
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


_a:    Tuple 							=			logging.get_logger(__name__)

_a:    Tuple 							=			'▁'

_a:    Any 							=			{'vocab_file': 'prophetnet.tokenizer'}

_a:    List[str] 							=			{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

_a:    str 							=			{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

_a:    Any 							=			{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			collections.OrderedDict()
	with open(A_       ,     "r"       ,     encoding="utf-8"    ) as reader:
		UpperCAmelCase_       				=			reader.readlines()
	for index, token in enumerate(A_    ):
		UpperCAmelCase_       				=			token.rstrip("\n"    )
		UpperCAmelCase_       				=			index
	return vocab




class 						__UpperCamelCase    (					_A						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						int="[SEP]"		,	lowerCAmelCase	:						Optional[int]="[SEP]"		,	lowerCAmelCase	:						Optional[Any]="[SEP]"		,	lowerCAmelCase	:						Optional[int]="[UNK]"		,	lowerCAmelCase	:						List[Any]="[PAD]"		,	lowerCAmelCase	:						Optional[Any]="[CLS]"		,	lowerCAmelCase	:						Any="[MASK]"		,	lowerCAmelCase	:						Optional[Dict[str, Any]] = None		,	**lowerCAmelCase	:						Tuple		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			{} if sp_model_kwargs is None else sp_model_kwargs

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

		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

		UpperCAmelCase_       				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(str(UpperCamelCase__							)							)
		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'

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

		for i in range(10							):
			UpperCAmelCase_       				=			F"[unused{i}]"
			UpperCAmelCase_       				=			5 + i

		# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
		UpperCAmelCase_       				=			12
		UpperCAmelCase_       				=			{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(UpperCamelCase__							)




	def __getstate__(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.__dict__.copy()
		UpperCAmelCase_       				=			None
		return state




	def __setstate__(		self	:						int		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			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"							):
			UpperCAmelCase_       				=			{}

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




	def       __A					(		self	:						List[str]		,	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=UpperCamelCase__		,	token_ids_a=UpperCamelCase__		,	already_has_special_tokens=UpperCamelCase__							)

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




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[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       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		return len(self.sp_model							) + self.fairseq_offset




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







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




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		UpperCAmelCase_       				=			self.sp_model.PieceToId(UpperCamelCase__							)

		# 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       __A					(		self	:						int		,	lowerCAmelCase	:						List[str]							):


		'''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       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"".join(UpperCamelCase__							).replace(UpperCamelCase__		,	" "							).strip()
		return out_string




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







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

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

		return (out_vocab_file,)




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







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


def       __lowerCAmelCase      (						A    ):
	if len(A    ) <= 1:
		return lst

	UpperCAmelCase_       				=			1

	while i < len(A    ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				UpperCAmelCase_       				=			1

	return lst


if __name__ == "__main__":
				_a:    List[Any] 							=			input("""Enter numbers separated by a comma:\n""").strip()
				_a:    Optional[int] 							=			[int(item) for item in user_input.split(""",""")]
				print(gnome_sort(unsorted))