



"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case   					=				{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case   					=				{
    '''junnyu/roformer_chinese_small''': 1_5_3_6,
    '''junnyu/roformer_chinese_base''': 1_5_3_6,
    '''junnyu/roformer_chinese_char_small''': 5_1_2,
    '''junnyu/roformer_chinese_char_base''': 5_1_2,
    '''junnyu/roformer_small_discriminator''': 1_2_8,
    '''junnyu/roformer_small_generator''': 1_2_8,
}


snake_case   					=				{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  VOCAB_FILES_NAMES
   A__		:	Tuple           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	List[Any]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Dict           =  PRETRAINED_INIT_CONFIGURATION
   A__		:	Any           =  RoFormerTokenizer



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	Any=None							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	int=True							,       __lowerCamelCase    :	Any="[UNK]"							,       __lowerCamelCase    :	Tuple="[SEP]"							,       __lowerCamelCase    :	Optional[Any]="[PAD]"							,       __lowerCamelCase    :	Union[str, Any]="[CLS]"							,       __lowerCamelCase    :	int="[MASK]"							,       __lowerCamelCase    :	str=True							,       __lowerCamelCase    :	List[Any]=None							,       **__lowerCamelCase    :	Any							,       ):



     """simple docstring"""
     super().__init__(
         __lowerCamelCase							,       tokenizer_file=__lowerCamelCase							,       do_lower_case=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       sep_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       cls_token=__lowerCamelCase							,       mask_token=__lowerCamelCase							,       tokenize_chinese_chars=__lowerCamelCase							,       strip_accents=__lowerCamelCase							,       **__lowerCamelCase							,       )

     _snake_case   	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
     if (
         pre_tok_state.get('''lowercase'''							,       __lowerCamelCase				) != do_lower_case
         or pre_tok_state.get('''strip_accents'''							,       __lowerCamelCase				) != strip_accents
     ):
       _snake_case   	=      getattr(__lowerCamelCase							,       pre_tok_state.pop('''type'''				)				)
       _snake_case   	=      do_lower_case
       _snake_case   	=      strip_accents
       _snake_case   	=      pre_tok_class(**__lowerCamelCase				)

     _snake_case   	=      do_lower_case



   def __getstate__(       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.__dict__.copy()
     _snake_case   	=      BertPreTokenizer()
     return state



   def __setstate__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      d
     _snake_case   	=      self.__dict__['''_tokenizer'''].get_vocab()
     _snake_case   	=      PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[Any]=None				):



     """simple docstring"""
     _snake_case   	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep				) * [0]
     return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     _snake_case   	=      self._tokenizer.model.save(__lowerCamelCase							,       name=__lowerCamelCase				)
     return tuple(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	List[str]=False							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      BertPreTokenizer()
     return super().save_pretrained(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       **__lowerCamelCase				)







"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
