


'''simple docstring'''


from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      int=1E-12):
      A_						:  Optional[Any]          =				jnp.divide(emb_a.T							,						jnp.clip(jnp.linalg.norm(snake_case__							,						axis=1)							,						a_min=snake_case__)).T
      A_						:  str          =				jnp.divide(emb_a.T							,						jnp.clip(jnp.linalg.norm(snake_case__							,						axis=1)							,						a_min=snake_case__)).T
      return jnp.matmul(snake_case__							,						norm_emb_a.T)



class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  a_  					=							42
  a_  					=							jnp.floataa






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				FlaxCLIPVisionModule(self.config.vision_config )
        A_						:  Optional[Any]          =				nn.Dense(self.config.projection_dim	,use_bias=A_	,dtype=self.dtype )

        A_						:  Tuple          =				self.param("""concept_embeds"""	,jax.nn.initializers.ones	,(17, self.config.projection_dim) )
        A_						:  Union[str, Any]          =				self.param(
            """special_care_embeds"""	,jax.nn.initializers.ones	,(3, self.config.projection_dim) )

        A_						:  Optional[Any]          =				self.param("""concept_embeds_weights"""	,jax.nn.initializers.ones	,(17,) )
        A_						:  Tuple          =				self.param("""special_care_embeds_weights"""	,jax.nn.initializers.ones	,(3,) )






  def __call__(							self :       Optional[int]	,_a :       str ):

        '''simple docstring'''




        A_						:  Tuple          =				self.vision_model(A_ )[1]
        A_						:  str          =				self.visual_projection(A_ )

        A_						:  List[str]          =				jax_cosine_distance(A_	,self.special_care_embeds )
        A_						:  Union[str, Any]          =				jax_cosine_distance(A_	,self.concept_embeds )

        # increase this value to create a stronger `nfsw` filter
        # at the cost of increasing the possibility of filtering benign image inputs
        A_						:  str          =				0.0

        A_						:  Tuple          =				special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
        A_						:  Optional[int]          =				jnp.round(A_	,3 )
        A_						:  Dict          =				jnp.any(special_scores > 0	,axis=1	,keepdims=A_ )
        # Use a lower threshold if an image has any special care concept
        A_						:  int          =				is_special_care * 0.01

        A_						:  Optional[int]          =				cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
        A_						:  Optional[int]          =				jnp.round(A_	,3 )
        A_						:  Optional[Any]          =				jnp.any(concept_scores > 0	,axis=1 )

        return has_nsfw_concepts







class 	__lowerCAmelCase		(							_lowercase    ):


  '''simple docstring'''







  a_  					=							CLIPConfig
  a_  					=							'''clip_input'''
  a_  					=							FlaxStableDiffusionSafetyCheckerModule






  def __init__(							self :       Union[str, Any]	,_a :       CLIPConfig	,_a :       Optional[Tuple] = None	,_a :       int = 0	,_a :       jnp.dtype = jnp.floataa	,_a :       bool = True	,**_a :       str	,):

        '''simple docstring'''




        if input_shape is None:
              A_						:  List[Any]          =				(1, 224, 224, 3)
        A_						:  Any          =				self.module_class(config=A_	,dtype=A_	,**A_ )
        super().__init__(A_	,A_	,input_shape=A_	,seed=A_	,dtype=A_	,_do_init=_do_init )






  def        _a							(							self :       str	,_a :       jax.random.KeyArray	,_a :       Tuple	,_a :       FrozenDict = None ):

        '''simple docstring'''




        A_						:  List[str]          =				jax.random.normal(A_	,A_ )

        A_   ,					A_						:  Optional[int]          =				jax.random.split(A_ )
        A_						:  Any          =				{"""params""": params_rng, """dropout""": dropout_rng}

        A_						:  Optional[int]          =				self.module.init(A_	,A_ )["""params"""]

        return random_params






  def __call__(							self :       str	,_a :       Optional[Any]	,_a :       dict = None	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				jnp.transpose(A_	,(0, 2, 3, 1) )

        return self.module.apply(
            {"""params""": params or self.params}	,jnp.array(A_	,dtype=jnp.floataa )	,rngs={}	,)




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      lowerCamelCase					(      lowerCamelCase  :      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 >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
          or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
          or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
          or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
          or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
          or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
      ):  #
            return True

      return False




def      lowerCamelCase					(      lowerCamelCase  :      str):
      # word like '180' or '身高' or '神'
      for char in word:
            A_						:  Optional[Any]          =				ord(lowerCamelCase)
            if not _is_chinese_char(lowerCamelCase):
                  return 0
      return 1




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

      for token in tokens:
            A_						:  str          =				len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
            if chinese_word:
                  word_set.add(lowerCamelCase)
      A_						:  Any          =				list(lowerCamelCase)
      return word_list




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

      A_						:  str          =				bert_tokens
      A_   ,					A_						:  Any          =				0, len(lowerCamelCase)
      while start < end:
            A_						:  Tuple          =				True
            if is_chinese(bert_word[start]):
                  A_						:  List[str]          =				min(end - start							,						lowerCamelCase)
                  for i in range(lowerCamelCase							,						1							,						-1):
                        A_						:  Tuple          =				"""""".join(bert_word[start : start + i])
                        if whole_word in chinese_word_set:
                              for j in range(start + 1							,						start + i):
                                    A_						:  Dict          =				"""##""" + bert_word[j]
                              A_						:  str          =				start + i
                              A_						:  Dict          =				False
                              break
            if single_word:
                  start += 1
      return bert_word




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				bert_tokenizer(lines[i : i + 100]							,						add_special_tokens=lowerCamelCase							,						truncation=lowerCamelCase							,						max_length=512)
            bert_res.extend(res["""input_ids"""])
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  Union[str, Any]          =				[]
      for input_ids, chinese_word in zip(lowerCamelCase							,						lowerCamelCase):
            A_						:  List[Any]          =				[]
            for id in input_ids:
                  A_						:  List[Any]          =				bert_tokenizer._convert_id_to_token(lowerCamelCase)
                  input_tokens.append(lowerCamelCase)
            A_						:  int          =				add_sub_symbol(lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				[]
            # 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_						:  Optional[Any]          =				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      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      # 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_						:  Optional[int]          =				f.readlines()
      A_						:  Union[str, Any]          =				[line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      A_						:  Optional[Any]          =				LTP(args.ltp)  # faster in GPU device
      A_						:  Dict          =				BertTokenizer.from_pretrained(args.bert)

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

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


if __name__ == "__main__":
     __magic_name__			=      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',
     )

     __magic_name__			=      parser.parse_args()
     main(args)
