
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 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase=-1  )       ->   List[str]:
							# in NER datasets, the last column is usually reserved for NER label
							lowerCAmelCase_      			=			label_idx




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[InputExample]:
							if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
										lowerCAmelCase_      			=			mode.value
							lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       f"""{mode}.txt"""  )
							lowerCAmelCase_      			=			1
							lowerCAmelCase_      			=			[]
							with open(_UpperCamelCase       ,       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=_UpperCamelCase       ,       labels=_UpperCamelCase  )  )
																			guid_index += 1
																			lowerCAmelCase_      			=			[]
																			lowerCAmelCase_      			=			[]
													else:
																lowerCAmelCase_      			=			line.split(" "  )
																words.append(splits[0]  )
																if len(_UpperCamelCase  ) > 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=_UpperCamelCase       ,       labels=_UpperCamelCase  )  )
							return examples




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			0
							for line in test_input_reader:
										if line.startswith("-DOCSTART-"  ) or line == "" or line == "\n":
													writer.write(_UpperCamelCase  )
													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(_UpperCamelCase  )
										else:
													logger.warning("Maximum sequence length exceeded: No prediction for '%s'."       ,       line.split()[0]  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							if path:
										with open(_UpperCamelCase       ,       "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 			_lowerCAmelCase      (			__a ):




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							if path:
										with open(_UpperCamelCase       ,       "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 			_lowerCAmelCase      (			__a ):




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[InputExample]:
							if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
										lowerCAmelCase_      			=			mode.value
							lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       f"""{mode}.txt"""  )
							lowerCAmelCase_      			=			1
							lowerCAmelCase_      			=			[]

							with open(_UpperCamelCase       ,       encoding="utf-8"  ) as f:
										for sentence in parse_incr(_UpperCamelCase  ):
													lowerCAmelCase_      			=			[]
													lowerCAmelCase_      			=			[]
													for token in sentence:
																words.append(token["form"]  )
																labels.append(token["upos"]  )
													assert len(_UpperCamelCase  ) == len(_UpperCamelCase  )
													if words:
																examples.append(InputExample(guid=f"""{mode}-{guid_index}"""       ,       words=_UpperCamelCase       ,       labels=_UpperCamelCase  )  )
																guid_index += 1
							return examples




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			0
							for sentence in parse_incr(_UpperCamelCase  ):
										lowerCAmelCase_      			=			preds_list[example_id]
										lowerCAmelCase_      			=			""
										for token in sentence:
													out += f"""{token['form']} ({token['upos']}|{s_p.pop(0  )}) """
										out += "\n"
										writer.write(_UpperCamelCase  )
										example_id += 1




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							if path:
										with open(_UpperCamelCase       ,       "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",
										]





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A     =  "▁"

_A     =  {"vocab_file": "spiece.model"}

_A     =  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A     =  {
    "google/pegasus-xsum": 5_12,
}


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES

				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=['''input_ids''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<mask_2>"       ,       _UpperCamelCase="<mask_1>"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=103       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   None:
							lowerCAmelCase_      			=			offset
							if additional_special_tokens is not None:
										if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													raise TypeError(
													    f"""additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is"""
													    f""" {type(_UpperCamelCase  )}"""  )

										lowerCAmelCase_      			=			(
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f"""<unk_{i}>""" for i in range(len(_UpperCamelCase  )       ,       self.offset - 1  )
										]

										if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
													raise ValueError(
													    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
													    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
										lowerCAmelCase_      			=			additional_special_tokens_extended
							else:
										lowerCAmelCase_      			=			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,       self.offset  )]

							lowerCAmelCase_      			=			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_UpperCamelCase       ,       unk_token=_UpperCamelCase       ,       mask_token=_UpperCamelCase       ,       pad_token=_UpperCamelCase       ,       mask_token_sent=_UpperCamelCase       ,       offset=_UpperCamelCase       ,       additional_special_tokens=_UpperCamelCase       ,       sp_model_kwargs=self.sp_model_kwargs       ,       **_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			mask_token_sent
							lowerCAmelCase_      			=			vocab_file
							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(_UpperCamelCase  )

							# add special tokens to encoder dict
							lowerCAmelCase_      			=			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }  )

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,       self.offset - 1  )}  )

							lowerCAmelCase_      			=			{v: k for k, v in self.encoder.items()}




				@property
				def   __a		(      self  )       ->   int:
							return len(self.sp_model  ) + self.offset




				def   __a		(      self  )       ->   Dict[str, int]:
							lowerCAmelCase_      			=			{self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def __getstate__(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.__dict__.copy()
							lowerCAmelCase_      			=			None
							return state




				def __setstate__(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			d

							# for backward compatibility
							if not hasattr(self       ,       "sp_model_kwargs"  ):
										lowerCAmelCase_      			=			{}

							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							return self.sp_model.encode(_UpperCamelCase       ,       out_type=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCAmelCase_      			=			self.sp_model.piece_to_id(_UpperCamelCase  )
							return sp_id + self.offset




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCAmelCase_      			=			self.sp_model.IdToPiece(index - self.offset  )
							return token




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_UpperCamelCase  ) + token
													lowerCAmelCase_      			=			[]
										else:
													current_sub_tokens.append(_UpperCamelCase  )
							out_string += self.sp_model.decode(_UpperCamelCase  )
							return out_string.strip()




				def   __a		(      self       ,       _UpperCamelCase=False  )       ->   Optional[int]:
							return 1




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			set(self.all_special_ids  )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False  )       ->   List[int]:
							if already_has_special_tokens:
										return self._special_token_mask(_UpperCamelCase  )
							elif token_ids_a is None:
										return self._special_token_mask(_UpperCamelCase  ) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   List[int]:
							if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple[str]:
							if not os.path.isdir(_UpperCamelCase  ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
							lowerCAmelCase_      			=			os.path.join(
							    _UpperCamelCase       ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file       ,       _UpperCamelCase  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(_UpperCamelCase       ,       "wb"  ) as fi:
													lowerCAmelCase_      			=			self.sp_model.serialized_model_proto()
													fi.write(_UpperCamelCase  )

							return (out_vocab_file,)



