






'''simple docstring'''



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

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


__UpperCamelCase      :       List[str]      			=      logging.getLogger(__name__)
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase=-1      )							->int:



         '''simple docstring'''
         # in NER datasets, the last column is usually reserved for NER label
         __a				     =    label_idx




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[InputExample]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   lowerCamelCase      ):
            __a				     =    mode.value
         __a				     =    os.path.join(lowerCamelCase ,   F"""{mode}.txt"""      )
         __a				     =    1
         __a				     =    []
         with open(lowerCamelCase ,   encoding='utf-8'      ) as f:
            __a				     =    []
            __a				     =    []
            for line in f:
               if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
                  if words:
                     examples.append(InputExample(guid=F"""{mode}-{guid_index}""" ,   words=lowerCamelCase ,   labels=lowerCamelCase      )      )
                     guid_index += 1
                     __a				     =    []
                     __a				     =    []
               else:
                  __a				     =    line.split(' '      )
                  words.append(splits[0]      )
                  if len(lowerCamelCase      ) > 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=lowerCamelCase ,   labels=lowerCamelCase      )      )
         return examples




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         __a				     =    0
         for line in test_input_reader:
            if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
               writer.write(lowerCamelCase      )
               if not preds_list[example_id]:
                  example_id += 1
            elif preds_list[example_id]:
               __a				     =    line.split()[0] + ' ' + preds_list[example_id].pop(0      ) + '\n'
               writer.write(lowerCamelCase      )
            else:
               logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,   line.split()[0]      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         if path:
            with open(lowerCamelCase ,   'r'      ) as f:
               __a				     =    f.read().splitlines()
            if "O" not in labels:
               __a				     =    ['O'] + labels
            return labels
         else:
            return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self      )							->Union[str, Any]:



         '''simple docstring'''
         # in CONLL2003 dataset chunk column is second-to-last
         super().__init__(label_idx=-2      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         if path:
            with open(lowerCamelCase ,   'r'      ) as f:
               __a				     =    f.read().splitlines()
            if "O" not in labels:
               __a				     =    ['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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[InputExample]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   lowerCamelCase      ):
            __a				     =    mode.value
         __a				     =    os.path.join(lowerCamelCase ,   F"""{mode}.txt"""      )
         __a				     =    1
         __a				     =    []

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    0
         for sentence in parse_incr(lowerCamelCase      ):
            __a				     =    preds_list[example_id]
            __a				     =    ''
            for token in sentence:
               out += F"""{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) """
            out += "\n"
            writer.write(lowerCamelCase      )
            example_id += 1






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         if path:
            with open(lowerCamelCase ,   '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",
            ]







'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
 import torch

if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


__UpperCamelCase      :       Optional[Any]      			=      logging.get_logger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(  self ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a				     =    deprecated_arg[3:]
               setattr(self ,   lowerCamelCase ,   not kwargs.pop(lowerCamelCase      )      )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""      )

         __a				     =    kwargs.pop('torchscript' ,   self.torchscript      )
         __a				     =    kwargs.pop('torch_xla_tpu_print_metrics' ,   self.torch_xla_tpu_print_metrics      )
         __a				     =    kwargs.pop('fp16_opt_level' ,   self.fpaa_opt_level      )
         super().__init__(**lowerCamelCase      )

      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Trace the models using torchscript"}     )
      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Print Xla/PyTorch tpu metrics"}     )
      __a  					=field(
          default="O1"      ,      metadata={
              "help": (
                  "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                  "See details at https://nvidia.github.io/apex/amp.html"
              )
          }      ,      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         logger.info('PyTorch: setting up devices'      )
         if not self.cuda:
            __a				     =    torch.device('cpu'      )
            __a				     =    0
         elif is_torch_tpu_available():
            __a				     =    xm.xla_device()
            __a				     =    0
         else:
            __a				     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
            __a				     =    torch.cuda.device_count()
         return device, n_gpu




      @property
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def  __UpperCamelCase							(  self      )							->"torch.device":



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[0]




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[1]






      @property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return self.n_gpu > 0