


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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ComputeEnvironment.AMAZON_SAGEMAKER
     __SCREAMING_SNAKE_CASE  			= True
     __SCREAMING_SNAKE_CASE  			= "ml.p3.2xlarge"
     __SCREAMING_SNAKE_CASE  			= "accelerate_sagemaker_execution_role"
     __SCREAMING_SNAKE_CASE  			= "hf-sm"
     __SCREAMING_SNAKE_CASE  			= "us-east-1"
     __SCREAMING_SNAKE_CASE  			= 1
     __SCREAMING_SNAKE_CASE  			= "accelerate-sagemaker-1"
     __SCREAMING_SNAKE_CASE  			= "1.6"
     __SCREAMING_SNAKE_CASE  			= "4.4"
     __SCREAMING_SNAKE_CASE  			= "train.py"
     __SCREAMING_SNAKE_CASE  			= [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __SCREAMING_SNAKE_CASE  			= [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				List[str]:
            # If no defaults are changed, `to_kwargs` returns an empty dict.
            _A      :       Any				    = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
            assert isinstance(converted_args["model_name_or_path"] ,						__lowerCamelCase)
            assert isinstance(converted_args["do_train"] ,						__lowerCamelCase)
            assert isinstance(converted_args["epochs"] ,						__lowerCamelCase)
            assert isinstance(converted_args["learning_rate"] ,						__lowerCamelCase)
            assert isinstance(converted_args["max_steps"] ,						__lowerCamelCase)

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




from typing import TYPE_CHECKING

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


lowerCAmelCase__      		= {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechEncoderDecoderModel']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
