

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

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


__A      :   List[str]          =	logging.getLogger(__name__)






class       __A (							_SCREAMING_SNAKE_CASE       ):






    def __init__(		self	:			Union[str, Any]			, UpperCAmelCase_	:			str=-1			):
        # in NER datasets, the last column is usually reserved for NER label
        lowerCAmelCase :	Optional[int]				=					label_idx






    def 		lowercase__       (		self	:			Optional[int]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Union[Split, str]			):
        if isinstance(_lowerCAmelCase			, _lowerCAmelCase			):
            lowerCAmelCase :	Dict				=					mode.value
        lowerCAmelCase :	List[str]				=					os.path.join(_lowerCAmelCase			, f"{mode}.txt"			)
        lowerCAmelCase :	Optional[int]				=					1
        lowerCAmelCase :	Any				=					[]
        with open(_lowerCAmelCase			, encoding='utf-8'			) as f:
            lowerCAmelCase :	Tuple				=					[]
            lowerCAmelCase :	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=_lowerCAmelCase			, labels=_lowerCAmelCase			)			)
                        guid_index += 1
                        lowerCAmelCase :	List[Any]				=					[]
                        lowerCAmelCase :	Union[str, Any]				=					[]
                else:
                    lowerCAmelCase :	Optional[int]				=					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 		lowercase__       (		self	:			Optional[int]			, UpperCAmelCase_	:			TextIO			, UpperCAmelCase_	:			TextIO			, UpperCAmelCase_	:			List			):
        lowerCAmelCase :	Dict				=					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]:
                lowerCAmelCase :	str				=					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 		lowercase__       (		self	:			List[Any]			, UpperCAmelCase_	:			str			):
        if path:
            with open(_lowerCAmelCase			, 'r'			) as f:
                lowerCAmelCase :	Dict				=					f.read().splitlines()
            if "O" not in labels:
                lowerCAmelCase :	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       __A (							_SCREAMING_SNAKE_CASE       ):






    def __init__(		self	:			Union[str, Any]			):
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2			)






    def 		lowercase__       (		self	:			str			, UpperCAmelCase_	:			str			):
        if path:
            with open(_lowerCAmelCase			, 'r'			) as f:
                lowerCAmelCase :	Union[str, Any]				=					f.read().splitlines()
            if "O" not in labels:
                lowerCAmelCase :	str				=					['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 (							_SCREAMING_SNAKE_CASE       ):






    def 		lowercase__       (		self	:			List[str]			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			Union[Split, str]			):
        if isinstance(_lowerCAmelCase			, _lowerCAmelCase			):
            lowerCAmelCase :	Dict				=					mode.value
        lowerCAmelCase :	Dict				=					os.path.join(_lowerCAmelCase			, f"{mode}.txt"			)
        lowerCAmelCase :	Union[str, Any]				=					1
        lowerCAmelCase :	Union[str, Any]				=					[]

        with open(_lowerCAmelCase			, encoding='utf-8'			) as f:
            for sentence in parse_incr(_lowerCAmelCase			):
                lowerCAmelCase :	Union[str, Any]				=					[]
                lowerCAmelCase :	int				=					[]
                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 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			TextIO			, UpperCAmelCase_	:			TextIO			, UpperCAmelCase_	:			List			):
        lowerCAmelCase :	List[Any]				=					0
        for sentence in parse_incr(_lowerCAmelCase			):
            lowerCAmelCase :	Any				=					preds_list[example_id]
            lowerCAmelCase :	List[Any]				=					''
            for token in sentence:
                out += f"{token['form']} ({token['upos']}|{s_p.pop(0			)}) "
            out += "\n"
            writer.write(_lowerCAmelCase			)
            example_id += 1






    def 		lowercase__       (		self	:			Optional[Any]			, UpperCAmelCase_	:			str			):
        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",
            ]











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)