





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple  )   ->    Tuple:
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0x4e_00 and cp <= 0x9f_ff)
      or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
      or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
      or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
      or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
      or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
      or (cp >= 0xf9_00 and cp <= 0xfa_ff)
      or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
  ):  #
    return True

  return False

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str  )   ->    Tuple:
  # word like '180' or '身高' or '神'
  for char in word:
    _lowercase						    =			ord(snake_case__  )
    if not _is_chinese_char(snake_case__  ):
      return 0
  return 1

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]  )   ->    str:
  _lowercase						    =			set()

  for token in tokens:
    _lowercase						    =			len(snake_case__  ) > 1 and is_chinese(snake_case__  )
    if chinese_word:
      word_set.add(snake_case__  )
  _lowercase						    =			list(snake_case__  )
  return word_list

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:set()  )   ->    Dict:
  if not chinese_word_set:
    return bert_tokens
  _lowercase						    =			max([len(snake_case__  ) for w in chinese_word_set]  )

  _lowercase						    =			bert_tokens
  _lowercase    ,		_lowercase						    =			0, len(snake_case__  )
  while start < end:
    _lowercase						    =			True
    if is_chinese(bert_word[start]  ):
      _lowercase						    =			min(end - start      ,				snake_case__  )
      for i in range(snake_case__      ,				1      ,				-1  ):
        _lowercase						    =			''.join(bert_word[start : start + i]  )
        if whole_word in chinese_word_set:
          for j in range(start + 1      ,				start + i  ):
            _lowercase						    =			'##' + bert_word[j]
          _lowercase						    =			start + i
          _lowercase						    =			False
          break
    if single_word:
      start += 1
  return bert_word

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:LTP      ,				snake_case__	:BertTokenizer  )   ->    Optional[Any]:
  _lowercase						    =			[]

  for i in range(0      ,				len(snake_case__  )      ,				100  ):
    _lowercase						    =			ltp_tokenizer.pipeline(lines[i : i + 100]      ,				tasks=['cws']  ).cws
    _lowercase						    =			[get_chinese_word(snake_case__  ) for r in res]
    ltp_res.extend(snake_case__  )
  assert len(snake_case__  ) == len(snake_case__  )

  _lowercase						    =			[]
  for i in range(0      ,				len(snake_case__  )      ,				100  ):
    _lowercase						    =			bert_tokenizer(lines[i : i + 100]      ,				add_special_tokens=snake_case__      ,				truncation=snake_case__      ,				max_length=512  )
    bert_res.extend(res['input_ids']  )
  assert len(snake_case__  ) == len(snake_case__  )

  _lowercase						    =			[]
  for input_ids, chinese_word in zip(snake_case__      ,				snake_case__  ):
    _lowercase						    =			[]
    for id in input_ids:
      _lowercase						    =			bert_tokenizer._convert_id_to_token(snake_case__  )
      input_tokens.append(snake_case__  )
    _lowercase						    =			add_sub_symbol(snake_case__      ,				snake_case__  )
    _lowercase						    =			[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(snake_case__  ):
      if token[:2] == "##":
        _lowercase						    =			token[2:]
        # save chinese tokens' pos
        if len(snake_case__  ) == 1 and _is_chinese_char(ord(snake_case__  )  ):
          ref_id.append(snake_case__  )
    ref_ids.append(snake_case__  )

  assert len(snake_case__  ) == len(snake_case__  )

  return ref_ids

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int  )   ->    Any:
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name      ,				'r'      ,				encoding='utf-8'  ) as f:
    _lowercase						    =			f.readlines()
  _lowercase						    =			[line.strip() for line in data if len(snake_case__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  _lowercase						    =			LTP(args.ltp  )  # faster in GPU device
  _lowercase						    =			BertTokenizer.from_pretrained(args.bert  )

  _lowercase						    =			prepare_ref(snake_case__      ,				snake_case__      ,				snake_case__  )

  with open(args.save_path      ,				'w'      ,				encoding='utf-8'  ) as f:
    _lowercase						    =			[json.dumps(snake_case__  ) + '\n' for ref in ref_ids]
    f.writelines(snake_case__  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser(description="""prepare_chinese_ref""")
      parser.add_argument(
          """--file_name""",
          required=False,
          type=str,
          default="""./resources/chinese-demo.txt""",
          help="""file need process, same as training data in lm""",
      )
      parser.add_argument(
          """--ltp""",
          required=False,
          type=str,
          default="""./resources/ltp""",
          help="""resources for LTP tokenizer, usually a path""",
      )
      parser.add_argument(
          """--bert""",
          required=False,
          type=str,
          default="""./resources/robert""",
          help="""resources for Bert tokenizer""",
      )
      parser.add_argument(
          """--save_path""",
          required=False,
          type=str,
          default="""./resources/ref.txt""",
          help="""path to save res""",
      )

      snake_case								=							parser.parse_args()
      main(args)



"""simple docstring"""


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A	:  List[str]            =     pytest.mark.integration


A	:  Optional[Any]            =     {"comet"}
A	:  int            =     importlib.util.find_spec("fairseq") is not None

A	:  Union[str, Any]            =     {"code_eval"}
A	:  Dict            =     os.name == "nt"

A	:  Dict            =     {"bertscore", "frugalscore", "perplexity"}
A	:  Any            =     importlib.util.find_spec("transformers") is not None


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("\"test requires Fairseq\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("\"test requires transformers\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("\"test not supported on Windows\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names()						)
@for_all_test_methods(
    lowerCAmelCase__			,lowerCAmelCase__			,lowerCAmelCase__						)
@local
class     _UpperCamelCase    (  parameterized.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 ={}
							__UpperCAmelCase		:       List[str]							 =None

							@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"      )
							@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"      )
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										__lowerCAmelCase							       =     datasets.load.import_main_class(metric_module.__name__       ,  dataset=__a      )
										# check parameters
										__lowerCAmelCase							       =     inspect.signature(metric._compute      ).parameters
										self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()      )      )  # no **kwargs
										# run doctest
										with self.patch_intensive_calls(__a       ,  metric_module.__name__      ):
													with self.use_local_metrics():
																try:
																			__lowerCAmelCase							       =     doctest.testmod(__a       ,  verbose=__a       ,  raise_on_error=__a      )
																except doctest.UnexpectedException as e:
																			raise e.exc_info[1]  # raise the exception that doctest caught
										self.assertEqual(results.failed       ,  0      )
										self.assertGreater(results.attempted       ,  1      )

							@slow
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										# run doctest
										with self.use_local_metrics():
													__lowerCAmelCase							       =     doctest.testmod(__a       ,  verbose=__a       ,  raise_on_error=__a      )
										self.assertEqual(results.failed       ,  0      )
										self.assertGreater(results.attempted       ,  1      )

							@contextmanager
							def      snake_case							(	self       ,  __a       ,  __a      ):
										if metric_name in self.INTENSIVE_CALLS_PATCHER:
													with self.INTENSIVE_CALLS_PATCHER[metric_name](__a      ):
																yield
										else:
													yield

							@contextmanager
							def      snake_case							(	self      ):
										def load_local_metric(__a       ,  *__a       ,  **__a      ):
													return load_metric(os.path.join("metrics"       ,  __a      )       ,  *__a       ,  **__a      )

										with patch("datasets.load_metric"      ) as mock_load_metric:
													__lowerCAmelCase							       =     load_local_metric
													yield



							@classmethod
							def      snake_case							(	cls       ,  __a      ):
										def wrapper(__a      ):
													__lowerCAmelCase							       =     contextmanager(__a      )
													__lowerCAmelCase							       =     patcher
													return patcher

										return wrapper


@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("sv"       ,					""       ,					"" )  # handle pytest cli flags
			class     _UpperCamelCase    (  lowerCAmelCase__						):
										'''simple docstring'''







										def      snake_case							(	self       ,  __a      ):
													assert len(input_dict["input_ids"]      ) == 2
													return np.array([1.0_3, 1.0_4]      )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
						__lowerCAmelCase							       =     MockedPredictor()
						yield


@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			import torch

			def bert_cos_score_idf(_UpperCamelCase       ,					_UpperCamelCase       ,					*_UpperCamelCase       ,					**_UpperCamelCase ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCamelCase ) )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("bert_score.scorer.get_model" ), patch(
			    "bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
						__lowerCAmelCase							       =     bert_cos_score_idf
						yield


@LocalMetricTest.register_intensive_calls_patcher("comet" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			def load_from_checkpoint(_UpperCamelCase ):
						class     _UpperCamelCase    :
													'''simple docstring'''







													def      snake_case							(	self       ,  __a       ,  *__a       ,  **__a      ):
																assert len(__a      ) == 2
																__lowerCAmelCase							       =     [0.1_9, 0.9_2]
																return scores, sum(__a      ) / len(__a      )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("comet.download_model" ) as mock_download_model:
						__lowerCAmelCase							       =     None
						with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
									__lowerCAmelCase							       =     load_from_checkpoint
									yield


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     load_metric(os.path.join("metrics"       ,					"seqeval" ) )
			__lowerCAmelCase							       =     "ERROR"
			__lowerCAmelCase							       =     f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
			with pytest.raises(_UpperCamelCase       ,					match=re.escape(_UpperCamelCase ) ):
						metric.compute(predictions=[]       ,					references=[]       ,					scheme=_UpperCamelCase )




