






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		"""▁"""

snake_case    				=		{"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case    				=		{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

snake_case    				=		{
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
snake_case    				=		["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]


class  SCREAMING_SNAKE_CASE			(			__a					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	VOCAB_FILES_NAMES
     UpperCamelCase_							:		Dict			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:		List[Any]			=	PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:		List[str]			=	['''input_ids''', '''attention_mask''']

     UpperCamelCase_							:		Union[str, Any]			=	[]
     UpperCamelCase_							:		str			=	[]


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	int="<s>"					, UpperCAmelCase_       :	str="</s>"					, UpperCAmelCase_       :	Tuple="</s>"					, UpperCAmelCase_       :	Union[str, Any]="<s>"					, UpperCAmelCase_       :	Union[str, Any]="<unk>"					, UpperCAmelCase_       :	Optional[Any]="<pad>"					, UpperCAmelCase_       :	List[Any]="<mask>"					, UpperCAmelCase_       :	Optional[int]=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	Optional[Any]=None					, UpperCAmelCase_       :	Optional[Dict[str, Any]] = None					, UpperCAmelCase_       :	Union[str, Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    AddedToken(a_					, lstrip=a_					, rstrip=a_      ) if isinstance(a_					, a_      ) else mask_token

       SCREAMING_SNAKE_CASE       :      Tuple							=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a_					, eos_token=a_					, unk_token=a_					, sep_token=a_					, cls_token=a_					, pad_token=a_					, mask_token=a_					, tokenizer_file=a_					, src_lang=a_					, tgt_lang=a_					, additional_special_tokens=a_					, sp_model_kwargs=self.sp_model_kwargs					, **a_					, )

       SCREAMING_SNAKE_CASE       :      str							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(str(a_      )      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       SCREAMING_SNAKE_CASE       :      List[Any]							=    {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       SCREAMING_SNAKE_CASE       :      List[Any]							=    1

       SCREAMING_SNAKE_CASE       :      List[str]							=    len(self.sp_model      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_      )
       }
       SCREAMING_SNAKE_CASE       :      Any							=    {v: k for k, v in self.lang_code_to_id.items()}
       SCREAMING_SNAKE_CASE       :      Any							=    len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
       SCREAMING_SNAKE_CASE       :      int							=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    list(self.lang_code_to_id.keys()      )

       if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         self._additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    src_lang if src_lang is not None else """en_XX"""
       SCREAMING_SNAKE_CASE       :      Dict							=    self.lang_code_to_id[self._src_lang]
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tgt_lang
       self.set_src_lang_special_tokens(self._src_lang      )


     def __getstate__(				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.__dict__.copy()
       SCREAMING_SNAKE_CASE       :      str							=    None
       SCREAMING_SNAKE_CASE       :      Any							=    self.sp_model.serialized_model_proto()
       return state


     def __setstate__(				self       :	List[Any]					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    d

       # for backward compatibility
       if not hasattr(self					, "sp_model_kwargs"      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {}

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )


     @property
     def 				_A       (				self       :	Any      ):
       return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token


     @property
     def 				_A       (				self       :	List[Any]      ):
       return self._src_lang


     @src_lang.setter
     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    new_src_lang
       self.set_src_lang_special_tokens(self._src_lang      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None					, UpperCAmelCase_       :	bool = False      ):



       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a_					, token_ids_a=a_					, already_has_special_tokens=a_      )

       SCREAMING_SNAKE_CASE       :      str							=    [1] * len(self.prefix_tokens      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    [1] * len(self.suffix_tokens      )
       if token_ids_a is None:
         return prefix_ones + ([0] * len(a_      )) + suffix_ones
       return prefix_ones + ([0] * len(a_      )) + ([0] * len(a_      )) + suffix_ones


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):



       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       SCREAMING_SNAKE_CASE       :      str							=    [self.sep_token_id]
       SCREAMING_SNAKE_CASE       :      str							=    [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep      ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str]					, UpperCAmelCase_       :	Optional[str]					, **UpperCAmelCase_       :	Any      ):



       if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    src_lang
       SCREAMING_SNAKE_CASE       :      Dict							=    self(a_					, add_special_tokens=a_					, return_tensors=a_					, **a_      )
       SCREAMING_SNAKE_CASE       :      Dict							=    self.convert_tokens_to_ids(a_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tgt_lang_id
       return inputs


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {self.convert_ids_to_tokens(a_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	str      ):
       return self.sp_model.encode(a_					, out_type=a_      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]      ):



       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE       :      Any							=    self.sp_model.PieceToId(a_      )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]      ):



       if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    """""".join(a_      ).replace(a_					, " "      ).strip()
       return out_string


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str] = None      ):



       if not os.path.isdir(a_      ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
         return
       SCREAMING_SNAKE_CASE       :      List[str]							=    os.path.join(
           a_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

       return (out_vocab_file,)


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	str = "en_XX"					, UpperCAmelCase_       :	Optional[List[str]] = None					, UpperCAmelCase_       :	str = "ro_RO"					, **UpperCAmelCase_       :	Dict					, ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    src_lang
       SCREAMING_SNAKE_CASE       :      int							=    tgt_lang
       return super().prepare_seqaseq_batch(a_					, a_					, **a_      )


     def 				_A       (				self       :	Optional[Any]      ):
       return self.set_src_lang_special_tokens(self.src_lang      )


     def 				_A       (				self       :	Tuple      ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang      )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.lang_code_to_id[src_lang]
       SCREAMING_SNAKE_CASE       :      Dict							=    []
       SCREAMING_SNAKE_CASE       :      List[str]							=    [self.eos_token_id, self.cur_lang_code]






     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.lang_code_to_id[lang]
       SCREAMING_SNAKE_CASE       :      str							=    []
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [self.eos_token_id, self.cur_lang_code]














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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Union[str, Any]=-1      ):
       # in NER datasets, the last column is usually reserved for NER label
       SCREAMING_SNAKE_CASE       :      int							=    label_idx


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    mode.value
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      int							=    1
       SCREAMING_SNAKE_CASE       :      Any							=    []
       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
         SCREAMING_SNAKE_CASE       :      List[str]							=    []
         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
               SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
               SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
           else:
             SCREAMING_SNAKE_CASE       :      str							=    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       :	Tuple					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    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]:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    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       :	Optional[Any]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      Tuple							=    ["O"] + labels
         return labels
       else:
         return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


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






     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      Dict							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      str							=    ["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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    mode.value
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
       SCREAMING_SNAKE_CASE       :      str							=    []

       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         for sentence in parse_incr(UpperCAmelCase_      ):
           SCREAMING_SNAKE_CASE       :      List[Any]							=    []
           SCREAMING_SNAKE_CASE       :      List[str]							=    []
           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       :	str					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    0
       for sentence in parse_incr(UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    preds_list[example_id]
         SCREAMING_SNAKE_CASE       :      Any							=    ""
         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       :	Dict					, UpperCAmelCase_       :	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",
         ]






