




'''simple docstring'''





import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Optional[int]						       =    ComputeEnvironment.AMAZON_SAGEMAKER
			lowerCAmelCase_     :      List[str]						       =    True
			lowerCAmelCase_     :      str						       =    """ml.p3.2xlarge"""
			lowerCAmelCase_     :      Optional[Any]						       =    """accelerate_sagemaker_execution_role"""
			lowerCAmelCase_     :      Optional[int]						       =    """hf-sm"""
			lowerCAmelCase_     :      List[Any]						       =    """us-east-1"""
			lowerCAmelCase_     :      Tuple						       =    1
			lowerCAmelCase_     :      List[str]						       =    """accelerate-sagemaker-1"""
			lowerCAmelCase_     :      Any						       =    """1.6"""
			lowerCAmelCase_     :      Optional[Any]						       =    """4.4"""
			lowerCAmelCase_     :      Union[str, Any]						       =    """train.py"""
			lowerCAmelCase_     :      str						       =    [
			    """--model_name_or_path""",
			    """bert""",
			    """--do_train""",
			    """False""",
			    """--epochs""",
			    """3""",
			    """--learning_rate""",
			    """5e-5""",
			    """--max_steps""",
			    """50.5""",
			]
			lowerCAmelCase_     :      Tuple						       =    [
			    """--model_name_or_path""",
			    """bert""",
			    """--do_train""",
			    """--do_test""",
			    """False""",
			    """--do_predict""",
			    """--epochs""",
			    """3""",
			    """--learning_rate""",
			    """5e-5""",
			    """--max_steps""",
			    """50.5""",
			]




class  _snake_case		(	unittest.TestCase							):




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
										assert isinstance(converted_args["model_name_or_path"]				,							a__							)
										assert isinstance(converted_args["do_train"]				,							a__							)
										assert isinstance(converted_args["epochs"]				,							a__							)
										assert isinstance(converted_args["learning_rate"]				,							a__							)
										assert isinstance(converted_args["max_steps"]				,							a__							)

										with pytest.raises(a__							):
																	_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)








'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCamelCase							:   str  						=	{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Optional[int]  						=	[
     '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

 lowerCamelCase							:   Tuple  						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




