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:
      UpperCamelCase__											=					None


UpperCamelCase__											=					logging.get_logger(__name__)


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

UpperCamelCase__											=					{
    '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'
        ),
    },
}

UpperCamelCase__											=					{
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
UpperCamelCase__											=					['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							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : List[str]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Optional[Any]      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : int      =			['input_ids', 'attention_mask']
   __UpperCAmelCase : Dict      =			NllbTokenizer

   __UpperCAmelCase : List[int]      =			[]
   __UpperCAmelCase : List[int]      =			[]


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



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase       ,  lstrip=__UpperCAmelCase       ,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) else mask_token
         UpperCAmelCase__								=  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       ,  )

         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True

         UpperCAmelCase__								=  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}		)
         UpperCAmelCase__								=  {
             lang_code: self.convert_tokens_to_ids(__UpperCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         UpperCAmelCase__								=  src_lang if src_lang is not None else "eng_Latn"
         UpperCAmelCase__								=  self.convert_tokens_to_ids(self._src_lang		)
         UpperCAmelCase__								=  tgt_lang
         self.set_src_lang_special_tokens(self._src_lang		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				str:
         """simple docstring"""



         return self._src_lang


   @src_lang.setter
   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang		)


   def 			lowercase_      (self     :						List[str]       ,  __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 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [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 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str]       ,  __UpperCAmelCase     :						Optional[str]       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				Dict:
         """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"		)
         UpperCAmelCase__								=  src_lang
         UpperCAmelCase__								=  self(__UpperCAmelCase       ,  add_special_tokens=__UpperCAmelCase       ,  return_tensors=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  self.convert_tokens_to_ids(__UpperCAmelCase		)
         UpperCAmelCase__								=  tgt_lang_id
         return inputs


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						str = "eng_Latn"       ,  __UpperCAmelCase     :						Optional[List[str]] = None       ,  __UpperCAmelCase     :						str = "fra_Latn"       ,  **__UpperCAmelCase     :						str       ,  )						->				BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  src_lang
         UpperCAmelCase__								=  tgt_lang
         return super().prepare_seqaseq_batch(__UpperCAmelCase       ,  __UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				List[str]:
         """simple docstring"""



         return self.set_src_lang_special_tokens(self.src_lang		)


   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         return self.set_tgt_lang_special_tokens(self.tgt_lang		)


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						List[str]		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  self.convert_tokens_to_ids(__UpperCAmelCase		)

         if self.legacy_behaviour:
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  [self.eos_token_id, self.cur_lang_code]
         else:
               UpperCAmelCase__								=  [self.cur_lang_code]
               UpperCAmelCase__								=  [self.eos_token_id]

         UpperCAmelCase__								=  self.convert_ids_to_tokens(self.prefix_tokens		)
         UpperCAmelCase__								=  self.convert_ids_to_tokens(self.suffix_tokens		)

         UpperCAmelCase__								=  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 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  self.convert_tokens_to_ids(__UpperCAmelCase		)
         if self.legacy_behaviour:
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  [self.eos_token_id, self.cur_lang_code]
         else:
               UpperCAmelCase__								=  [self.cur_lang_code]
               UpperCAmelCase__								=  [self.eos_token_id]

         UpperCAmelCase__								=  self.convert_ids_to_tokens(self.prefix_tokens		)
         UpperCAmelCase__								=  self.convert_ids_to_tokens(self.suffix_tokens		)

         UpperCAmelCase__								=  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 			lowercase_      (self     :						Optional[Any]       ,  __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
         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		):
               copyfile(self.vocab_file       ,  __UpperCAmelCase		)

         return (out_vocab_file,)




import math
import random




def    lowerCAmelCase_ (     __A,  __A = False							)     ->				float:







      '''simple docstring'''


      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
UpperCamelCase__											=					0.0_2




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      UpperCAmelCase__								=  float(2 * (random.randint(1,  100							)) - 1							)

      for _ in range(__A							):
            # Forward propagation
            UpperCAmelCase__								=  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            UpperCAmelCase__								=  (expected / 100) - layer_a
            # Error delta
            UpperCAmelCase__								=  layer_1_error * sigmoid_function(__A,  __A							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					int(input('Expected value: '))
      UpperCamelCase__											=					int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))



