




import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
lowerCamelCase							:						Optional[Any]					       =  logging.getLogger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str	):


      '''simple docstring'''



      lowerCamelCase_							=       git.Repo(search_parent_directories=lowercase	)
      lowerCamelCase_							=       {
          'repo_id': str(lowercase	),
          'repo_sha': str(repo.head.object.hexsha	),
          'repo_branch': str(repo.active_branch	),
      }

      with open(os.path.join(lowercase      ,				'git_log.json'	)      ,				'w'	) as f:
            json.dump(lowercase      ,				lowercase      ,				indent=4	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any	):


      '''simple docstring'''



      if params.n_gpu <= 0:
            lowerCamelCase_							=       0
            lowerCamelCase_							=       -1
            lowerCamelCase_							=       True
            lowerCamelCase_							=       False
            return

      assert torch.cuda.is_available()

      logger.info('Initializing GPUs'	)
      if params.n_gpu > 1:
            assert params.local_rank != -1

            lowerCamelCase_							=       int(os.environ['WORLD_SIZE']	)
            lowerCamelCase_							=       int(os.environ['N_GPU_NODE']	)
            lowerCamelCase_							=       int(os.environ['RANK']	)

            # number of nodes / node ID
            lowerCamelCase_							=       params.world_size // params.n_gpu_per_node
            lowerCamelCase_							=       params.global_rank // params.n_gpu_per_node
            lowerCamelCase_							=       True

            assert params.n_nodes == int(os.environ['N_NODES']	)
            assert params.node_id == int(os.environ['NODE_RANK']	)

      # local job (single GPU)
      else:
            assert params.local_rank == -1

            lowerCamelCase_							=       1
            lowerCamelCase_							=       0
            lowerCamelCase_							=       0
            lowerCamelCase_							=       0
            lowerCamelCase_							=       1
            lowerCamelCase_							=       1
            lowerCamelCase_							=       False

      # sanity checks
      assert params.n_nodes >= 1
      assert 0 <= params.node_id < params.n_nodes
      assert 0 <= params.local_rank <= params.global_rank < params.world_size
      assert params.world_size == params.n_nodes * params.n_gpu_per_node

      # define whether this is the master process / if we are in multi-node distributed mode
      lowerCamelCase_							=       params.node_id == 0 and params.local_rank == 0
      lowerCamelCase_							=       params.n_nodes > 1

      # summary
      lowerCamelCase_							=       f"""--- Global rank: {params.global_rank} - """
      logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes	)
      logger.info(PREFIX + 'Node ID        : %i' % params.node_id	)
      logger.info(PREFIX + 'Local rank     : %i' % params.local_rank	)
      logger.info(PREFIX + 'World size     : %i' % params.world_size	)
      logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node	)
      logger.info(PREFIX + 'Master         : %s' % str(params.is_master	)	)
      logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node	)	)
      logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu	)	)
      logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()	)

      # set GPU device
      torch.cuda.set_device(params.local_rank	)

      # initialize multi-GPU
      if params.multi_gpu:
            logger.info('Initializing PyTorch distributed'	)
            torch.distributed.init_process_group(
                init_method='env://'      ,				backend='nccl'      ,				)






def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      np.random.seed(args.seed	)
      torch.manual_seed(args.seed	)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed	)






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)