


"""simple docstring"""







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

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


lowerCamelCase_       :       Tuple      						=  logging.getLogger(__name__)






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A=-1						) ->					Any:
     # in NER datasets, the last column is usually reserved for NER label
     a					=label_idx


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					List[InputExample]:
     if isinstance(__A			,   __A						):
         a					=mode.value
     a					=os.path.join(__A			,   f'''{mode}.txt'''						)
     a					=1
     a					=[]
     with open(__A			,   encoding='''utf-8'''						) as f:
         a					=[]
         a					=[]
         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
                     a					=[]
                     a					=[]
             else:
                 a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=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]:
             a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[str]:
     if path:
         with open(__A			,   '''r'''						) as f:
             a					=f.read().splitlines()
         if "O" not in labels:
             a					=['''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      ):






 """simple docstring"""




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





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[str]:
     if path:
         with open(__A			,   '''r'''						) as f:
             a					=f.read().splitlines()
         if "O" not in labels:
             a					=['''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      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					List[InputExample]:
     if isinstance(__A			,   __A						):
         a					=mode.value
     a					=os.path.join(__A			,   f'''{mode}.txt'''						)
     a					=1
     a					=[]

     with open(__A			,   encoding='''utf-8'''						) as f:
         for sentence in parse_incr(__A						):
             a					=[]
             a					=[]
             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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Any:
     a					=0
     for sentence in parse_incr(__A						):
         a					=preds_list[example_id]
         a					=''''''
         for token in sentence:
             out += f'''{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '''
         out += "\n"
         writer.write(__A						)
         example_id += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[str]:
     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",
         ]



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_       :       str      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

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






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         a					={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         a					={0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]						)