






'''simple docstring'''




import torch

from transformers import AutoTokenizer

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			Optional[int]	):





         '''simple docstring'''





         _A      =			AutoTokenizer.from_pretrained(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			List[Any]	):





         '''simple docstring'''





         if isinstance(__UpperCAmelCase   ,   __UpperCAmelCase	) and all(isinstance(_x   ,   __UpperCAmelCase	) for _x in x	):
               _A      =			None
               for l in x:
                     _A      =			self.tokenizer(
                         __UpperCAmelCase   ,   padding="max_length"   ,   max_length=384   ,   truncation=__UpperCAmelCase   ,   return_tensors="pt"   ,   )
                     _A      =			torch.tensor([len(__UpperCAmelCase	)]	)
                     if d is not None:
                           for k in d.keys():
                                 _A      =			torch.cat((d[k], t[k])   ,   0	)
                     else:
                           _A      =			t

               _A      =			torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"	)	)
               _A      =			torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"	)	)

         elif isinstance(__UpperCAmelCase   ,   __UpperCAmelCase	) and all(isinstance(_x   ,   __UpperCAmelCase	) for _x in x	):
               _A      =			self.tokenizer(
                   __UpperCAmelCase   ,   padding="max_length"   ,   max_length=384   ,   truncation=__UpperCAmelCase   ,   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 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			int=0.70	):





         '''simple docstring'''





         _A      =			[]
         for idx in range(len(W_query["input_ids"]	)	):
               _A      =			_A      =			range(p_start.shape[1]	)

               _A      =			[]
               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 __UpperCAmelCase	: (tup[2] * tup[3])   ,   reverse=__UpperCAmelCase	)
               _A      =			[]
               for k in range(len(__UpperCAmelCase	)	):
                     if output[k][2] * output[k][3] >= thresh:
                           _A  ,			_A      =			output[k][0], output[k][1]
                           _A      =			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(__UpperCAmelCase	)

         return final_outputs













'''simple docstring'''




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

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


if is_sentencepiece_available():
    from .tokenization_reformer import ReformerTokenizer
else:
    lowerCamelCase_							      =      None


lowerCamelCase_							      =      logging.get_logger(__name__)


lowerCamelCase_							      =      '''▁'''

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

lowerCamelCase_							      =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

lowerCamelCase_							      =      {
    '''google/reformer-crime-and-punishment''': 52_42_88,
}

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		VOCAB_FILES_NAMES
   snake_case					       =		PRETRAINED_VOCAB_FILES_MAP
   snake_case					       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case					       =		['''input_ids''', '''attention_mask''']
   snake_case					       =		ReformerTokenizer

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			List[Any]="</s>"   ,   __UpperCAmelCase				:			Optional[Any]="<unk>"   ,   __UpperCAmelCase				:			List[Any]=[]   ,   **__UpperCAmelCase				:			int   ,   ):





         '''simple docstring'''





         super().__init__(
             __UpperCAmelCase   ,   tokenizer_file=__UpperCAmelCase   ,   eos_token=__UpperCAmelCase   ,   unk_token=__UpperCAmelCase   ,   additional_special_tokens=__UpperCAmelCase   ,   **__UpperCAmelCase   ,   )

         _A      =			vocab_file
         _A      =			False if not self.vocab_file else True

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





         '''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
         _A      =			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,)





