



"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case   					=				logging.get_logger(__name__)


snake_case   					=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

snake_case   					=				{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

snake_case   					=				{
    '''allenai/longformer-base-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			snake_case		(		)						->				Tuple:
  _snake_case   	=      (
      list(range(ord('''!'''	)  ,						ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)  ,						ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)  ,						ord('''ÿ'''	) + 1	)	)
  )
  _snake_case   	=      bs[:]
  _snake_case   	=      0
  for b in range(2**8	):
    if b not in bs:
      bs.append(lowerCAmelCase_	)
      cs.append(2**8 + n	)
      n += 1
  _snake_case   	=      [chr(lowerCAmelCase_	) for n in cs]
  return dict(zip(lowerCAmelCase_  ,						lowerCAmelCase_	)	)




def 			snake_case		(		lowerCAmelCase_	)						->				Optional[Any]:
  _snake_case   	=      set()
  _snake_case   	=      word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    _snake_case   	=      char
  return pairs







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Dict           =  VOCAB_FILES_NAMES
   A__		:	Union[str, Any]           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	Optional[int]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Optional[int]           =  ['''input_ids''', '''attention_mask''']



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]="replace"							,       __lowerCamelCase    :	Union[str, Any]="<s>"							,       __lowerCamelCase    :	Optional[int]="</s>"							,       __lowerCamelCase    :	List[Any]="</s>"							,       __lowerCamelCase    :	str="<s>"							,       __lowerCamelCase    :	Dict="<unk>"							,       __lowerCamelCase    :	Any="<pad>"							,       __lowerCamelCase    :	Optional[Any]="<mask>"							,       __lowerCamelCase    :	List[str]=False							,       **__lowerCamelCase    :	Optional[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else bos_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else eos_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else sep_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else cls_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else unk_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else mask_token

     super().__init__(
         errors=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       sep_token=__lowerCamelCase							,       cls_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       mask_token=__lowerCamelCase							,       add_prefix_space=__lowerCamelCase							,       **__lowerCamelCase							,       )

     with open(__lowerCamelCase							,       encoding='''utf-8'''				) as vocab_handle:
       _snake_case   	=      json.load(__lowerCamelCase				)
     _snake_case   	=      {v: k for k, v in self.encoder.items()}
     _snake_case   	=      errors  # how to handle errors in decoding
     _snake_case   	=      bytes_to_unicode()
     _snake_case   	=      {v: k for k, v in self.byte_encoder.items()}
     with open(__lowerCamelCase							,       encoding='''utf-8'''				) as merges_handle:
       _snake_case   	=      merges_handle.read().split('''\n'''				)[1:-1]
     _snake_case   	=      [tuple(merge.split()				) for merge in bpe_merges]
     _snake_case   	=      dict(zip(__lowerCamelCase							,       range(len(__lowerCamelCase				)				)				)				)
     _snake_case   	=      {}
     _snake_case   	=      add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     _snake_case   	=      re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)



   @property
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return len(self.encoder				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     return dict(self.encoder							,       **self.added_tokens_encoder				)



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     if token in self.cache:
       return self.cache[token]
     _snake_case   	=      tuple(__lowerCamelCase				)
     _snake_case   	=      get_pairs(__lowerCamelCase				)

     if not pairs:
       return token

     while True:
       _snake_case   	=      min(__lowerCamelCase							,       key=lambda __lowerCamelCase				: self.bpe_ranks.get(__lowerCamelCase							,       float('''inf'''				)				)				)
       if bigram not in self.bpe_ranks:
         break
       _snake_case	,     _snake_case   	=      bigram
       _snake_case   	=      []
       _snake_case   	=      0
       while i < len(__lowerCamelCase				):
         try:
           _snake_case   	=      word.index(__lowerCamelCase							,       __lowerCamelCase				)
         except ValueError:
           new_word.extend(word[i:]				)
           break
         else:
           new_word.extend(word[i:j]				)
           _snake_case   	=      j

         if word[i] == first and i < len(__lowerCamelCase				) - 1 and word[i + 1] == second:
           new_word.append(first + second				)
           i += 2
         else:
           new_word.append(word[i]				)
           i += 1
       _snake_case   	=      tuple(__lowerCamelCase				)
       _snake_case   	=      new_word
       if len(__lowerCamelCase				) == 1:
         break
       else:
         _snake_case   	=      get_pairs(__lowerCamelCase				)
     _snake_case   	=      ''' '''.join(__lowerCamelCase				)
     _snake_case   	=      word
     return word



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     _snake_case   	=      []
     for token in re.findall(self.pat							,       __lowerCamelCase				):
       _snake_case   	=      ''''''.join(
           self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase				).split(''' '''				)				)
     return bpe_tokens



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     return self.encoder.get(__lowerCamelCase							,       self.encoder.get(self.unk_token				)				)



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Dict				):



     """simple docstring"""
     return self.decoder.get(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      ''''''.join(__lowerCamelCase				)
     _snake_case   	=      bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''							,       errors=self.errors				)
     return text



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



     """simple docstring"""
     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''']				)
     _snake_case   	=      os.path.join(
         __lowerCamelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as f:
       f.write(json.dumps(self.encoder							,       indent=2							,       sort_keys=__lowerCamelCase							,       ensure_ascii=__lowerCamelCase				) + '''\n'''				)

     _snake_case   	=      0
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as writer:
       writer.write('''#version: 0.2\n'''				)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,       key=lambda __lowerCamelCase				: kv[1]				):
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
               ''' Please check that the tokenizer is not corrupted!'''				)
           _snake_case   	=      token_index
         writer.write(''' '''.join(__lowerCamelCase				) + '''\n'''				)
         index += 1

     return vocab_file, merge_file



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



     """simple docstring"""
     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _snake_case   	=      [self.cls_token_id]
     _snake_case   	=      [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



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



     """simple docstring"""
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase							,       token_ids_a=__lowerCamelCase							,       already_has_special_tokens=__lowerCamelCase				)

     if token_ids_a is None:
       return [1] + ([0] * len(__lowerCamelCase				)) + [1]
     return [1] + ([0] * len(__lowerCamelCase				)) + [1, 1] + ([0] * len(__lowerCamelCase				)) + [1]



   def    __UpperCAmelCase    (       self    :	str							,       __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 + sep + token_ids_a + sep				) * [0]





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[Any]=False							,       **__lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      kwargs.pop('''add_prefix_space'''							,       self.add_prefix_space				)
     if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase				) > 0 and not text[0].isspace()):
       _snake_case   	=      ''' ''' + text
     return (text, kwargs)











'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




