



from datetime import datetime

import matplotlib.pyplot as plt
import torch

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict    ):
    """simple docstring"""







    for param in module.parameters():
        __a       =		False

def   lowerCAmelCase__						(							):
    """simple docstring"""







    __a       =		"""cuda""" if torch.cuda.is_available() else """cpu"""
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        __a       =		"""mps"""
    if device == "mps":
        print(
            """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
            """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
            """ with generations."""    )
    return device

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
    """simple docstring"""







    __a       =		plt.imshow(_SCREAMING_SNAKE_CASE    )
    fig.axes.get_xaxis().set_visible(_SCREAMING_SNAKE_CASE    )
    fig.axes.get_yaxis().set_visible(_SCREAMING_SNAKE_CASE    )
    plt.show()

def   lowerCAmelCase__						(							):
    """simple docstring"""







    __a       =		datetime.now()
    __a       =		current_time.strftime("""%H:%M:%S"""    )
    return timestamp





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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[Any]    =ComputeEnvironment.AMAZON_SAGEMAKER
							__lowerCamelCase	:	Optional[int]    =True
							__lowerCamelCase	:	List[Any]    ='ml.p3.2xlarge'
							__lowerCamelCase	:	Optional[int]    ='accelerate_sagemaker_execution_role'
							__lowerCamelCase	:	Optional[Any]    ='hf-sm'
							__lowerCamelCase	:	Any    ='us-east-1'
							__lowerCamelCase	:	Any    =1
							__lowerCamelCase	:	List[str]    ='accelerate-sagemaker-1'
							__lowerCamelCase	:	Union[str, Any]    ='1.6'
							__lowerCamelCase	:	str    ='4.4'
							__lowerCamelCase	:	List[str]    ='train.py'
							__lowerCamelCase	:	List[Any]    =[
							    '--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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

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

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