



import copy
import inspect

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

@require_torch
class 						lowerCamelCase_     :






						'''simple docstring'''
						a__ =	()
						a__ =	True







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					List[Any]:
									A				:       Optional[int]														=    self.config_class

									# test default config
									A				:       Optional[int]														=    config_class()
									self.assertIsNotNone(a_			)
									A				:       Optional[Any]														=    ['''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				:       Any														=    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				:       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
									A				:       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
									A				:       str														=    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				:       List[str]														=    config_class(out_features=["stage1", "stage2"]						,						out_indices=[0, 2]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					List[str]:
									A				:       int														=    self.model_tester.prepare_config_and_inputs_for_common()

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       Tuple														=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A				:       str														=    model_class(a_			)
												self.assertEqual(len(model.channels			)						,						len(config.out_features			)			)
												A				:       Dict														=    model.num_features
												A				:       Union[str, Any]														=    [config.stage_names.index(a_			) for feat in config.out_features]
												A				:       Dict														=    [num_features[idx] for idx in out_indices]
												self.assertListEqual(model.channels						,						a_			)

												A				:       Tuple														=    copy.deepcopy(a_			)
												A				:       Dict														=    None
												A				:       Union[str, Any]														=    model_class(a_			)
												self.assertEqual(len(model.channels			)						,						1			)
												self.assertListEqual(model.channels						,						[num_features[-1]]			)

												A				:       Dict														=    copy.deepcopy(a_			)
												A				:       str														=    None
												A				:       Tuple														=    model_class(a_			)
												self.assertEqual(len(model.channels			)						,						1			)
												self.assertListEqual(model.channels						,						[num_features[-1]]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					List[Any]:
									A				:       str														=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

												A				:       Tuple														=    copy.deepcopy(a_			)
												A				:       Dict														=    None
												A				:       List[Any]														=    model_class(a_			)
												model.to(a_			)
												model.eval()
												A				:       Tuple														=    model(**a_			)

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

												# Check backbone can be initialized with fresh weights
												A				:       int														=    copy.deepcopy(a_			)
												A				:       Tuple														=    False
												A				:       Optional[Any]														=    model_class(a_			)
												model.to(a_			)
												model.eval()
												A				:       int														=    model(**a_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    self.model_tester.prepare_config_and_inputs_for_common()

									for backbone_class in self.all_model_classes:
												A				:       Dict														=    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       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					List[Any]:
									A				:       Tuple														=    self.model_tester.prepare_config_and_inputs_for_common()
									A				:       Optional[Any]														=    inputs_dict['''pixel_values'''].shape[0]

									for backbone_class in self.all_model_classes:
												A				:       List[Any]														=    backbone_class(a_			)
												backbone.to(a_			)
												backbone.eval()

												A				:       Optional[int]														=    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				:       str														=    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				:       Optional[Any]														=    backbone(**a_						,						output_attentions=a_			)
															self.assertIsNotNone(outputs.attentions			)




import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





