




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        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    inspect.getfile(accelerate.test_utils)
           A__       =    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__       =    test_metrics


    @require_cpu
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           debug_launcher(self.test_metrics.main					, num_processes=1)


    @require_cpu
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->List[Any]:





           '''simple docstring'''

           debug_launcher(self.test_metrics.main)


    @require_single_gpu
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           self.test_metrics.main()





    @require_multi_gpu
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Optional[Any]:





           '''simple docstring'''

           print(f"""Found {torch.cuda.device_count()} devices.""")
           A__       =    ['''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())









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

