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

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


lowercase  :					Tuple						    =							logging.getLogger(__name__)

class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case=-1      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      label_idx
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     int       	=      mode.value
           lowercase							:     Any       	=      os.path.join(snake_case		,f"{mode}.txt"      )
           lowercase							:     int       	=      1
           lowercase							:     List[Any]       	=      []
           with open(snake_case		,encoding="""utf-8"""      ) as f:
                  lowercase							:     Dict       	=      []
                  lowercase							:     List[Any]       	=      []
                  for line in f:
                         if line.startswith("""-DOCSTART-"""      ) or line == "" or line == "\n":
                                if words:
                                       examples.append(InputExample(guid=f"{mode}-{guid_index}"		,words=snake_case		,labels=snake_case      )      )
                                       guid_index += 1
                                       lowercase							:     str       	=      []
                                       lowercase							:     Union[str, Any]       	=      []
                         else:
                                lowercase							:     str       	=      line.split(""" """      )
                                words.append(splits[0]      )
                                if len(snake_case      ) > 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=snake_case		,labels=snake_case      )      )
           return examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      0
           for line in test_input_reader:
                  if line.startswith("""-DOCSTART-"""      ) or line == "" or line == "\n":
                         writer.write(snake_case      )
                         if not preds_list[example_id]:
                                example_id += 1
                  elif preds_list[example_id]:
                         lowercase							:     Optional[int]       	=      line.split()[0] + """ """ + preds_list[example_id].pop(0      ) + """\n"""
                         writer.write(snake_case      )
                  else:
                         logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""		,line.split()[0]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if path:
                  with open(snake_case		,"""r"""      ) as f:
                         lowercase							:     str       	=      f.read().splitlines()
                  if "O" not in labels:
                         lowercase							:     List[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 __snake_case		(      lowerCAmelCase     ):
    def __init__(  self      ):



           '''simple docstring'''







           super().__init__(label_idx=-2      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if path:
                  with open(snake_case		,"""r"""      ) as f:
                         lowercase							:     List[str]       	=      f.read().splitlines()
                  if "O" not in labels:
                         lowercase							:     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 __snake_case		(      lowerCAmelCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     Optional[Any]       	=      mode.value
           lowercase							:     int       	=      os.path.join(snake_case		,f"{mode}.txt"      )
           lowercase							:     List[Any]       	=      1
           lowercase							:     Optional[Any]       	=      []

           with open(snake_case		,encoding="""utf-8"""      ) as f:
                  for sentence in parse_incr(snake_case      ):
                         lowercase							:     Optional[int]       	=      []
                         lowercase							:     str       	=      []
                         for token in sentence:
                                words.append(token["""form"""]      )
                                labels.append(token["""upos"""]      )
                         assert len(snake_case      ) == len(snake_case      )
                         if words:
                                examples.append(InputExample(guid=f"{mode}-{guid_index}"		,words=snake_case		,labels=snake_case      )      )
                                guid_index += 1
           return examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      0
           for sentence in parse_incr(snake_case      ):
                  lowercase							:     str       	=      preds_list[example_id]
                  lowercase							:     int       	=      """"""
                  for token in sentence:
                         out += f"{token['form']} ({token['upos']}|{s_p.pop(0      )}) "
                  out += "\n"
                  writer.write(snake_case      )
                  example_id += 1
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if path:
                  with open(snake_case		,"""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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :					str						    =							{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Tuple						    =							["""FunnelTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[str]						    =							[
      """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FunnelBaseModel""",
      """FunnelForMaskedLM""",
      """FunnelForMultipleChoice""",
      """FunnelForPreTraining""",
      """FunnelForQuestionAnswering""",
      """FunnelForSequenceClassification""",
      """FunnelForTokenClassification""",
      """FunnelModel""",
      """FunnelPreTrainedModel""",
      """load_tf_weights_in_funnel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Optional[Any]						    =							[
      """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFFunnelBaseModel""",
      """TFFunnelForMaskedLM""",
      """TFFunnelForMultipleChoice""",
      """TFFunnelForPreTraining""",
      """TFFunnelForQuestionAnswering""",
      """TFFunnelForSequenceClassification""",
      """TFFunnelForTokenClassification""",
      """TFFunnelModel""",
      """TFFunnelPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .tokenization_funnel import FunnelTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_funnel_fast import FunnelTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_funnel import (
        FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
        FunnelPreTrainedModel,
        load_tf_weights_in_funnel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_funnel import (
        TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFunnelBaseModel,
        TFFunnelForMaskedLM,
        TFFunnelForMultipleChoice,
        TFFunnelForPreTraining,
        TFFunnelForQuestionAnswering,
        TFFunnelForSequenceClassification,
        TFFunnelForTokenClassification,
        TFFunnelModel,
        TFFunnelPreTrainedModel,
    )

else:
  import sys

  lowercase  :					Any						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
