import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				str     			={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase      :				str     			={
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}




class 						__snake_case						(			a__		):





      '''simple docstring'''






      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Optional[Any]=None     ,	_UpperCamelCase  :					str=False     ,	_UpperCamelCase  :					Dict=True     ,	_UpperCamelCase  :					int=False     ,	_UpperCamelCase  :					int="<s>"     ,	_UpperCamelCase  :					List[str]="</s>"     ,	_UpperCamelCase  :					Any="<unk>"     ,	_UpperCamelCase  :					Any="<sep>"     ,	_UpperCamelCase  :					Dict="<pad>"     ,	_UpperCamelCase  :					List[str]="<cls>"     ,	_UpperCamelCase  :					Union[str, Any]="<mask>"     ,	_UpperCamelCase  :					int=["<eop>", "<eod>"]     ,	**_UpperCamelCase  :					int     ,	)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    AddedToken(UpperCamelCase_     ,	lstrip=UpperCamelCase_     ,	rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_     ,	UpperCamelCase_) else mask_token

          super().__init__(
              vocab_file=UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	remove_space=UpperCamelCase_     ,	keep_accents=UpperCamelCase_     ,	bos_token=UpperCamelCase_     ,	eos_token=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	additional_special_tokens=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          _lowerCamelCase  :		Union[str, Any]			    =    3
          _lowerCamelCase  :		Optional[int]			    =    do_lower_case
          _lowerCamelCase  :		Tuple			    =    remove_space
          _lowerCamelCase  :		Any			    =    keep_accents
          _lowerCamelCase  :		Optional[Any]			    =    vocab_file
          _lowerCamelCase  :		str			    =    False if not self.vocab_file else True

          try:
              import jieba
          except ModuleNotFoundError as error:
              raise error.__class__(
                  """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
                  """See https://pypi.org/project/jieba/ for installation.""")
          _lowerCamelCase  :		Dict			    =    jieba
          _lowerCamelCase  :		Optional[Any]			    =    str.maketrans(""" \n"""     ,	"""\u2582\u2583""")



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					List[int]     ,	_UpperCamelCase  :					Optional[List[int]] = None)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    [self.sep_token_id]
          _lowerCamelCase  :		List[str]			    =    [self.cls_token_id]
          if token_ids_a is None:
              return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int]     ,	_UpperCamelCase  :					List[int]     ,	_UpperCamelCase  :					Optional[List[int]] = None)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    [self.sep_token_id]
          _lowerCamelCase  :		int			    =    [2]

          if token_ids_a is None:
              return len(token_ids_a + sep) * [0] + cls_segment_id
          return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Optional[str] = None)					->Any:


          """simple docstring"""


          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer.""")

          if not os.path.isdir(UpperCamelCase_):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
              return
          _lowerCamelCase  :		int			    =    os.path.join(
              UpperCamelCase_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
              copyfile(self.vocab_file     ,	UpperCamelCase_)

          return (out_vocab_file,)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					List[Any]     ,	*_UpperCamelCase  :					Union[str, Any]     ,	**_UpperCamelCase  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    [
              ''' '''.join([x.translate(self.translator) for x in self.jieba.cut(UpperCamelCase_     ,	cut_all=UpperCamelCase_)])
              for text in batch_text_or_text_pairs
          ]
          return super()._batch_encode_plus(UpperCamelCase_     ,	*UpperCamelCase_     ,	**UpperCamelCase_)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	*_UpperCamelCase  :					Dict     ,	**_UpperCamelCase  :					Optional[int])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    super()._decode(*UpperCamelCase_     ,	**UpperCamelCase_)
          _lowerCamelCase  :		int			    =    text.replace(""" """     ,	"""""").replace("""\u2582"""     ,	""" """).replace("""\u2583"""     ,	"""\n""")
          return text








"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


