

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  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Tuple						 =  ComputeEnvironment.AMAZON_SAGEMAKER
  __lowerCAmelCase     :	Optional[Any]						 =  True
  __lowerCAmelCase     :	Union[str, Any]						 =  'ml.p3.2xlarge'
  __lowerCAmelCase     :	Any						 =  'accelerate_sagemaker_execution_role'
  __lowerCAmelCase     :	Union[str, Any]						 =  'hf-sm'
  __lowerCAmelCase     :	List[str]						 =  'us-east-1'
  __lowerCAmelCase     :	List[str]						 =  1
  __lowerCAmelCase     :	int						 =  'accelerate-sagemaker-1'
  __lowerCAmelCase     :	Union[str, Any]						 =  '1.6'
  __lowerCAmelCase     :	int						 =  '4.4'
  __lowerCAmelCase     :	Tuple						 =  'train.py'
  __lowerCAmelCase     :	Tuple						 =  [
      '--model_name_or_path',
      'bert',
      '--do_train',
      'False',
      '--epochs',
      '3',
      '--learning_rate',
      '5e-5',
      '--max_steps',
      '50.5',
  ]
  __lowerCAmelCase     :	Dict						 =  [
      '--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):
      # If no defaults are changed, `to_kwargs` returns an empty dict.
      SCREAMING_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)








from typing import TYPE_CHECKING

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


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


