



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  Optional[Any]:
    # 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 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Optional[Any]:
    # word like '180' or '身高' or '神'
    for char in word:
        A__		 =					ord(UpperCamelCase_	)
        if not _is_chinese_char(UpperCamelCase_	):
            return 0
    return 1



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  List[Any]:
    A__		 =					set()

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       set()	)->  List[Any]:
    if not chinese_word_set:
        return bert_tokens
    A__		 =					max([len(UpperCamelCase_	) for w in chinese_word_set]	)

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       LTP ,       UpperCamelCase_				:       BertTokenizer	)->  int:
    A__		 =					[]

    for i in range(0 ,       len(UpperCamelCase_	) ,       1_0_0	):
        A__		 =					ltp_tokenizer.seg(lines[i : i + 1_0_0]	)[0]
        A__		 =					[get_chinese_word(UpperCamelCase_	) for r in res]
        ltp_res.extend(UpperCamelCase_	)
    assert len(UpperCamelCase_	) == len(UpperCamelCase_	)

    A__		 =					[]
    for i in range(0 ,       len(UpperCamelCase_	) ,       1_0_0	):
        A__		 =					bert_tokenizer(lines[i : i + 1_0_0] ,       add_special_tokens=UpperCamelCase_ ,       truncation=UpperCamelCase_ ,       max_length=5_1_2	)
        bert_res.extend(res['''input_ids''']	)
    assert len(UpperCamelCase_	) == len(UpperCamelCase_	)

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

    assert len(UpperCamelCase_	) == len(UpperCamelCase_	)

    return ref_ids



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int	)->  List[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:
        A__		 =					f.readlines()
    A__		 =					[line.strip() for line in data if len(UpperCamelCase_	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    A__		 =					LTP(args.ltp	)  # faster in GPU device
    A__		 =					BertTokenizer.from_pretrained(args.bert	)

    A__		 =					prepare_ref(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

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


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

   _lowercase						  =		parser.parse_args()
   main(args)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       FlaubertConfig,
       TFFlaubertForMultipleChoice,
       TFFlaubertForQuestionAnsweringSimple,
       TFFlaubertForSequenceClassification,
       TFFlaubertForTokenClassification,
       TFFlaubertModel,
       TFFlaubertWithLMHeadModel,
   )



class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						):
         A__		 =					parent
         A__		 =					1_3
         A__		 =					7
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					False
         A__		 =					False
         A__		 =					False
         A__		 =					2
         A__		 =					9_9
         A__		 =					0
         A__		 =					3_2
         A__		 =					2
         A__		 =					4
         A__		 =					0.1
         A__		 =					0.1
         A__		 =					5_1_2
         A__		 =					1_6
         A__		 =					2
         A__		 =					0.0_2
         A__		 =					3
         A__		 =					4
         A__		 =					'''last'''
         A__		 =					True
         A__		 =					None
         A__		 =					0




     def 		snake_case_		(				self):
         A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)
         A__		 =					random_attention_mask([self.batch_size, self.seq_length] ,						dtype=tf.floataa)

         A__		 =					None
         if self.use_input_lengths:
             A__		 =					(
                 ids_tensor([self.batch_size] ,						vocab_size=2) + self.seq_length - 2
             )  # small variation of seq_length

         A__		 =					None
         if self.use_token_type_ids:
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.n_langs)

         A__		 =					None
         A__		 =					None
         A__		 =					None
         if self.use_labels:
             A__		 =					ids_tensor([self.batch_size] ,						self.type_sequence_label_size)
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels)
             A__		 =					ids_tensor([self.batch_size] ,						2 ,						dtype=tf.floataa)
             A__		 =					ids_tensor([self.batch_size] ,						self.num_choices)

         A__		 =					FlaubertConfig(
             vocab_size=self.vocab_size ,						n_special=self.n_special ,						emb_dim=self.hidden_size ,						n_layers=self.num_hidden_layers ,						n_heads=self.num_attention_heads ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						gelu_activation=self.gelu_activation ,						sinusoidal_embeddings=self.sinusoidal_embeddings ,						asm=self.asm ,						causal=self.causal ,						n_langs=self.n_langs ,						max_position_embeddings=self.max_position_embeddings ,						initializer_range=self.initializer_range ,						summary_type=self.summary_type ,						use_proj=self.use_proj ,						bos_token_id=self.bos_token_id ,						)

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertModel(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         A__		 =					[input_ids, input_mask]
         A__		 =					model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertWithLMHeadModel(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForQuestionAnsweringSimple(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForSequenceClassification(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_labels
         A__		 =					TFFlaubertForTokenClassification(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_choices
         A__		 =					TFFlaubertForMultipleChoice(config=a__)
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					{
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices))




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         (
             (
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	
         )		 =					config_and_inputs
         A__		 =					{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''langs''': token_type_ids,
             '''lengths''': input_lengths,
         }
         return config, inputs_dict







@require_tf
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		    =				(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     UpperCamelCase__		    =				(
         {
             '''feature-extraction''': TFFlaubertModel,
             '''fill-mask''': TFFlaubertWithLMHeadModel,
             '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
             '''text-classification''': TFFlaubertForSequenceClassification,
             '''token-classification''': TFFlaubertForTokenClassification,
             '''zero-shot''': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__):
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('''Fast''')
         ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

         return False




     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=a__ ,						emb_dim=3_7)




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_token_classification(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_multiple_choice(*a__)




     @slow
     def 		snake_case_		(				self):
         for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					TFFlaubertModel.from_pretrained(a__)
             self.assertIsNotNone(a__)







@require_tf
@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase			(       unittest.TestCase     ):




     @slow
     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

         A__		 =					tf.convert_to_tensor(
             [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]] ,						dtype=tf.intaa ,						)  # "J'aime flaubert !"

         A__		 =					model(a__)[0]
         A__		 =					tf.TensorShape((1, 8, 5_1_2))
         self.assertEqual(output.shape ,						a__)
         # compare the actual values for a slice.
         A__		 =					tf.convert_to_tensor(
             [
                 [
                     [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                     [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                     [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
                 ]
             ] ,						dtype=tf.floataa ,						)

         self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1e-4))
