





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_nllb import NllbTokenizer
else:
						_lowerCamelCase :    Union[str, Any]  							= None


_lowerCamelCase :    str  							= logging.get_logger(__name__)


_lowerCamelCase :    Dict  							= {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase :    List[Any]  							= {
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCamelCase :    Optional[int]  							= {
    '''facebook/nllb-large-en-ro''': 1_0_2_4,
    '''facebook/nllb-200-distilled-600M''': 1_0_2_4,
}

# fmt: off
_lowerCamelCase :    Optional[Any]  							= ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]
							UpperCAmelCase_      						=   NllbTokenizer

							UpperCAmelCase_      						=   []
							UpperCAmelCase_      						=   []






							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					Optional[int]=None,			_UpperCAmelCase     :					str=None,			_UpperCAmelCase     :					Optional[Any]="<s>",			_UpperCAmelCase     :					Dict="</s>",			_UpperCAmelCase     :					Optional[int]="</s>",			_UpperCAmelCase     :					str="<s>",			_UpperCAmelCase     :					Any="<unk>",			_UpperCAmelCase     :					Dict="<pad>",			_UpperCAmelCase     :					Optional[int]="<mask>",			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					Dict=None,			_UpperCAmelCase     :					List[str]=None,			_UpperCAmelCase     :					Any=False,			**_UpperCAmelCase     :					List[Any],			)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						AddedToken(_UpperCAmelCase,			lstrip=_UpperCAmelCase,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) else mask_token
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						legacy_behaviour
														super().__init__(
														    vocab_file=_UpperCAmelCase,			tokenizer_file=_UpperCAmelCase,			bos_token=_UpperCAmelCase,			eos_token=_UpperCAmelCase,			sep_token=_UpperCAmelCase,			cls_token=_UpperCAmelCase,			unk_token=_UpperCAmelCase,			pad_token=_UpperCAmelCase,			mask_token=_UpperCAmelCase,			src_lang=_UpperCAmelCase,			tgt_lang=_UpperCAmelCase,			additional_special_tokens=_UpperCAmelCase,			legacy_behaviour=_UpperCAmelCase,			**_UpperCAmelCase,			)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						vocab_file
														SCREAMING_SNAKE_CASE__       :      Tuple     =						False if not self.vocab_file else True

														SCREAMING_SNAKE_CASE__       :      int     =						FAIRSEQ_LANGUAGE_CODES.copy()

														if additional_special_tokens is not None:
																					# Only add those special tokens if they are not already there.
																					_additional_special_tokens.extend(
																					    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

														self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{
														    lang_code: self.convert_tokens_to_ids(_UpperCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
														}

														SCREAMING_SNAKE_CASE__       :      List[str]     =						src_lang if src_lang is not None else "eng_Latn"
														SCREAMING_SNAKE_CASE__       :      int     =						self.convert_tokens_to_ids(self._src_lang						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tgt_lang
														self.set_src_lang_special_tokens(self._src_lang						)






							@property
							def  A_     ( self     :					str						)				->			str:





														"""simple docstring"""
														return self._src_lang






							@src_lang.setter
							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					str						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						new_src_lang
														self.set_src_lang_special_tokens(self._src_lang						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														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     :					List[str],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      Any     =						[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]






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str],			_UpperCAmelCase     :					Optional[str],			**_UpperCAmelCase     :					List[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														if src_lang is None or tgt_lang is None:
																					raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
														SCREAMING_SNAKE_CASE__       :      Any     =						src_lang
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			return_tensors=_UpperCAmelCase,			**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						self.convert_tokens_to_ids(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tgt_lang_id
														return inputs






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					str = "eng_Latn",			_UpperCAmelCase     :					Optional[List[str]] = None,			_UpperCAmelCase     :					str = "fra_Latn",			**_UpperCAmelCase     :					Any,			)				->			BatchEncoding:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						src_lang
														SCREAMING_SNAKE_CASE__       :      str     =						tgt_lang
														return super().prepare_seqaseq_batch(_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			List[Any]:





														"""simple docstring"""
														return self.set_src_lang_special_tokens(self.src_lang						)






							def  A_     ( self     :					List[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														return self.set_tgt_lang_special_tokens(self.tgt_lang						)






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Dict						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.convert_tokens_to_ids(_UpperCAmelCase						)

														if self.legacy_behaviour:
																					SCREAMING_SNAKE_CASE__       :      Any     =						[]
																					SCREAMING_SNAKE_CASE__       :      int     =						[self.eos_token_id, self.cur_lang_code]
														else:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.cur_lang_code]
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[self.eos_token_id]

														SCREAMING_SNAKE_CASE__       :      str     =						self.convert_ids_to_tokens(self.prefix_tokens						)
														SCREAMING_SNAKE_CASE__       :      int     =						self.convert_ids_to_tokens(self.suffix_tokens						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						processors.TemplateProcessing(
														    single=prefix_tokens_str + ["$A"] + suffix_tokens_str,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,			self.prefix_tokens + self.suffix_tokens						)						),			)






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					str						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.convert_tokens_to_ids(_UpperCAmelCase						)
														if self.legacy_behaviour:
																					SCREAMING_SNAKE_CASE__       :      str     =						[]
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.eos_token_id, self.cur_lang_code]
														else:
																					SCREAMING_SNAKE_CASE__       :      int     =						[self.cur_lang_code]
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[self.eos_token_id]

														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.convert_ids_to_tokens(self.prefix_tokens						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.convert_ids_to_tokens(self.suffix_tokens						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						processors.TemplateProcessing(
														    single=prefix_tokens_str + ["$A"] + suffix_tokens_str,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,			self.prefix_tokens + self.suffix_tokens						)						),			)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""simple docstring"""
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																					    "tokenizer."						)

														if not os.path.isdir(_UpperCAmelCase						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      Any     =						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						):
																					copyfile(self.vocab_file,			_UpperCAmelCase						)

														return (out_vocab_file,)













import qiskit




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    qiskit.result.counts.Counts:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						qiskit.Aer.get_backend("aer_simulator"       )

							# Create a Quantum Circuit acting on the q register
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							# Apply X (NOT) Gate to Qubits 0 & 1
							circuit.x(0       )
							circuit.x(1       )

							# Map the quantum measurement to the classical bits
							circuit.measure([0, 1]			,				[0, 1]       )

							# Execute the circuit on the qasm simulator
							SCREAMING_SNAKE_CASE__       :      int     =						qiskit.execute(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				shots=10_00       )

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		_lowerCamelCase :    List[str]  							= single_qubit_measure(2, 2)
		print(f"Total count for various states are: {counts}")






