


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__      		= logging.getLogger(__name__)
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       _A      :       Dict				    = git.Repo(search_parent_directories=UpperCamelCase__						)
       _A      :       Optional[Any]				    = {
           "repo_id": str(UpperCamelCase__						),
           "repo_sha": str(repo.head.object.hexsha						),
           "repo_branch": str(repo.active_branch						),
       }

       with open(os.path.join(UpperCamelCase__				,							"git_log.json"						)				,							"w"						) as f:
              json.dump(UpperCamelCase__				,							UpperCamelCase__				,							indent=4						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       if params.n_gpu <= 0:
              _A      :       Union[str, Any]				    = 0
              _A      :       Dict				    = -1
              _A      :       Dict				    = True
              _A      :       str				    = False
              return

       assert torch.cuda.is_available()

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

              _A      :       Any				    = int(os.environ["WORLD_SIZE"]						)
              _A      :       List[Any]				    = int(os.environ["N_GPU_NODE"]						)
              _A      :       Any				    = int(os.environ["RANK"]						)

              # number of nodes / node ID
              _A      :       int				    = params.world_size // params.n_gpu_per_node
              _A      :       str				    = params.global_rank // params.n_gpu_per_node
              _A      :       Any				    = 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

              _A      :       Dict				    = 1
              _A      :       List[str]				    = 0
              _A      :       int				    = 0
              _A      :       List[Any]				    = 0
              _A      :       str				    = 1
              _A      :       Union[str, Any]				    = 1
              _A      :       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
       _A      :       str				    = params.node_id == 0 and params.local_rank == 0
       _A      :       Any				    = params.n_nodes > 1

       # summary
       _A      :       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 							_UpperCAmelCase				(UpperCamelCase__     :  Any						):
       np.random.seed(args.seed						)
       torch.manual_seed(args.seed						)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed						)








"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=-1							)					->       Tuple:
   # in NER datasets, the last column is usually reserved for NER label
   _a  :       Optional[int]             =	label_idx


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 1:
           labels.append(splits[self.label_idx].replace("""\n"""							,						""""""							)							)
         else:
           # Examples could have no label for mode = "test"
           labels.append("""O"""							)
     if words:
       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	0
   for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
       writer.write(UpperCAmelCase__							)
       if not preds_list[example_id]:
         example_id += 1
     elif preds_list[example_id]:
       _a  :       int             =	line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
       writer.write(UpperCAmelCase__							)
     else:
       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,						line.split()[0]							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""O"""] + labels
     return labels
   else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Union[str, Any]							)					->       List[str]:
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""O"""] + labels
     return labels
   else:
     return [
         "O",
         "B-ADVP",
         "B-INTJ",
         "B-LST",
         "B-PRT",
         "B-NP",
         "B-SBAR",
         "B-VP",
         "B-ADJP",
         "B-CONJP",
         "B-PP",
         "I-ADVP",
         "I-INTJ",
         "I-LST",
         "I-PRT",
         "I-NP",
         "I-SBAR",
         "I-VP",
         "I-ADJP",
         "I-CONJP",
         "I-PP",
     ]







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     for sentence in parse_incr(UpperCAmelCase__							):
       _a  :       List[Any]             =	[]
       _a  :       Any             =	[]
       for token in sentence:
         words.append(token["""form"""]							)
         labels.append(token["""upos"""]							)
       assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)
       if words:
         examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
         guid_index += 1
   return examples


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       return f.read().splitlines()
   else:
     return [
         "ADJ",
         "ADP",
         "ADV",
         "AUX",
         "CCONJ",
         "DET",
         "INTJ",
         "NOUN",
         "NUM",
         "PART",
         "PRON",
         "PROPN",
         "PUNCT",
         "SCONJ",
         "SYM",
         "VERB",
         "X",
     ]

