


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

import sentencepiece as spm

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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						"""▁"""

lowerCAmelCase							=						{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase							=						{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase							=						{
    """facebook/mbart-large-50-one-to-many-mmt""": 1_024,
}

# fmt: off
lowerCAmelCase							=						["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		Tuple        =	VOCAB_FILES_NAMES
	_lowerCAmelCase    :		int        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase    :		List[str]        =	PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase    :		Optional[Any]        =	['''input_ids''', '''attention_mask''']

	_lowerCAmelCase    :		List[int]        =	[]
	_lowerCAmelCase    :		List[int]        =	[]


	def __init__(						self							,    lowercase__							,    lowercase__=None							,    lowercase__=None							,    lowercase__="</s>"							,    lowercase__="</s>"							,    lowercase__="<s>"							,    lowercase__="<unk>"							,    lowercase__="<pad>"							,    lowercase__="<mask>"							,    lowercase__ = None							,    **lowercase__							,    ):
							# Mask token behave like a normal word, i.e. include the space before it
							__UpperCAmelCase :       Optional[Any]  						=  AddedToken(lowercase__							,    lstrip=lowercase__							,    rstrip=lowercase__) if isinstance(lowercase__							,    lowercase__) else mask_token

							__UpperCAmelCase :       int  						=  {} if sp_model_kwargs is None else sp_model_kwargs

							__UpperCAmelCase :       Optional[Any]  						=  kwargs.get('''additional_special_tokens'''							,    [])
							kwargs["additional_special_tokens"] += [
							    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=lowercase__							,    tgt_lang=lowercase__							,    eos_token=lowercase__							,    unk_token=lowercase__							,    sep_token=lowercase__							,    cls_token=lowercase__							,    pad_token=lowercase__							,    mask_token=lowercase__							,    sp_model_kwargs=self.sp_model_kwargs							,    **lowercase__							,    )

							__UpperCAmelCase :       Any  						=  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'

							# Mimic fairseq token-to-id alignment for the first 4 token
							__UpperCAmelCase :       Any  						=  {'''<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 :       Optional[int]  						=  1

							__UpperCAmelCase :       Optional[Any]  						=  len(self.sp_model)
							__UpperCAmelCase :       Union[str, Any]  						=  {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__)
							}
							__UpperCAmelCase :       Any  						=  {v: k for k, v in self.lang_code_to_id.items()}
							__UpperCAmelCase :       int  						=  len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
							__UpperCAmelCase :       List[Any]  						=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

							__UpperCAmelCase :       Optional[Any]  						=  src_lang if src_lang is not None else '''en_XX'''
							__UpperCAmelCase :       Dict  						=  self.lang_code_to_id[self._src_lang]
							__UpperCAmelCase :       int  						=  tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)


	@property
	def    A(						self):
							return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token


	@property
	def    A(						self):
							return self._src_lang


	@src_lang.setter
	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       int  						=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def __getstate__(						self):
							__UpperCAmelCase :       List[Any]  						=  self.__dict__.copy()
							__UpperCAmelCase :       Optional[Any]  						=  None
							return state


	def __setstate__(						self							,    lowercase__):
							__UpperCAmelCase :       Tuple  						=  d

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

							__UpperCAmelCase :       Any  						=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(self.vocab_file)


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  {self.convert_ids_to_tokens(lowercase__): i for i in range(self.vocab_size)}
							vocab.update(self.added_tokens_encoder)
							return vocab


	def    A(						self							,    lowercase__):
							return self.sp_model.encode(lowercase__							,    out_type=lowercase__)


	def    A(						self							,    lowercase__):
							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    A(						self							,    lowercase__):
							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							,    lowercase__):
							__UpperCAmelCase :       List[str]  						=  []
							__UpperCAmelCase :       List[str]  						=  ''''''
							__UpperCAmelCase :       Union[str, Any]  						=  False
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			if not prev_is_special:
																									out_string += " "
																			out_string += self.sp_model.decode(lowercase__) + token
																			__UpperCAmelCase :       Tuple  						=  True
																			__UpperCAmelCase :       List[str]  						=  []
													else:
																			current_sub_tokens.append(lowercase__)
																			__UpperCAmelCase :       str  						=  False
							out_string += self.sp_model.decode(lowercase__)
							return out_string.strip()


	def    A(						self							,    lowercase__							,    lowercase__ = None):
							if not os.path.isdir(lowercase__):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory")
													return
							__UpperCAmelCase :       str  						=  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 :       Union[str, Any]  						=  self.sp_model.serialized_model_proto()
																			fi.write(lowercase__)

							return (out_vocab_file,)


	def    A(						self							,    lowercase__							,    lowercase__ = None							,    lowercase__ = False):

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

							__UpperCAmelCase :       int  						=  [1] * len(self.prefix_tokens)
							__UpperCAmelCase :       List[str]  						=  [1] * len(self.suffix_tokens)
							if token_ids_a is None:
													return prefix_ones + ([0] * len(lowercase__)) + suffix_ones
							return prefix_ones + ([0] * len(lowercase__)) + ([0] * len(lowercase__)) + suffix_ones


	def    A(						self							,    lowercase__							,    lowercase__ = None):
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    **lowercase__):
							if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
							__UpperCAmelCase :       str  						=  src_lang
							__UpperCAmelCase :       List[Any]  						=  self(lowercase__							,    add_special_tokens=lowercase__							,    return_tensors=lowercase__							,    **lowercase__)
							__UpperCAmelCase :       Any  						=  self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase :       Optional[Any]  						=  tgt_lang_id
							return inputs


	def    A(						self							,    lowercase__							,    lowercase__ = "en_XX"							,    lowercase__ = None							,    lowercase__ = "ro_RO"							,    **lowercase__							,    ):
							__UpperCAmelCase :       Optional[Any]  						=  src_lang
							__UpperCAmelCase :       int  						=  tgt_lang
							return super().prepare_seqaseq_batch(lowercase__							,    lowercase__							,    **lowercase__)


	def    A(						self):
							return self.set_src_lang_special_tokens(self.src_lang)


	def    A(						self):
							return self.set_tgt_lang_special_tokens(self.tgt_lang)


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       List[Any]  						=  self.lang_code_to_id[src_lang]
							__UpperCAmelCase :       Optional[Any]  						=  [self.cur_lang_code_id]
							__UpperCAmelCase :       List[str]  						=  [self.eos_token_id]


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       Tuple  						=  self.lang_code_to_id[tgt_lang]
							__UpperCAmelCase :       int  						=  [self.cur_lang_code_id]
							__UpperCAmelCase :       Dict  						=  [self.eos_token_id]





def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    list:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[Any]  						=  int(lowercase_			)
						if n_element < 1:
												__UpperCAmelCase :       str  						=  ValueError('''a should be a positive number'''			)
												raise my_error

						__UpperCAmelCase :       Any  						=  [1]
						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  (0, 0, 0)
						__UpperCAmelCase :       int  						=  1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2			,					hamming_list[j] * 3			,					hamming_list[k] * 5			)			)
												index += 1
						return hamming_list


if __name__ == "__main__":
				lowerCAmelCase							=						input("""Enter the last number (nth term) of the Hamming Number Series: """)
				print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
				lowerCAmelCase							=						hamming(int(n))
				print("""-----------------------------------------------------""")
				print(F'The list with nth numbers is: {hamming_numbers}')
				print("""-----------------------------------------------------""")

