





"""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_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase=-1  ):



     '''simple docstring'''




     __A    :						str						 =     label_idx

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     if isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         __A    :						Dict						 =     mode.value
     __A    :						Union[str, Any]						 =     os.path.join(__lowerCamelCase					,						F"""{mode}.txt"""  )
     __A    :						Optional[int]						 =     1
     __A    :						List[str]						 =     []
     with open(__lowerCamelCase					,						encoding='''utf-8'''  ) as f:
         __A    :						List[str]						 =     []
         __A    :						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
                     __A    :						int						 =     []
                     __A    :						List[Any]						 =     []
             else:
                 __A    :						Dict						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     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]:
             __A    :						int						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     if path:
         with open(__lowerCamelCase					,						'''r'''  ) as f:
             __A    :						Dict						 =     f.read().splitlines()
         if "O" not in labels:
             __A    :						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					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self  ):



     '''simple docstring'''




     super().__init__(label_idx=-2  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     if path:
         with open(__lowerCamelCase					,						'''r'''  ) as f:
             __A    :						str						 =     f.read().splitlines()
         if "O" not in labels:
             __A    :						int						 =     ['''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					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     if isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         __A    :						int						 =     mode.value
     __A    :						int						 =     os.path.join(__lowerCamelCase					,						F"""{mode}.txt"""  )
     __A    :						Dict						 =     1
     __A    :						int						 =     []

     with open(__lowerCamelCase					,						encoding='''utf-8'''  ) as f:
         for sentence in parse_incr(__lowerCamelCase  ):
             __A    :						List[str]						 =     []
             __A    :						Optional[Any]						 =     []
             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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     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",
         ]











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       a_ =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




