import torch

from transformers import AutoTokenizer




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		Any	)			->				str:
        _UpperCamelCase  :							List[str]	    =	AutoTokenizer.from_pretrained(lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		List[str]	)			->				str:

        if isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							Any	    =	None
               for l in x:
                      _UpperCamelCase  :							List[str]	    =	self.tokenizer(
                          lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )
                      _UpperCamelCase  :							Optional[Any]	    =	torch.tensor([len(lowerCamelCase	)]	)
                      if d is not None:
                             for k in d.keys():
                                    _UpperCamelCase  :							Dict	    =	torch.cat((d[k], t[k])							,      0	)
                      else:
                             _UpperCamelCase  :							Optional[int]	    =	t

               _UpperCamelCase  :							Optional[int]	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'	)	)
               _UpperCamelCase  :							Dict	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'	)	)

        elif isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							int	    =	self.tokenizer(
                   lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )

        else:
               raise Exception(
                   'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                   ' strings` for supports are supported.'	)

        return d

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]=0.7_0	)			->				Optional[int]:
        _UpperCamelCase  :							Union[str, Any]	    =	[]
        for idx in range(len(W_query['input_ids']	)	):
               _UpperCamelCase  :							List[Any]	    =	range(p_start.shape[1]	)

               _UpperCamelCase  :							List[str]	    =	[]
               for start_id in start_indexes:
                      for end_id in end_indexes:
                             if start_id < end_id:
                                    output.append(
                                        (
                                            start_id,
                                            end_id,
                                            p_start[idx][start_id].item(),
                                            p_end[idx][end_id].item(),
                                        )	)

               output.sort(key=lambda lowerCamelCase	: (tup[2] * tup[3])							,      reverse=lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	[]
               for k in range(len(lowerCamelCase	)	):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	output[k][0], output[k][1]
                             _UpperCamelCase  :							Optional[Any]	    =	self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]	)
                             temp.append((decoded, output[k][2] * output[k][3])	)

               final_outputs.append(lowerCamelCase	)

        return final_outputs








'''simple docstring'''




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

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


a  :     Any       =       logging.get_logger(__name__)

a  :     int       =       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

a  :     List[Any]       =       {
    """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      UpperCamelCase_					(				__magic_name__     ):





	def __init__(    self						,						A=None						,						A=None						,						A=False						,						A=True						,						A=False						,						A="<s>"						,						A="</s>"						,						A="<unk>"						,						A="<sep>"						,						A="<pad>"						,						A="<cls>"						,						A="<mask>"						,						A=["<eop>", "<eod>"]						,						**A						,						)     ->     Tuple:
								UpperCAmelCase	:						str					=						AddedToken(A						,						lstrip=A						,						rstrip=A       ) if isinstance(A						,						A       ) else mask_token

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

								UpperCAmelCase	:						List[Any]					=						3
								UpperCAmelCase	:						Tuple					=						do_lower_case
								UpperCAmelCase	:						Union[str, Any]					=						remove_space
								UpperCAmelCase	:						Tuple					=						keep_accents
								UpperCAmelCase	:						Any					=						vocab_file
								UpperCAmelCase	:						Tuple					=						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."""       )
								UpperCAmelCase	:						int					=						jieba
								UpperCAmelCase	:						Optional[int]					=						str.maketrans(""" \n"""						,						"""\u2582\u2583"""       )





	def 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Optional[int]					=						[self.sep_token_id]
								UpperCAmelCase	:						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 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Union[str, Any]					=						[self.sep_token_id]
								UpperCAmelCase	:						str					=						[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 					_lowercase(    self						,						A						,						A = None       )     ->     Tuple[str]:
								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(A       ):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
															return
								UpperCAmelCase	:						Tuple					=						os.path.join(
								    A						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

								return (out_vocab_file,)





	def 					_lowercase(    self						,						A						,						*A						,						**A       )     ->     List[Any]:
								UpperCAmelCase	:						Optional[Any]					=						[
								    """ """.join([x.translate(self.translator       ) for x in self.jieba.cut(A						,						cut_all=A       )]       )
								    for text in batch_text_or_text_pairs
								]
								return super()._batch_encode_plus(A						,						*A						,						**A       )






	def 					_lowercase(    self						,						*A						,						**A       )     ->     str:
								UpperCAmelCase	:						Optional[int]					=						super()._decode(*A						,						**A       )
								UpperCAmelCase	:						int					=						text.replace(""" """						,						""""""       ).replace("""\u2582"""						,						""" """       ).replace("""\u2583"""						,						"""\n"""       )
								return text

