




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       :	str									=     None


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)


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

lowerCamelCase       :	Optional[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       :	Union[str, Any]									=     {
    "facebook/nllb-large-en-ro": 1_024,
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
lowerCamelCase       :	List[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 						A(		UpperCamelCase     ):



 '''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]		,							A_      :						str=None		,							A_      :						Any=None		,							A_      :						List[str]="<s>"		,							A_      :						List[Any]="</s>"		,							A_      :						List[str]="</s>"		,							A_      :						Optional[int]="<s>"		,							A_      :						Dict="<unk>"		,							A_      :						Dict="<pad>"		,							A_      :						Any="<mask>"		,							A_      :						List[str]=None		,							A_      :						List[Any]=None		,							A_      :						List[Any]=None		,							A_      :						str=False		,							**A_      :						int		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else mask_token
       lowerCamelCase_	  =					legacy_behaviour
       super().__init__(
           vocab_file=A_		,							tokenizer_file=A_		,							bos_token=A_		,							eos_token=A_		,							sep_token=A_		,							cls_token=A_		,							unk_token=A_		,							pad_token=A_		,							mask_token=A_		,							src_lang=A_		,							tgt_lang=A_		,							additional_special_tokens=A_		,							legacy_behaviour=A_		,							**A_		,							)

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

       lowerCamelCase_	  =					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}						)
       lowerCamelCase_	  =					{
           lang_code: self.convert_tokens_to_ids(A_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

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

 @property
 def        a__				(       self      :						Optional[Any]						)     ->			str:

       """simple docstring"""






       return self._src_lang

 @src_lang.setter
 def        a__				(       self      :						Dict		,							A_      :						str						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)

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

       """simple docstring"""






       lowerCamelCase_	  =					[self.sep_token_id]
       lowerCamelCase_	  =					[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      :						str		,							A_      :						int		,							A_      :						str		,							A_      :						Optional[str]		,							A_      :						Optional[str]		,							**A_      :						int						)     ->			int:

       """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'						)
       lowerCamelCase_	  =					src_lang
       lowerCamelCase_	  =					self(A_		,							add_special_tokens=A_		,							return_tensors=A_		,							**A_						)
       lowerCamelCase_	  =					self.convert_tokens_to_ids(A_						)
       lowerCamelCase_	  =					tgt_lang_id
       return inputs

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

       """simple docstring"""






       lowerCamelCase_	  =					src_lang
       lowerCamelCase_	  =					tgt_lang
       return super().prepare_seqaseq_batch(A_		,							A_		,							**A_						)

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

       """simple docstring"""






       return self.set_src_lang_special_tokens(self.src_lang						)

 def        a__				(       self      :						str						)     ->			str:

       """simple docstring"""






       return self.set_tgt_lang_special_tokens(self.tgt_lang						)

 def        a__				(       self      :						Optional[int]		,							A_      :						Dict						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					self.convert_tokens_to_ids(A_						)

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

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

       lowerCamelCase_	  =					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      :						str		,							A_      :						str						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					self.convert_tokens_to_ids(A_						)
       if self.legacy_behaviour:
             lowerCamelCase_	  =					[]
             lowerCamelCase_	  =					[self.eos_token_id, self.cur_lang_code]
       else:
             lowerCamelCase_	  =					[self.cur_lang_code]
             lowerCamelCase_	  =					[self.eos_token_id]

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

       lowerCamelCase_	  =					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]		,							A_      :						str		,							A_      :						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(A_						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
             return
       lowerCamelCase_	  =					os.path.join(
           A_		,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

       return (out_vocab_file,)









from manim import *







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					Rectangle(height=0.5		,							width=0.5						)
       lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0						)

       lowerCamelCase_	  =					Rectangle(height=0.25		,							width=0.25						)

       lowerCamelCase_	  =					[mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					[mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(A_		,							A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('CPU'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       cpu.move_to([-2.5, -0.5, 0]						)
       self.add(A_						)

       lowerCamelCase_	  =					[mem.copy() for i in range(4						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('GPU'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       gpu.move_to([-1, -1, 0]						)
       self.add(A_						)

       lowerCamelCase_	  =					[mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)

       lowerCamelCase_	  =					Text('Model'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       model.move_to([3, -1.0, 0]						)
       self.add(A_						)

       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					[]

       for i, rect in enumerate(A_						):
             lowerCamelCase_	  =					fill.copy().set_fill(A_		,							opacity=0.8						)
             target.move_to(A_						)
             model_arr.append(A_						)

             lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0.0						).set_fill(A_		,							opacity=0.8						)
             cpu_target.move_to(cpu_left_col_base[i]						)
             model_cpu_arr.append(A_						)

       self.add(*A_		,							*A_						)

       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(A_		,							A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('Disk'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       disk.move_to([-4, -1.25, 0]						)
       self.add(A_		,							A_						)

       lowerCamelCase_	  =					Square(side_length=2.2						)
       key.move_to([-5, 2, 0]						)

       lowerCamelCase_	  =					MarkupText(
           f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""		,							font_size=18		,							)

       key_text.move_to([-5, 2.4, 0]						)

       self.add(A_		,							A_						)

       lowerCamelCase_	  =					MarkupText(
           f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""		,							font_size=18		,							)

       blue_text.next_to(A_		,							DOWN * 2.4		,							aligned_edge=key_text.get_left()						)
       self.add(A_						)

       lowerCamelCase_	  =					MarkupText(
           f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_						)						)

       lowerCamelCase_	  =					Square(0.3						)
       input.set_fill(A_		,							opacity=1.0						)
       input.set_stroke(width=0.0						)
       input.next_to(model_base[0]		,							A_		,							buff=0.5						)

       self.play(Write(A_						)						)

       input.generate_target()
       input.target.next_to(model_arr[0]		,							direction=A_		,							buff=0.02						)
       self.play(MoveToTarget(A_						)						)

       self.play(FadeOut(A_						)						)

       lowerCamelCase_	  =					Arrow(start=A_		,							end=A_		,							color=A_		,							buff=0.5						)
       a.next_to(model_arr[0].get_left()		,							A_		,							buff=0.2						)

       model_cpu_arr[0].generate_target()
       model_cpu_arr[0].target.move_to(gpu_rect[0]						)

       lowerCamelCase_	  =					MarkupText(
           f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_		,							run_time=3						)						)

       lowerCamelCase_	  =					{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

       self.play(
           Write(A_						)		,							Circumscribe(model_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_cpu_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)
       self.play(MoveToTarget(model_cpu_arr[0]						)						)

       lowerCamelCase_	  =					a.copy()
       for i in range(6						):
             a_c.next_to(model_arr[i].get_right() + 0.02		,							A_		,							buff=0.2						)

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02						)

             lowerCamelCase_	  =					AnimationGroup(
                 FadeOut(A_		,							run_time=0.5						)		,							MoveToTarget(A_		,							run_time=0.5						)		,							FadeIn(A_		,							run_time=0.5						)		,							lag_ratio=0.2						)

             self.play(A_						)

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

             if i < 5:
                   model_cpu_arr[i + 1].generate_target()
                   model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
                   if i >= 1:
                         lowerCamelCase_	  =					0.7

                   self.play(
                       Circumscribe(model_arr[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i + 1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_arr[i + 1]		,							color=A_		,							**A_						)		,							)
                   if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						)		,							MoveToTarget(model_cpu_arr[i + 1]						)		,							)
                   else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]		,							run_time=0.7						)		,							MoveToTarget(model_cpu_arr[i + 1]		,							run_time=0.7						)		,							)
             else:
                   model_cpu_arr[i].generate_target()
                   model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
                   input.generate_target()
                   input.target.next_to(model_arr[-1].get_right()		,							RIGHT + 0.02		,							buff=0.2						)

                   self.play(
                       Circumscribe(model_arr[-1]		,							color=A_		,							**A_						)		,							Circumscribe(cpu_left_col_base[-1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)

                   self.play(MoveToTarget(model_cpu_arr[i]						)						)

             lowerCamelCase_	  =					a_c
             lowerCamelCase_	  =					a_c.copy()

       input.generate_target()
       input.target.next_to(model_base[-1]		,							RIGHT + 0.02		,							buff=0.5						)
       self.play(
           FadeOut(A_						)		,							FadeOut(A_		,							run_time=0.5						)		,							)

       lowerCamelCase_	  =					MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""		,							font_size=24						)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_		,							run_time=3						)		,							MoveToTarget(A_						)						)

       self.wait()



