






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

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


snake_case__   :						Any													=   logging.getLogger(__name__)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase=-1			)      ->			str:
     # in NER datasets, the last column is usually reserved for NER label
     UpperCamelCase_						=  label_idx




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[InputExample]:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  mode.value
     UpperCamelCase_						=  os.path.join(_UpperCAmelCase		,		f"""{mode}.txt"""			)
     UpperCamelCase_						=  1
     UpperCamelCase_						=  []
     with open(_UpperCAmelCase		,		encoding='utf-8'			) as f:
        UpperCamelCase_						=  []
        UpperCamelCase_						=  []
        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
                 UpperCamelCase_						=  []
                 UpperCamelCase_						=  []
           else:
              UpperCamelCase_						=  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  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]:
           UpperCamelCase_						=  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     if path:
        with open(_UpperCAmelCase		,		'r'			) as f:
           UpperCamelCase_						=  f.read().splitlines()
        if "O" not in labels:
           UpperCamelCase_						=  ['O'] + labels
        return labels
     else:
        return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self			)      ->			Optional[int]:
     # in CONLL2003 dataset chunk column is second-to-last
     super().__init__(label_idx=-2			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     if path:
        with open(_UpperCAmelCase		,		'r'			) as f:
           UpperCamelCase_						=  f.read().splitlines()
        if "O" not in labels:
           UpperCamelCase_						=  ['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     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[InputExample]:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  mode.value
     UpperCamelCase_						=  os.path.join(_UpperCAmelCase		,		f"""{mode}.txt"""			)
     UpperCamelCase_						=  1
     UpperCamelCase_						=  []

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as f:
        for sentence in parse_incr(_UpperCAmelCase			):
           UpperCamelCase_						=  []
           UpperCamelCase_						=  []
           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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  0
     for sentence in parse_incr(_UpperCAmelCase			):
        UpperCamelCase_						=  preds_list[example_id]
        UpperCamelCase_						=  ''
        for token in sentence:
           out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0			)}) """
        out += "\n"
        writer.write(_UpperCAmelCase			)
        example_id += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			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",
        ]









'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



