

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 						_lowercase					(	lowercase__		):




      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 						_lowercase					(	lowercase__		):




      for char in word:
            __lowerCAmelCase   :		List[Any]   =   ord(_SCREAMING_SNAKE_CASE		)
            if not _is_chinese_char(_SCREAMING_SNAKE_CASE		):
                  return 0
      return 1







def 						_lowercase					(	lowercase__		):
      __lowerCAmelCase   :		Union[str, Any]   =   set()

      for token in tokens:
            __lowerCAmelCase   :		str   =   len(_SCREAMING_SNAKE_CASE		) > 1 and is_chinese(_SCREAMING_SNAKE_CASE		)
            if chinese_word:
                  word_set.add(_SCREAMING_SNAKE_CASE		)
      __lowerCAmelCase   :		int   =   list(_SCREAMING_SNAKE_CASE		)
      return word_list







def 						_lowercase					(	lowercase__  ,      lowercase__		):




      if not chinese_word_set:
            return bert_tokens
      __lowerCAmelCase   :		Optional[int]   =   max([len(_SCREAMING_SNAKE_CASE		) for w in chinese_word_set]		)

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







def 						_lowercase					(	lowercase__  ,      lowercase__  ,      lowercase__		):
      __lowerCAmelCase   :		List[Any]   =   []

      for i in range(0  ,      len(_SCREAMING_SNAKE_CASE		)  ,      1_0_0		):
            __lowerCAmelCase   :		List[str]   =   ltp_tokenizer.pipeline(lines[i : i + 1_0_0]  ,      tasks=['''cws''']		).cws
            __lowerCAmelCase   :		Optional[Any]   =   [get_chinese_word(_SCREAMING_SNAKE_CASE		) for r in res]
            ltp_res.extend(_SCREAMING_SNAKE_CASE		)
      assert len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		)

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

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

      assert len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		)

      return ref_ids







def 						_lowercase					(	lowercase__		):




      with open(args.file_name  ,      '''r'''  ,      encoding='''utf-8'''		) as f:
            __lowerCAmelCase   :		List[str]   =   f.readlines()
      __lowerCAmelCase   :		List[Any]   =   [line.strip() for line in data if len(_SCREAMING_SNAKE_CASE		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      __lowerCAmelCase   :		int   =   LTP(args.ltp		)  # faster in GPU device
      __lowerCAmelCase   :		Optional[Any]   =   BertTokenizer.from_pretrained(args.bert		)

      __lowerCAmelCase   :		List[Any]   =   prepare_ref(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE		)

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


if __name__ == "__main__":
 _UpperCamelCase					     =						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",
 )

 _UpperCamelCase					     =						parser.parse_args()
 main(args)









from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers.models.esm.modeling_tf_esm import (
     TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFEsmForMaskedLM,
     TFEsmForSequenceClassification,
     TFEsmForTokenClassification,
     TFEsmModel,
 )







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     ):
         _A     =							parent
         _A     =							13
         _A     =							7
         _A     =							True
         _A     =							True
         _A     =							True
         _A     =							99
         _A     =							32
         _A     =							2
         _A     =							4
         _A     =							37
         _A     =							'gelu'
         _A     =							0.1
         _A     =							0.1
         _A     =							512
         _A     =							16
         _A     =							2
         _A     =							0.02
         _A     =							3
         _A     =							4
         _A     =							None




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

         _A     =							None
         if self.use_input_mask:
             _A     =							random_attention_mask([self.batch_size, self.seq_length]							)

         _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]				,     self.num_choices							)

         _A     =							EsmConfig(
             vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     pad_token_id=1				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     )

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							self.prepare_config_and_inputs()

         _A     =							True
         _A     =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2							)

         return (
             config,
             input_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_							)

         _A     =							model(snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         _A     =							True

         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'encoder_hidden_states': encoder_hidden_states,
             'encoder_attention_mask': encoder_attention_mask,
         }
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_				,     encoder_hidden_states=snake_case_							)

         # Also check the case where encoder outputs are not passed
         _A     =							model(snake_case_				,     attention_mask=snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmForMaskedLM(config=snake_case_							)
         _A     =							model([input_ids, input_mask]							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							TFEsmForTokenClassification(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': TFEsmModel,
             'fill-mask': TFEsmForMaskedLM,
             'text-classification': TFEsmForSequenceClassification,
             'token-classification': TFEsmForTokenClassification,
             'zero-shot': TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							TFEsmModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							model_class(snake_case_							)
             assert isinstance(model.get_input_embeddings()				,     tf.keras.layers.Layer							)
             if model_class is TFEsmForMaskedLM:
                 # Output embedding test differs from the main test because they're a matrix, not a layer
                 _A     =							model.get_bias()
                 assert isinstance(snake_case_				,     snake_case_							)
                 for k, v in name.items():
                     assert isinstance(snake_case_				,     tf.Variable							)
             else:
                 _A     =							model.get_output_embeddings()
                 assert x is None
                 _A     =							model.get_bias()
                 assert name is None







@require_tf
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 1, 2, 3, 4, 5]]							)
         _A     =							model(snake_case_							)[0]
         _A     =							[1, 6, 33]
         self.assertEqual(list(output.numpy().shape							)				,     snake_case_							)
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [8.92_1518, -10.58_9814, -6.467_1307],
                     [-6.396_7156, -13.91_1377, -1.121_1915],
                     [-7.78_1247, -13.95_1557, -3.74_0592],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-2							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]							)
         _A     =							model(snake_case_							)[0]
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [0.1444_3092, 0.5412_5327, 0.324_7739],
                     [0.3034_0484, 0.0052_6676, 0.3107_7722],
                     [0.3227_8043, -0.2498_7096, 0.341_4628],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-4							)							)





