



"""simple docstring"""







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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=-1		)      ->	Optional[int]:
				# in NER datasets, the last column is usually reserved for NER label
				A__											=					label_idx


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


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


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

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


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A__						:   str				=    logging.get_logger(__name__)

A__						:   str				=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__						:   Tuple				=    {
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

A__						:   str				=    {
    """junnyu/roformer_chinese_small""": 1536,
    """junnyu/roformer_chinese_base""": 1536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


A__						:   Tuple				=    {
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Union[str, Any]					=     VOCAB_FILES_NAMES
    lowerCamelCase    :   int					=     PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase    :   Any					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase    :   Optional[int]					=     PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase    :   Dict					=     RoFormerTokenizer







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="[UNK]"     ,	SCREAMING_SNAKE_CASE_="[SEP]"     ,	SCREAMING_SNAKE_CASE_="[PAD]"     ,	SCREAMING_SNAKE_CASE_="[CLS]"     ,	SCREAMING_SNAKE_CASE_="[MASK]"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE_     ,	tokenizer_file=SCREAMING_SNAKE_CASE_     ,	do_lower_case=SCREAMING_SNAKE_CASE_     ,	unk_token=SCREAMING_SNAKE_CASE_     ,	sep_token=SCREAMING_SNAKE_CASE_     ,	pad_token=SCREAMING_SNAKE_CASE_     ,	cls_token=SCREAMING_SNAKE_CASE_     ,	mask_token=SCREAMING_SNAKE_CASE_     ,	tokenize_chinese_chars=SCREAMING_SNAKE_CASE_     ,	strip_accents=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)

         __lowerCamelCase  :      Optional[int]														=						json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
         if (
             pre_tok_state.get('lowercase'     ,	SCREAMING_SNAKE_CASE_      ) != do_lower_case
             or pre_tok_state.get('strip_accents'     ,	SCREAMING_SNAKE_CASE_      ) != strip_accents
         ):
              __lowerCamelCase  :      Optional[int]														=						getattr(SCREAMING_SNAKE_CASE_     ,	pre_tok_state.pop('type'      )      )
              __lowerCamelCase  :      Union[str, Any]														=						do_lower_case
              __lowerCamelCase  :      str														=						strip_accents
              __lowerCamelCase  :      Optional[Any]														=						pre_tok_class(**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						do_lower_case







    def __getstate__(				self      )							->	List[str]:
         __lowerCamelCase  :      Union[str, Any]														=						self.__dict__.copy()
         __lowerCamelCase  :      Dict														=						BertPreTokenizer()
         return state







    def __setstate__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						d
         __lowerCamelCase  :      List[Any]														=						self.__dict__['_tokenizer'].get_vocab()
         __lowerCamelCase  :      Union[str, Any]														=						PreTokenizer.custom(JiebaPreTokenizer(SCREAMING_SNAKE_CASE_      )      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	str:
         __lowerCamelCase  :      Union[str, Any]														=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	List[int]:
         __lowerCamelCase  :      List[str]														=						[self.sep_token_id]
         __lowerCamelCase  :      Dict														=						[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      ) * [0] + len(token_ids_a + sep      ) * [1]







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	Tuple[str]:
         __lowerCamelCase  :      Optional[Any]														=						self._tokenizer.model.save(SCREAMING_SNAKE_CASE_     ,	name=SCREAMING_SNAKE_CASE_      )
         return tuple(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Any:
         __lowerCamelCase  :      Tuple														=						BertPreTokenizer()
         return super().save_pretrained(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






