

'''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 						__magic_name__   (							unittest.TestCase ):







 def 						_A(       self   ):
   lowercase									=inspect.getfile(accelerate.test_utils   )
   lowercase									=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

   lowercase									=test_metrics







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







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







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







 @require_multi_gpu
 def 						_A(       self   ):
   print(f'Found {torch.cuda.device_count()} devices.'   )
   lowercase									=['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
   with patch_environment(omp_num_threads=1   ):
     execute_subprocess_async(snake_case_  , env=os.environ.copy()   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "RagModel",
         "RagPreTrainedModel",
         "RagSequenceForGeneration",
         "RagTokenForGeneration",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "TFRagModel",
         "TFRagPreTrainedModel",
         "TFRagSequenceForGeneration",
         "TFRagTokenForGeneration",
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


