


'''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  __magic_name__							(		SCREAMING_SNAKE_CASE__    ):
       UpperCAmelCase										=ComputeEnvironment.AMAZON_SAGEMAKER
       UpperCAmelCase										=True
       UpperCAmelCase										='ml.p3.2xlarge'
       UpperCAmelCase										='accelerate_sagemaker_execution_role'
       UpperCAmelCase										='hf-sm'
       UpperCAmelCase										='us-east-1'
       UpperCAmelCase										=1
       UpperCAmelCase										='accelerate-sagemaker-1'
       UpperCAmelCase										='1.6'
       UpperCAmelCase										='4.4'
       UpperCAmelCase										='train.py'
       UpperCAmelCase										=[
           '--model_name_or_path',
           'bert',
           '--do_train',
           'False',
           '--epochs',
           '3',
           '--learning_rate',
           '5e-5',
           '--max_steps',
           '50.5',
       ]
       UpperCAmelCase										=[
           '--model_name_or_path',
           'bert',
           '--do_train',
           '--do_test',
           'False',
           '--do_predict',
           '--epochs',
           '3',
           '--learning_rate',
           '5e-5',
           '--max_steps',
           '50.5',
       ]






class  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          # If no defaults are changed, `to_kwargs` returns an empty dict.
          _UpperCAmelCase       :       int							   =_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
          assert isinstance(converted_args['model_name_or_path']  ,							_SCREAMING_SNAKE_CASE)
          assert isinstance(converted_args['do_train']  ,							_SCREAMING_SNAKE_CASE)
          assert isinstance(converted_args['epochs']  ,							_SCREAMING_SNAKE_CASE)
          assert isinstance(converted_args['learning_rate']  ,							_SCREAMING_SNAKE_CASE)
          assert isinstance(converted_args['max_steps']  ,							_SCREAMING_SNAKE_CASE)

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





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

