
'''simple docstring'''


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

import sentencepiece as spm

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


snake_case_ :  Union[str, Any]     =					logging.get_logger(__name__)

snake_case_ :  List[str]     =					'▁'

snake_case_ :  str     =					{'vocab_file': 'sentencepiece.bpe.model'}

snake_case_ :  str     =					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

snake_case_ :  List[Any]     =					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}


class 						lowercase__	(  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''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   sep_token=lowerCamelCase__ ,   cls_token=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   sp_model_kwargs=self.sp_model_kwargs ,   **lowerCamelCase__ ,   )

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )
      UpperCamelCase       =  vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # Mimic fairseq token-to-id alignment for the first 4 token
      UpperCamelCase       =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      UpperCamelCase       =  1

      UpperCamelCase       =  len(self.sp_model   ) + self.fairseq_offset
      UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''   ):
         UpperCamelCase       =  {}

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]
      UpperCamelCase       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 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 ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [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]







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      UpperCamelCase       =  self.sp_model.PieceToId(lowerCamelCase__   )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  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__   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(lowerCamelCase__ ,   '''wb'''   ) as fi:
            UpperCamelCase       =  self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase__   )

      return (out_vocab_file,)







'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   if start < end:
      UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
      UpperCamelCase       =  a[end]
      UpperCamelCase       =  a[pivot]
      UpperCamelCase       =  temp

      UpperCamelCase     , UpperCamelCase       =  _in_place_partition(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
      count += _in_place_quick_sort(_UpperCAmelCase,      _UpperCAmelCase,      p - 1)
      count += _in_place_quick_sort(_UpperCAmelCase,      p + 1,      _UpperCAmelCase)
   return count



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  a[pivot]
   UpperCamelCase       =  temp
   UpperCamelCase       =  start - 1
   for index in range(_UpperCAmelCase,      _UpperCAmelCase):
      count += 1
      if a[index] < a[end]:  # check if current val is less than pivot value
         UpperCamelCase       =  new_pivot_index + 1
         UpperCamelCase       =  a[new_pivot_index]
         UpperCamelCase       =  a[index]
         UpperCamelCase       =  temp

   UpperCamelCase       =  a[new_pivot_index + 1]
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  temp
   return new_pivot_index + 1, count


snake_case_ :  List[Any]     =					TemporaryFile()
snake_case_ :  Optional[Any]     =					100  # 1000 elements are to be sorted


snake_case_  , snake_case_ :  List[str]     =					0, 1  # mean and standard deviation
snake_case_ :  Any     =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
snake_case_ :  Optional[int]     =					np.load(outfile)
snake_case_ :  Optional[int]     =					len(M) - 1
snake_case_ :  List[str]     =					_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)





