

import torch

from transformers import AutoTokenizer
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       def __init__(						self       :						str    ,		lowerCAmelCase__       :						Tuple							)			->    Optional[Any]:
             snake_case__					=   AutoTokenizer.from_pretrained(lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						Optional[int]							)			->    Optional[int]:

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

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

             elif isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							) and all(isinstance(_x    ,		lowerCAmelCase__							) for _x in x							):
                   snake_case__					=   self.tokenizer(
                       lowerCAmelCase__    ,		padding="""max_length"""    ,		max_length=384    ,		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 UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						List[Any]    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						str=0.70							)			->    Any:
             snake_case__					=   []
             for idx in range(len(W_query["""input_ids"""]							)							):
                   snake_case__					=   snake_case__					=   range(p_start.shape[1]							)

                   snake_case__					=   []
                   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__							)
                   snake_case__					=   []
                   for k in range(len(lowerCAmelCase__							)							):
                         if output[k][2] * output[k][3] >= thresh:
                               snake_case__	,			snake_case__					=   output[k][0], output[k][1]
                               snake_case__					=   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









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						:   Union[str, Any]										=    None


lowerCAmelCase						:   str										=    logging.get_logger(__name__)


lowerCAmelCase						:   Any										=    '''▁'''

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

lowerCAmelCase						:   Optional[Any]										=    {
    '''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						:   Union[str, Any]										=    {
    '''google/reformer-crime-and-punishment''': 52_4288,
}
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     Union[str, Any]				    =       VOCAB_FILES_NAMES
       UpperCamelCase__ :     str				    =       PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__ :     str				    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__ :     str				    =       ['''input_ids''', '''attention_mask''']
       UpperCamelCase__ :     Optional[Any]				    =       ReformerTokenizer

       def __init__(						self       :						List[str]    ,		lowerCAmelCase__       :						Tuple=None    ,		lowerCAmelCase__       :						Optional[Any]=None    ,		lowerCAmelCase__       :						Dict="</s>"    ,		lowerCAmelCase__       :						Tuple="<unk>"    ,		lowerCAmelCase__       :						Dict=[]    ,		**lowerCAmelCase__       :						Optional[Any]    ,		)			->    Dict:
             super().__init__(
                 lowerCAmelCase__    ,		tokenizer_file=lowerCAmelCase__    ,		eos_token=lowerCAmelCase__    ,		unk_token=lowerCAmelCase__    ,		additional_special_tokens=lowerCAmelCase__    ,		**lowerCAmelCase__    ,		)

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

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						Optional[str] = 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(lowerCAmelCase__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             snake_case__					=   os.path.join(
                 lowerCAmelCase__    ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

             return (out_vocab_file,)






