


'''simple docstring'''

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

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


a_  :				Optional[Any]						= logging.getLogger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase=-1     ):







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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if isinstance(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  mode.value
   lowerCamelCase_      =  os.path.join(UpperCamelCase			,  f'''{mode}.txt'''     )
   lowerCamelCase_      =  1
   lowerCamelCase_      =  []
   with open(UpperCamelCase			,  encoding="utf-8"     ) as f:
    lowerCamelCase_      =  []
    lowerCamelCase_      =  []
    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
       lowerCamelCase_      =  []
       lowerCamelCase_      =  []
     else:
      lowerCamelCase_      =  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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  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]:
     lowerCamelCase_      =  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   snake_case		(    self			,  UpperCamelCase     ):







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





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self     ):







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







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if path:
    with open(UpperCamelCase			,  "r"     ) as f:
     lowerCamelCase_      =  f.read().splitlines()
    if "O" not in labels:
     lowerCamelCase_      =  ["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      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if isinstance(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  mode.value
   lowerCamelCase_      =  os.path.join(UpperCamelCase			,  f'''{mode}.txt'''     )
   lowerCamelCase_      =  1
   lowerCamelCase_      =  []

   with open(UpperCamelCase			,  encoding="utf-8"     ) as f:
    for sentence in parse_incr(UpperCamelCase     ):
     lowerCamelCase_      =  []
     lowerCamelCase_      =  []
     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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







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







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   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",
    ]




from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)