


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,
)
_snake_case             =     logging.getLogger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Optional[int]    =				{
      "repo_id": str(SCREAMING_SNAKE_CASE_	),
      "repo_sha": str(repo.head.object.hexsha	),
      "repo_branch": str(repo.active_branch	),
  }

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if params.n_gpu <= 0:
    lowerCamelCase :					str    =				0
    lowerCamelCase :					int    =				-1
    lowerCamelCase :					Union[str, Any]    =				True
    lowerCamelCase :					List[Any]    =				False
    return

  assert torch.cuda.is_available()

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

    lowerCamelCase :					Dict    =				int(os.environ["WORLD_SIZE"]	)
    lowerCamelCase :					Union[str, Any]    =				int(os.environ["N_GPU_NODE"]	)
    lowerCamelCase :					Any    =				int(os.environ["RANK"]	)

    # number of nodes / node ID
    lowerCamelCase :					Any    =				params.world_size // params.n_gpu_per_node
    lowerCamelCase :					Any    =				params.global_rank // params.n_gpu_per_node
    lowerCamelCase :					Optional[int]    =				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 :					Tuple    =				1
    lowerCamelCase :					Dict    =				0
    lowerCamelCase :					Dict    =				0
    lowerCamelCase :					Dict    =				0
    lowerCamelCase :					Dict    =				1
    lowerCamelCase :					Union[str, Any]    =				1
    lowerCamelCase :					List[Any]    =				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 :					int    =				params.node_id == 0 and params.local_rank == 0
  lowerCamelCase :					Dict    =				params.n_nodes > 1

  # summary
  lowerCamelCase :					Optional[Any]    =				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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''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 json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



