



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		       =      None


lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase		       =      {
    """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		       =      {
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
lowerCamelCase		       =      ["""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 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Optional[int] =       VOCAB_FILES_NAMES
							A				:str =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A				:Any =       PRETRAINED_VOCAB_FILES_MAP
							A				:Dict =       ["input_ids", "attention_mask"]
							A				:List[Any] =       NllbTokenizer

							A				:List[int] =       []
							A				:List[int] =       []



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







											"""simple docstring"""



											a__	:							Optional[Any]      						= AddedToken(__UpperCAmelCase ,					lstrip=__UpperCAmelCase ,					rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				) else mask_token
											a__	:							str      						= 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 ,					)

											a__	:							int      						= vocab_file
											a__	:							Optional[Any]      						= False if not self.vocab_file else True

											a__	:							Optional[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}				)
											a__	:							List[str]      						= {
											    lang_code: self.convert_tokens_to_ids(__UpperCAmelCase				) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

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



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._src_lang



							@src_lang.setter
							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= new_src_lang
											self.set_src_lang_special_tokens(self._src_lang				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""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 ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= [self.sep_token_id]
											a__	:							Optional[int]      						= [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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""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"				)
											a__	:							Any      						= src_lang
											a__	:							Optional[Any]      						= self(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= self.convert_tokens_to_ids(__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= tgt_lang_id
											return inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "eng_Latn" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = "fra_Latn" ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Tuple      						= src_lang
											a__	:							Optional[Any]      						= tgt_lang
											return super().prepare_seqaseq_batch(__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											return self.set_src_lang_special_tokens(self.src_lang				)



							def 		_A	(							self				):







											"""simple docstring"""



											return self.set_tgt_lang_special_tokens(self.tgt_lang				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.convert_tokens_to_ids(__UpperCAmelCase				)

											if self.legacy_behaviour:
															a__	:							str      						= []
															a__	:							Optional[int]      						= [self.eos_token_id, self.cur_lang_code]
											else:
															a__	:							Tuple      						= [self.cur_lang_code]
															a__	:							Any      						= [self.eos_token_id]

											a__	:							List[Any]      						= self.convert_ids_to_tokens(self.prefix_tokens				)
											a__	:							List[str]      						= self.convert_ids_to_tokens(self.suffix_tokens				)

											a__	:							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 ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.convert_tokens_to_ids(__UpperCAmelCase				)
											if self.legacy_behaviour:
															a__	:							Dict      						= []
															a__	:							List[str]      						= [self.eos_token_id, self.cur_lang_code]
											else:
															a__	:							int      						= [self.cur_lang_code]
															a__	:							Tuple      						= [self.eos_token_id]

											a__	:							List[Any]      						= self.convert_ids_to_tokens(self.prefix_tokens				)
											a__	:							str      						= self.convert_ids_to_tokens(self.suffix_tokens				)

											a__	:							Tuple      						= 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 ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""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
											a__	:							Optional[int]      						= 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


