






"""simple docstring"""



import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)











"""simple docstring"""



import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a  :						Dict					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Any:
 UpperCAmelCase__ 						=				ArgumentParser()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				parser.parse_known_args()
 for arg in unknown:
  if arg.startswith(("""-""", """--""")			):
   parser.add_argument(arg.split("""="""			)[0]			)

 return parser.parse_args()




def       snake_case__		(   )    ->List[Any]:
 UpperCAmelCase__ 						=				parse_args()
 UpperCAmelCase__ 						=				8_8_8_8
 UpperCAmelCase__ 						=				int(os.environ["""SM_NUM_GPUS"""]			)
 UpperCAmelCase__ 						=				json.loads(os.environ["""SM_HOSTS"""]			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				os.environ["""SM_CURRENT_HOST"""]
 UpperCAmelCase__ 						=				hosts.index(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				"""INFO"""

 if num_nodes > 1:
  UpperCAmelCase__ 						=				F'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]			)}'''
 else:
  UpperCAmelCase__ 						=				F'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]			)}'''
 try:
  subprocess.run(_SCREAMING_SNAKE_CASE       ,	shell=_SCREAMING_SNAKE_CASE			)
 except Exception as e:
  logger.info(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 main()



