






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


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		"""▁"""

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

snake_case     					=		{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

snake_case     					=		{
    """facebook/nllb-200-distilled-600M""": 1_024,
}

# fmt: off
snake_case     					=		["""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      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						VOCAB_FILES_NAMES
     UpperCamelCase_				:		int								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_				:		List[str]								=						PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_				:		Dict								=						['''input_ids''', '''attention_mask''']

     UpperCamelCase_				:		List[int]								=						[]
     UpperCamelCase_				:		List[int]								=						[]


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

       SCREAMING_SNAKE_CASE							:   Tuple						    =     {} if sp_model_kwargs is None else sp_model_kwargs
       SCREAMING_SNAKE_CASE							:   str						    =     legacy_behaviour

       super().__init__(
           bos_token=UpperCAmelCase_       ,			eos_token=UpperCAmelCase_       ,			unk_token=UpperCAmelCase_       ,			sep_token=UpperCAmelCase_       ,			cls_token=UpperCAmelCase_       ,			pad_token=UpperCAmelCase_       ,			mask_token=UpperCAmelCase_       ,			tokenizer_file=UpperCAmelCase_       ,			src_lang=UpperCAmelCase_       ,			tgt_lang=UpperCAmelCase_       ,			additional_special_tokens=UpperCAmelCase_       ,			sp_model_kwargs=self.sp_model_kwargs       ,			legacy_behaviour=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   int						    =     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(UpperCAmelCase_   )   )
       SCREAMING_SNAKE_CASE							:   Union[str, 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       SCREAMING_SNAKE_CASE							:   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
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     1

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

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       SCREAMING_SNAKE_CASE							:   List[str]						    =     list(self.lang_code_to_id.keys()   )

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

       SCREAMING_SNAKE_CASE							:   int						    =     src_lang if src_lang is not None else "eng_Latn"
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.lang_code_to_id[self._src_lang]
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang   )


     def __getstate__(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.__dict__.copy()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     None
       SCREAMING_SNAKE_CASE							:   str						    =     self.sp_model.serialized_model_proto()
       return state


     def __setstate__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     d

       # for backward compatibility
       if not hasattr(self       ,			"sp_model_kwargs"   ):
         SCREAMING_SNAKE_CASE							:   Any						    =     {}

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


     @property
     def 					_A							(					self							:						Optional[int]   ):
       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							:						List[Any]   ):
       return self._src_lang


     @src_lang.setter
     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None       ,			UpperCAmelCase_							:						bool = False   ):

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

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


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = 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							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     [self.sep_token_id]
       SCREAMING_SNAKE_CASE							:   str						    =     [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							:						Dict       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str]       ,			UpperCAmelCase_							:						Optional[str]       ,			**UpperCAmelCase_							:						List[str]   ):
       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							:   Optional[Any]						    =     src_lang
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self(UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.convert_tokens_to_ids(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     tgt_lang_id
       return inputs


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						str   ):
       return self.sp_model.encode(UpperCAmelCase_       ,			out_type=UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						Optional[Any]   ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE							:   Any						    =     self.sp_model.PieceToId(UpperCAmelCase_   )

       # 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							:						List[str]       ,			UpperCAmelCase_							:						Optional[Any]   ):
       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							:						Dict       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "".join(UpperCAmelCase_   ).replace(UpperCAmelCase_       ,			" "   ).strip()
       return out_string


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str] = None   ):
       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_   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,			UpperCAmelCase_   )
       elif not os.path.isfile(self.vocab_file   ):
         with open(UpperCAmelCase_       ,			"wb"   ) as fi:
           SCREAMING_SNAKE_CASE							:   Dict						    =     self.sp_model.serialized_model_proto()
           fi.write(UpperCAmelCase_   )

       return (out_vocab_file,)


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						str = "eng_Latn"       ,			UpperCAmelCase_							:						Optional[List[str]] = None       ,			UpperCAmelCase_							:						str = "fra_Latn"       ,			**UpperCAmelCase_							:						str       ,			):
       SCREAMING_SNAKE_CASE							:   str						    =     src_lang
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tgt_lang
       return super().prepare_seqaseq_batch(UpperCAmelCase_       ,			UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]   ):
       return self.set_src_lang_special_tokens(self.src_lang   )


     def 					_A							(					self							:						List[Any]   ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang   )


     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     []
         SCREAMING_SNAKE_CASE							:   str						    =     [self.eos_token_id, self.cur_lang_code]
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     [self.cur_lang_code]
         SCREAMING_SNAKE_CASE							:   Tuple						    =     [self.eos_token_id]






     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						str   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.lang_code_to_id[lang]
       if self.legacy_behaviour:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     []
         SCREAMING_SNAKE_CASE							:   List[str]						    =     [self.eos_token_id, self.cur_lang_code]
       else:
         SCREAMING_SNAKE_CASE							:   str						    =     [self.cur_lang_code]
         SCREAMING_SNAKE_CASE							:   Tuple						    =     [self.eos_token_id]











from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp