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

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


UpperCAmelCase_								=							logging.getLogger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__=-1       ):
            # in NER datasets, the last column is usually reserved for NER label
            _A=	label_idx



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	mode.value
            _A=	os.path.join(lowerCAmelCase__				,      f"{mode}.txt"       )
            _A=	1
            _A=	[]
            with open(lowerCAmelCase__				,      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=lowerCAmelCase__				,      labels=lowerCAmelCase__       )       )
                                        guid_index += 1
                                        _A=	[]
                                        _A=	[]
                          else:
                                 _A=	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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	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=	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 			a__  (					self				,      lowerCAmelCase__       ):
            if path:
                   with open(lowerCAmelCase__				,      '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      lowerCAmelCase						(					_a			):



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





     def 			a__  (					self				,      lowerCAmelCase__       ):
            if path:
                   with open(lowerCAmelCase__				,      '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      lowerCAmelCase						(					_a			):



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	mode.value
            _A=	os.path.join(lowerCAmelCase__				,      f"{mode}.txt"       )
            _A=	1
            _A=	[]

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





     def 			a__  (					self				,      lowerCAmelCase__       ):
            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 collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		="""convbert"""



     def __init__(					self				,      lowerCAmelCase__=30522				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=1E-12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=768				,      lowerCAmelCase__=2				,      lowerCAmelCase__=9				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size
            _A=	hidden_act
            _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=	embedding_size
            _A=	head_ratio
            _A=	conv_kernel_size
            _A=	num_groups
            _A=	classifier_dropout





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					self       ):
            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),
                    ('token_type_ids', dynamic_axis),
                ]       )