        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
						    )			)

						# Optional arguments for the launch helper
						parser.add_argument('''--num_cores'''						,      type=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      help=(
						        '''The full path to the single TPU training '''
						        '''program/script to be launched in parallel, '''
						        '''followed by all the arguments for the '''
						        '''training script'''
						    )						,      )

						# rest from the training program
						parser.add_argument('''training_script_args'''						,      nargs=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores			)]

						xmp.spawn(mod._mp_fn						,      args=()						,      nprocs=args.num_cores			)


if __name__ == "__main__":
						main()