



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

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


__lowerCamelCase         =			logging.getLogger(__name__)


class   snake_case_ (a__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase=-1):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		label_idx



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             if isinstance(_A     ,_A):
                   UpperCAmelCase_  :	Any				      =		mode.value
             UpperCAmelCase_  :	Optional[Any]				      =		os.path.join(_A     ,F"""{mode}.txt""")
             UpperCAmelCase_  :	Optional[Any]				      =		1
             UpperCAmelCase_  :	Tuple				      =		[]
             with open(_A     ,encoding="utf-8") as f:
                   UpperCAmelCase_  :	Any				      =		[]
                   UpperCAmelCase_  :	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=_A     ,labels=_A))
                                     guid_index += 1
                                     UpperCAmelCase_  :	str				      =		[]
                                     UpperCAmelCase_  :	int				      =		[]
                         else:
                               UpperCAmelCase_  :	int				      =		line.split(" ")
                               words.append(splits[0])
                               if len(_A) > 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=_A     ,labels=_A))
             return examples



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             if path:
                   with open(_A     ,"r") as f:
                         UpperCAmelCase_  :	Any				      =		f.read().splitlines()
                   if "O" not in labels:
                         UpperCAmelCase_  :	Optional[int]				      =		['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_ (a__  ):




       """simple docstring"""






       def __init__(     self):

             """simple docstring"""





             super().__init__(label_idx=-2)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             if path:
                   with open(_A     ,"r") as f:
                         UpperCAmelCase_  :	Union[str, Any]				      =		f.read().splitlines()
                   if "O" not in labels:
                         UpperCAmelCase_  :	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_ (a__  ):




       """simple docstring"""






       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             if isinstance(_A     ,_A):
                   UpperCAmelCase_  :	Dict				      =		mode.value
             UpperCAmelCase_  :	Dict				      =		os.path.join(_A     ,F"""{mode}.txt""")
             UpperCAmelCase_  :	List[str]				      =		1
             UpperCAmelCase_  :	str				      =		[]

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



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		0
             for sentence in parse_incr(_A):
                   UpperCAmelCase_  :	str				      =		preds_list[example_id]
                   UpperCAmelCase_  :	Tuple				      =		''
                   for token in sentence:
                         out += F"""{token['form']} ({token['upos']}|{s_p.pop(0)}) """
                   out += "\n"
                   writer.write(_A)
                   example_id += 1



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             if path:
                   with open(_A     ,"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",
                   ]





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch


class   snake_case_ (TensorFormatter[Mapping, """torch.Tensor""", Mapping]  ):




       """simple docstring"""






       def __init__(     self     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             super().__init__(features=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		torch_tensor_kwargs
             import torch  # noqa import torch at initialization



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             if isinstance(lowercase     ,lowercase) and column:
                   if all(
                       isinstance(lowercase     ,torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
                       for x in column):
                         return torch.stack(lowercase)
             return column



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             if isinstance(lowercase     ,(str, bytes, type(lowercase))):
                   return value
             elif isinstance(lowercase     ,(np.character, np.ndarray)) and np.issubdtype(value.dtype     ,np.character):
                   return value.tolist()

             UpperCAmelCase_  :	List[str]				      =		{}

             if isinstance(lowercase     ,(np.number, np.ndarray)) and np.issubdtype(value.dtype     ,np.integer):
                   UpperCAmelCase_  :	Dict				      =		{"dtype": torch.intaa}
             elif isinstance(lowercase     ,(np.number, np.ndarray)) and np.issubdtype(value.dtype     ,np.floating):
                   UpperCAmelCase_  :	Optional[Any]				      =		{"dtype": torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                   import PIL.Image

                   if isinstance(lowercase     ,PIL.Image.Image):
                         UpperCAmelCase_  :	List[str]				      =		np.asarray(lowercase)
             return torch.tensor(lowercase     ,**{**default_dtype, **self.torch_tensor_kwargs})



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             # support for torch, tf, jax etc.
             if hasattr(lowercase     ,"__array__") and not isinstance(lowercase     ,torch.Tensor):
                   UpperCAmelCase_  :	int				      =		data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(lowercase     ,np.ndarray):
                   if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
             elif isinstance(lowercase     ,(list, tuple)):
                   return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
             return self._tensorize(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return map_nested(self._recursive_tensorize     ,lowercase     ,map_list=lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.numpy_arrow_extractor().extract_row(lowercase)
             UpperCAmelCase_  :	Tuple				      =		self.python_features_decoder.decode_row(lowercase)
             return self.recursive_tensorize(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.numpy_arrow_extractor().extract_column(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.python_features_decoder.decode_column(lowercase     ,pa_table.column_names[0])
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             UpperCAmelCase_  :	List[str]				      =		self._consolidate(lowercase)
             return column



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.numpy_arrow_extractor().extract_batch(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		self.python_features_decoder.decode_batch(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             for column_name in batch:
                   UpperCAmelCase_  :	Optional[int]				      =		self._consolidate(batch[column_name])
             return batch
