






"""simple docstring"""
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	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   inspect.getfile(accelerate.test_utils		)
				UpperCAmelCase_	:					int     	=   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

				UpperCAmelCase_	:					List[Any]     	=   test_metrics





	@require_cpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Union[str, Any]:
				debug_launcher(self.test_metrics.main      ,      num_processes=1		)





	@require_cpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[Any]:
				self.test_metrics.main()





	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[int]:
				print(f"""Found {torch.cuda.device_count()} devices."""		)
				UpperCAmelCase_	:					List[str]     	=   ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
				with patch_environment(omp_num_threads=1		):
							execute_subprocess_async(lowerCAmelCase_      ,      env=os.environ.copy()		)









from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
