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

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


snake_case__				= logging.getLogger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				A_=-1						) ->				Dict:






           """simple docstring"""
           # in NER datasets, the last column is usually reserved for NER label
           _lowerCamelCase			    =				label_idx



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[InputExample]:






           """simple docstring"""
           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				mode.value
           _lowerCamelCase			    =				os.path.join(A_				,				F'{mode}.txt'						)
           _lowerCamelCase			    =				1
           _lowerCamelCase			    =				[]
           with open(A_				,				encoding='''utf-8'''						) as f:
                 _lowerCamelCase			    =				[]
                 _lowerCamelCase			    =				[]
                 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
                                   _lowerCamelCase			    =				[]
                                   _lowerCamelCase			    =				[]
                       else:
                             _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				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]:
                       _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           if path:
                 with open(A_				,				'''r'''						) as f:
                       _lowerCamelCase			    =				f.read().splitlines()
                 if "O" not in labels:
                       _lowerCamelCase			    =				['''O'''] + labels
                 return labels
           else:
                 return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self						) ->				List[str]:






           """simple docstring"""
           # in CONLL2003 dataset chunk column is second-to-last
           super().__init__(label_idx=-2						)





     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           if path:
                 with open(A_				,				'''r'''						) as f:
                       _lowerCamelCase			    =				f.read().splitlines()
                 if "O" not in labels:
                       _lowerCamelCase			    =				['''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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[InputExample]:






           """simple docstring"""
           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				mode.value
           _lowerCamelCase			    =				os.path.join(A_				,				F'{mode}.txt'						)
           _lowerCamelCase			    =				1
           _lowerCamelCase			    =				[]

           with open(A_				,				encoding='''utf-8'''						) as f:
                 for sentence in parse_incr(A_						):
                       _lowerCamelCase			    =				[]
                       _lowerCamelCase			    =				[]
                       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 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				0
           for sentence in parse_incr(A_						):
                 _lowerCamelCase			    =				preds_list[example_id]
                 _lowerCamelCase			    =				''''''
                 for token in sentence:
                       out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '
                 out += "\n"
                 writer.write(A_						)
                 example_id += 1





     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """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",
                 ]
from typing import List

import numpy as np
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				{key: len(__UpperCAmelCase		) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    '''Sharding is ambiguous for this dataset: '''
                    + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                    + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()		)
                    + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                    + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                )		)
      _lowerCamelCase			    =				max(lists_lengths.values() ,						default=0		)
      return max(1 ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[range]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for group_idx in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowerCamelCase			    =				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowerCamelCase			    =				range(__UpperCAmelCase ,						start + num_shards_to_add		)
            shards_indices_per_group.append(__UpperCAmelCase		)
      return shards_indices_per_group
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[dict]:



      '''simple docstring'''

      _lowerCamelCase			    =				_number_of_shards_in_gen_kwargs(__UpperCAmelCase		)
      if num_shards == 1:
            return [dict(__UpperCAmelCase		)]
      else:
            _lowerCamelCase			    =				_distribute_shards(num_shards=__UpperCAmelCase ,						max_num_jobs=__UpperCAmelCase		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__UpperCAmelCase		)		)
            ]
def       __magic_name__(  __UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key] ,						__UpperCAmelCase		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      _lowerCamelCase			    =				{len(__UpperCAmelCase		) for value in gen_kwargs.values() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      _lowerCamelCase			    =				{}
      for size in list_sizes:
            _lowerCamelCase			    =				list(range(__UpperCAmelCase		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      _lowerCamelCase			    =				dict(__UpperCAmelCase		)
      for key, value in shuffled_kwargs.items():
            if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		):
                  _lowerCamelCase			    =				[value[i] for i in indices_per_size[len(__UpperCAmelCase		)]]
      return shuffled_kwargs