


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_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__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 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args['''model_name_or_path'''] ,       snake_case_						)
								assert isinstance(converted_args['''do_train'''] ,       snake_case_						)
								assert isinstance(converted_args['''epochs'''] ,       snake_case_						)
								assert isinstance(converted_args['''learning_rate'''] ,       snake_case_						)
								assert isinstance(converted_args['''max_steps'''] ,       snake_case_						)

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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


