

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE  :						List[str]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Dict         =    '''▁'''

_SCREAMING_SNAKE_CASE  :						List[str]         =    {'''vocab_file''': '''prophetnet.tokenizer'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}
def        UpperCAmelCase_     (		_A       ):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__					=					collections.OrderedDict()
					with open(_A   ,	'''r'''   ,	encoding='''utf-8'''       ) as reader:
										SCREAMING_SNAKE_CASE__					=					reader.readlines()
					for index, token in enumerate(_A       ):
										SCREAMING_SNAKE_CASE__					=					token.rstrip('''\n'''       )
										SCREAMING_SNAKE_CASE__					=					index
					return vocab







class  UpperCAmelCase__	( A__ ):






		"""simple docstring"""


		a     =      VOCAB_FILES_NAMES
		a     =      PRETRAINED_VOCAB_FILES_MAP
		a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a     =      ["input_ids", "attention_mask"]






		def __init__(							self		:	Dict			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	Optional[Any]="[SEP]"			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	int="[UNK]"			,     __lowerCamelCase		:	str="[PAD]"			,     __lowerCamelCase		:	Optional[Any]="[CLS]"			,     __lowerCamelCase		:	Dict="[MASK]"			,     __lowerCamelCase		:	Optional[Dict[str, Any]] = None			,     **__lowerCamelCase		:	int			,     )	->							None:
							SCREAMING_SNAKE_CASE__					=					{} if sp_model_kwargs is None else sp_model_kwargs

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

							try:
												import sentencepiece as spm
							except ImportError:
												logger.warning(
												    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
												    ''' pip install sentencepiece'''      )
												raise

							SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(str(__lowerCamelCase      )      )
							SCREAMING_SNAKE_CASE__					=					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'

							# put special tokens and [unused] tokens into the vocab
							SCREAMING_SNAKE_CASE__					=					{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

							for i in range(10      ):
												SCREAMING_SNAKE_CASE__					=					f'''[unused{i}]'''
												SCREAMING_SNAKE_CASE__					=					5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							SCREAMING_SNAKE_CASE__					=					12
							SCREAMING_SNAKE_CASE__					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
												self.unique_no_split_tokens.append(__lowerCamelCase      )






		def __getstate__(							self		:	Optional[Any]      )	->							Optional[int]:
							SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
							SCREAMING_SNAKE_CASE__					=					None
							return state






		def __setstate__(							self		:	List[Any]			,     __lowerCamelCase		:	List[str]      )	->							Tuple:
							SCREAMING_SNAKE_CASE__					=					d
							try:
												import sentencepiece as spm
							except ImportError:
												logger.warning(
												    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
												    ''' pip install sentencepiece'''      )
												raise

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

							SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(self.vocab_file      )






		def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

							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 ([0] * len(__lowerCamelCase      )) + [1]
							return ([0] * len(__lowerCamelCase      )) + [1] + ([0] * len(__lowerCamelCase      )) + [1]






		def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
							SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]

							if token_ids_a is None:
												return len(token_ids_a + sep      ) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






		@property
		def    lowercase_	(							self		:	int      )	->							Optional[int]:
							return len(self.sp_model      ) + self.fairseq_offset






		def    lowercase_	(							self		:	Any      )	->							int:
							SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab






		def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	str      )	->							str:
							return self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )






		def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Optional[int]      )	->							Any:
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	int      )	->							Tuple:
							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    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	int      )	->							List[str]:
							SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      ).replace(__lowerCamelCase			,     ''' '''      ).strip()
							return out_string






		def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
							if not os.path.isdir(__lowerCamelCase      ):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
												return
							SCREAMING_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      ) 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:
																	SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
																	fi.write(__lowerCamelCase      )

							return (out_vocab_file,)







		def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:

							if token_ids_a is None:
												return token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep





import random
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					a[left_index]
     SCREAMING_SNAKE_CASE__					=					left_index + 1
     for j in range(left_index + 1   ,	_A       ):
          if a[j] < pivot:
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i], a[j]
               i += 1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i - 1], a[left_index]
     return i - 1
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     if left < right:
          SCREAMING_SNAKE_CASE__					=					random.randint(_A   ,	right - 1       )
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          SCREAMING_SNAKE_CASE__					=					partition(_A   ,	_A   ,	_A       )
          quick_sort_random(
              _A   ,	_A   ,	_A       )  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              _A   ,	pivot_index + 1   ,	_A       )  # recursive quicksort to the right of the pivot point
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					input('''Enter numbers separated by a comma:\n'''       ).strip()
     SCREAMING_SNAKE_CASE__					=					[int(_A       ) for item in user_input.split(''','''       )]

     quick_sort_random(_A   ,	0   ,	len(_A       )       )

     print(_A       )


if __name__ == "__main__":
       main()


