





'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 		UpperCamelCase							(				_lowerCamelCase   :   List[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 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    # word like '180' or '身高' or '神'
    for char in word:
        A__							=     ord(lowerCAmelCase_			)
        if not _is_chinese_char(lowerCAmelCase_			):
            return 0
    return 1





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     set()

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





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    if not chinese_word_set:
        return bert_tokens
    A__							=     max([len(lowerCAmelCase_			) for w in chinese_word_set]			)

    A__							=     bert_tokens
    A__,      A__							=     0, len(lowerCAmelCase_			)
    while start < end:
        A__							=     True
        if is_chinese(bert_word[start]			):
            A__							=     min(end - start		,			lowerCAmelCase_			)
            for i in range(lowerCAmelCase_		,			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 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]			):
    A__							=     []

    for i in range(0		,			len(lowerCAmelCase_			)		,			1_00			):
        A__							=     ltp_tokenizer.pipeline(lines[i : i + 1_00]		,			tasks=["cws"]			).cws
        A__							=     [get_chinese_word(lowerCAmelCase_			) for r in res]
        ltp_res.extend(lowerCAmelCase_			)
    assert len(lowerCAmelCase_			) == len(lowerCAmelCase_			)

    A__							=     []
    for i in range(0		,			len(lowerCAmelCase_			)		,			1_00			):
        A__							=     bert_tokenizer(lines[i : i + 1_00]		,			add_special_tokens=lowerCAmelCase_		,			truncation=lowerCAmelCase_		,			max_length=5_12			)
        bert_res.extend(res["input_ids"]			)
    assert len(lowerCAmelCase_			) == len(lowerCAmelCase_			)

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

    assert len(lowerCAmelCase_			) == len(lowerCAmelCase_			)

    return ref_ids





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, 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(lowerCAmelCase_			) > 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(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_			)

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


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      =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",
  )

  __lowerCAmelCase				:      List[str]						      =parser.parse_args()
  main(args)







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Dict=1_0       ,							__lowerCamelCase    :       Optional[int]=[1_0, 2_0, 3_0, 4_0]       ,							__lowerCamelCase    :       Optional[int]=[1, 1, 2, 1]       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       str=True       ,							__lowerCamelCase    :       str="relu"       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  embeddings_size
         UpperCAmelCase      					=  hidden_sizes
         UpperCAmelCase      					=  depths
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  scope
         UpperCAmelCase      					=  len(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase      					=  None
         if self.use_labels:
            UpperCAmelCase      					=  ids_tensor([self.batch_size]       ,							self.num_labels							)

         UpperCAmelCase      					=  self.get_config()

         return config, pixel_values, labels





      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return ResNetConfig(
             num_channels=self.num_channels       ,							embeddings_size=self.embeddings_size       ,							hidden_sizes=self.hidden_sizes       ,							depths=self.depths       ,							hidden_act=self.hidden_act       ,							num_labels=self.num_labels       ,							image_size=self.image_size       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Tuple							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModel(config=__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  TFResNetForImageClassification(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.prepare_config_and_inputs()
         UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  config_and_inputs
         UpperCAmelCase      					=  {"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_tf
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         return





      @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         pass





      @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  inspect.signature(model.call							)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase      					=  [*signature.parameters.keys()]

            UpperCAmelCase      					=  ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase      					=  self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase							)       ,							expected_num_stages + 1							)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]							)       ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase      					=  ["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase      					=  layer_type
               UpperCAmelCase      					=  True
               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase      					=  True

               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  TFResNetModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)

def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_tf
@require_vision
class 	__lowercase					(	unittest.TestCase	):





      @cached_property
      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             if is_vision_available()
             else None
         )





      @slow
      def 							_lowercase					(		self    :       Dict							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""tf"""							)

         # forward pass
         UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,							__lowerCamelCase       ,							atol=1e-4							)							)



