



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase__			= """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__			= importlib.util.spec_from_file_location(
    """transformers""",
    os.path.join(PATH_TO_TRANSFORMERS, """__init__.py"""),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCamelCase__			= spec.loader.load_module()

lowerCamelCase__			= transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCamelCase__			= re.compile("""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


lowerCamelCase__			= {
    """CLIPConfigMixin""",
    """DecisionTransformerConfigMixin""",
    """EncoderDecoderConfigMixin""",
    """RagConfigMixin""",
    """SpeechEncoderDecoderConfigMixin""",
    """VisionEncoderDecoderConfigMixin""",
    """VisionTextDualEncoderConfigMixin""",
}

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







				__a       =		[]

				for config_class in list(CONFIG_MAPPING.values()    ):
								__a       =		False

								# source code of `config_class`
								__a       =		inspect.getsource(_SCREAMING_SNAKE_CASE    )
								__a       =		_re_checkpoint.findall(_SCREAMING_SNAKE_CASE    )

								for checkpoint in checkpoints:
												# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
												# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
												__a     ,					__a       =		checkpoint

												# verify the checkpoint name corresponds to the checkpoint link
												__a       =		f"https://huggingface.co/{ckpt_name}"
												if ckpt_link == ckpt_link_from_name:
																__a       =		True
																break

								__a       =		config_class.__name__
								if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(_SCREAMING_SNAKE_CASE    )

				if len(_SCREAMING_SNAKE_CASE    ) > 0:
								__a       =		"""\n""".join(sorted(_SCREAMING_SNAKE_CASE    )    )
								raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"    )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()





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	)
