
"""simple docstring"""





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

import sentencepiece as spm

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


_a		       =							logging.get_logger(__name__)

_a		       =							'▁'

_a		       =							{'vocab_file': 'sentencepiece.bpe.model'}

_a		       =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

_a		       =							{
    'facebook/xglm-564M': 2_048,
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Dict       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Any       =		["""input_ids""", """attention_mask"""]







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

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    {} if sp_model_kwargs is None else sp_model_kwargs

     # Compatibility with the original tokenizer
     UpperCAmelCase_					:					List[str]			     =    7
     UpperCAmelCase_					:					Optional[int]			     =    [F"""<madeupword{i}>""" for i in range(self.num_madeup_words  )]

     UpperCAmelCase_					:					int			     =    kwargs.get("additional_special_tokens"				,   []  )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

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

     UpperCAmelCase_					:					List[str]			     =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(str(lowercase_  )  )
     UpperCAmelCase_					:					Any			     =    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'

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     UpperCAmelCase_					:					List[str]			     =    1

     # Mimic fairseq token-to-id alignment for the first 4 token
     UpperCAmelCase_					:					Optional[Any]			     =    {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     UpperCAmelCase_					:					int			     =    len(self.sp_model  )
     UpperCAmelCase_					:					Tuple			     =    {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words  )}
     self.fairseq_tokens_to_ids.update(lowercase_  )

     UpperCAmelCase_					:					Dict			     =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.__dict__.copy()
     UpperCAmelCase_					:					Optional[int]			     =    None
     UpperCAmelCase_					:					Optional[int]			     =    self.sp_model.serialized_model_proto()
     return state







  def __setstate__(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    d

     # for backward compatibility
     if not hasattr(self				,   "sp_model_kwargs"  ):
        UpperCAmelCase_					:					str			     =    {}

     UpperCAmelCase_					:					Dict			     =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = False  ):

     """simple docstring"""

     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowercase_				,   token_ids_a=lowercase_				,   already_has_special_tokens=lowercase_  )

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    [self.sep_token_id]

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







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    {self.convert_ids_to_tokens(lowercase_  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return self.sp_model.encode(lowercase_				,   out_type=lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     UpperCAmelCase_					:					Optional[Any]			     =    self.sp_model.PieceToId(lowercase_  )

     # 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				,   lowercase_  ):

     """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				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    "".join(lowercase_  ).replace(lowercase_				,   " "  ).strip()
     return out_string







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					Optional[int]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase_  ) and os.path.isfile(self.vocab_file  ):
        copyfile(self.vocab_file				,   lowercase_  )
     elif not os.path.isfile(self.vocab_file  ):
        with open(lowercase_				,   "wb"  ) as fi:
           UpperCAmelCase_					:					List[Any]			     =    self.sp_model.serialized_model_proto()
           fi.write(lowercase_  )

     return (out_vocab_file,)




import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 0	, lowerCamelCase__ = 0    ):
				lowerCamelCase_						=			end or len(lowerCamelCase__    )
				for i in range(lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			i
								lowerCamelCase_						=			array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												lowerCamelCase_						=			array[temp_index - 1]
												temp_index -= 1
								lowerCamelCase_						=			temp_index_value
				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):  # Max Heap
				lowerCamelCase_						=			index
				lowerCamelCase_						=			2 * index + 1  # Left Node
				lowerCamelCase_						=			2 * index + 2  # Right Node

				if left_index < heap_size and array[largest] < array[left_index]:
								lowerCamelCase_						=			left_index

				if right_index < heap_size and array[largest] < array[right_index]:
								lowerCamelCase_						=			right_index

				if largest != index:
								lowerCamelCase_	,    lowerCamelCase_						=			array[largest], array[index]
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )

				for i in range(n // 2	, -1	, -1    ):
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				for i in range(n - 1	, 0	, -1    ):
								lowerCamelCase_	,    lowerCamelCase_						=			array[0], array[i]
								heapify(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if (array[first_index] > array[middle_index]) != (
				    array[first_index] > array[last_index]
				):
								return array[first_index]
				elif (array[middle_index] > array[first_index]) != (
				    array[middle_index] > array[last_index]
				):
								return array[middle_index]
				else:
								return array[last_index]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			low
				lowerCamelCase_						=			high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								lowerCamelCase_	,    lowerCamelCase_						=			array[j], array[i]
								i += 1





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if len(lowerCamelCase__    ) == 0:
								return array
				lowerCamelCase_						=			2 * math.ceil(math.loga(len(lowerCamelCase__    )    )    )
				lowerCamelCase_						=			1_6
				return intro_sort(lowerCamelCase__	, 0	, len(lowerCamelCase__    )	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(lowerCamelCase__    )
								max_depth -= 1
								lowerCamelCase_						=			median_of_a(lowerCamelCase__	, lowerCamelCase__	, start + ((end - start) // 2) + 1	, end - 1    )
								lowerCamelCase_						=			partition(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								intro_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			p
				return insertion_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__A				=input('''Enter numbers separated by a comma : ''').strip()
			__A				=[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))


