





"""simple docstring"""
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": 1_0_2_4,
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# 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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_   :      int							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_   :      Dict							=  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  ['''input_ids''', '''attention_mask''']
       SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  NllbTokenizer

       SCREAMING_SNAKE_CASE_   :      List[int]							=  []
       SCREAMING_SNAKE_CASE_   :      List[int]							=  []




       def __init__(						self		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="<s>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="<s>"		,SCREAMING_SNAKE_CASE__="<unk>"		,SCREAMING_SNAKE_CASE__="<pad>"		,SCREAMING_SNAKE_CASE__="<mask>"		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :List[str]						=  vocab_file
             __SCREAMING_SNAKE_CASE   :int						=  False if not self.vocab_file else True

             __SCREAMING_SNAKE_CASE   :Tuple						=  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   :Union[str, Any]						=  {
                 lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							) for lang_code in FAIRSEQ_LANGUAGE_CODES
             }

             __SCREAMING_SNAKE_CASE   :Any						=  src_lang if src_lang is not None else '''eng_Latn'''
             __SCREAMING_SNAKE_CASE   :Any						=  self.convert_tokens_to_ids(self._src_lang							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tgt_lang
             self.set_src_lang_special_tokens(self._src_lang							)




       @property
       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             return self._src_lang




       @src_lang.setter
       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  new_src_lang
             self.set_src_lang_special_tokens(self._src_lang							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  [self.sep_token_id]
             __SCREAMING_SNAKE_CASE   :Dict						=  [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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)			->      Optional[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   :List[str]						=  src_lang
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self(SCREAMING_SNAKE_CASE__		,add_special_tokens=SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tgt_lang_id
             return inputs




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = "eng_Latn"		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = "fra_Latn"		,**SCREAMING_SNAKE_CASE__		,)			->      BatchEncoding:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  src_lang
             __SCREAMING_SNAKE_CASE   :str						=  tgt_lang
             return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             return self.set_src_lang_special_tokens(self.src_lang							)




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             return self.set_tgt_lang_special_tokens(self.tgt_lang							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE   :Any						=  self.convert_ids_to_tokens(self.prefix_tokens							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.convert_ids_to_tokens(self.suffix_tokens							)

             __SCREAMING_SNAKE_CASE   :Dict						=  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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
             if self.legacy_behaviour:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  []
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  [self.eos_token_id, self.cur_lang_code]
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  [self.cur_lang_code]
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [self.eos_token_id]

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.convert_ids_to_tokens(self.prefix_tokens							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.convert_ids_to_tokens(self.suffix_tokens							)

             __SCREAMING_SNAKE_CASE   :str						=  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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 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(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
                   return
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  os.path.join(
                 SCREAMING_SNAKE_CASE__		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(SCREAMING_SNAKE_CASE__							):
                   copyfile(self.vocab_file		,SCREAMING_SNAKE_CASE__							)

             return (out_vocab_file,)






"""simple docstring"""
import qiskit



def      __lowerCamelCase   (							a_     :		int ,   a_     :		int							)			->      qiskit.result.counts.Counts:
      __SCREAMING_SNAKE_CASE   :Tuple						=  qiskit.Aer.get_backend('''aer_simulator'''							)

      # Create a Quantum Circuit acting on the q register
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  qiskit.QuantumCircuit(a_ ,   a_							)

      # 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   :Tuple						=  qiskit.execute(a_ ,   a_ ,   shots=10_00							)

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


if __name__ == "__main__":
  lowerCamelCase_								=		single_qubit_measure(2, 2)
  print(f'Total count for various states are: {counts}')