





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



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







          '''simple docstring'''

          a__						:     Optional[Any]			=    inspect.getfile(accelerate.test_utils)
          a__						:     List[str]			=    os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_script.py'])
          a__						:     List[str]			=    os.path.sep.join(
              mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_distributed_data_loop.py'])
          a__						:     Any			=    os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_ops.py'])

   @require_multi_gpu
   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          print(F'Found {torch.cuda.device_count()} devices.')
          a__						:     Optional[int]			=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
          with patch_environment(omp_num_threads=1):
                 execute_subprocess_async(lowercase ,  env=os.environ.copy())

   @require_multi_gpu
   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          print(F'Found {torch.cuda.device_count()} devices.')
          a__						:     List[Any]			=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
          print(F'Command: {cmd}')
          with patch_environment(omp_num_threads=1):
                 execute_subprocess_async(lowercase ,  env=os.environ.copy())

   @require_multi_gpu
   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     int			=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
          with patch_environment(omp_num_threads=1):
                 execute_subprocess_async(lowercase ,  env=os.environ.copy())

   @require_multi_gpu
   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          print(F'Found {torch.cuda.device_count()} devices, using 2 devices only')
          a__						:     List[str]			=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
          with patch_environment(omp_num_threads=1 ,  cuda_visible_devices='0,1'):
                 execute_subprocess_async(lowercase ,  env=os.environ.copy())


if __name__ == "__main__":
    lowercase				:	List[str]      						=  Accelerator()
    lowercase				:	Tuple      						=  (accelerator.state.process_index + 2, 1_0)
    lowercase				:	Tuple      						=  torch.randint(0, 1_0, shape).to(accelerator.device)

    lowercase				:	List[Any]      						=  """"""

    lowercase				:	Tuple      						=  accelerator.pad_across_processes(tensor)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
        error_msg += "Padding was not done with the right value (0)."

    lowercase				:	str      						=  accelerator.pad_across_processes(tensor, pad_first=True)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    lowercase				:	Any      						=  accelerator.state.num_processes - accelerator.state.process_index - 1
    if not torch.equal(tensora[index:], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[:index] == 0):
        error_msg += "Padding was not done with the right value (0)."

    # Raise error at the end to make sure we don't stop at the first failure.
    if len(error_msg) > 0:
        raise ValueError(error_msg)









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 A_     (		A__		,				A__		,				A__    )  ->		Any:
       # Construct model
       if gpta_config_file == "":
              a__						:     Optional[int]			=    GPTaConfig()
       else:
              a__						:     List[str]			=    GPTaConfig.from_json_file(A__    )
       a__						:     List[str]			=    GPTaModel(A__    )

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

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


if __name__ == "__main__":
    lowercase				:	Dict      						=  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."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


