
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE    :      Dict          =			logging.get_logger(__name__)







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"upernet"





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=512				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=[1, 2, 3, 6]				,       UpperCamelCase__=True				,       UpperCamelCase__=0.4				,       UpperCamelCase__=384				,       UpperCamelCase__=256				,       UpperCamelCase__=1				,       UpperCamelCase__=False				,       UpperCamelCase__=255				,       **UpperCamelCase__				,       ):
					super().__init__(**UpperCamelCase__		)

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''		)
									A__  :	Optional[int]         =							CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		)
					elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Optional[int]         =							backbone_config.get('''model_type'''		)
									A__  :	Dict         =							CONFIG_MAPPING[backbone_model_type]
									A__  :	Optional[Any]         =							config_class.from_dict(UpperCamelCase__		)

					A__  :	Union[str, Any]         =							backbone_config
					A__  :	List[str]         =							hidden_size
					A__  :	Tuple         =							initializer_range
					A__  :	Optional[int]         =							pool_scales
					A__  :	Tuple         =							use_auxiliary_head
					A__  :	Optional[Any]         =							auxiliary_loss_weight
					A__  :	Optional[int]         =							auxiliary_in_channels
					A__  :	Union[str, Any]         =							auxiliary_channels
					A__  :	Tuple         =							auxiliary_num_convs
					A__  :	List[Any]         =							auxiliary_concat_input
					A__  :	Optional[int]         =							loss_ignore_index





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							copy.deepcopy(self.__dict__		)
					A__  :	Optional[Any]         =							self.backbone_config.to_dict()
					A__  :	Any         =							self.__class__.model_type
					return output

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Dict         =							inspect.getfile(accelerate.test_utils		)
					A__  :	Any         =							os.path.sep.join(
					    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

					from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

					A__  :	Tuple         =							test_metrics





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main				,       num_processes=1		)





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def   __snake_case				(			self		):
					self.test_metrics.main()





	@require_multi_gpu
	def   __snake_case				(			self		):
					print(F"Found {torch.cuda.device_count()} devices."		)
					A__  :	int         =							['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
					with patch_environment(omp_num_threads=1		):
									execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()		)