


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)



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



